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Development and Validation of Scientific Inquiry with Technology

Permanent Link: http://ufdc.ufl.edu/UFE0022581/00001

Material Information

Title: Development and Validation of Scientific Inquiry with Technology Teachers' Perceptions and Practices Scale (SIT-TIPPS)
Physical Description: 1 online resource (169 p.)
Language: english
Creator: Baslanti, Ugur
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: inquiry, instrument, learning, perception, practice, scale, science, scientific, sit, survey, teacher, teaching, technology, tipps
Teaching and Learning -- Dissertations, Academic -- UF
Genre: Curriculum and Instruction (ISC) thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This study was based on the premise technology can enhance the quality of scientific inquiry-based instruction and explored the question of how science teachers use technology to attain the goals of scientific inquiry-based instruction. The instrument developed in this study (SIT-TIPPS) can serve as a useful tool for science teachers and science teacher educators regarding the integration of technology in scientific inquiry-based learning environments. For this purpose, 715 middle and high school science teachers were surveyed for their perceptions about implementing scientific inquiry using technology and the degree to which they use technology for such a goal. Results explored whether relationships exist and the degree of these relationships among a set of variables related to teachers' use of technology for scientific inquiry purposes. Study results supported the validity and reliability of the SIT-TIPPS instrument. It also demonstrated significant relationships among teachers' self-reported perceptions and practices regarding the use of technology to attain the goals of scientific inquiry and the level of preparedness and frequency of using inquiry skills and technology tools in instruction. Analysis of the survey responses also documented science teachers have a variety of skills in using inquiry skills in instruction such as supporting students to explain cause-effect relationships and to discuss scientific explanations/models/ideas with others. However, they report low comfort level and use of inquiry skills such as supporting students to identify their own misconceptions of science content; to find biases and flaws in their scientific explanations; to test scientific explanations against current scientific knowledge; and to critique experiments. Regarding technology tools science teachers report more frequent use of most commonly used technology applications/tools such as word processing, spreadsheets, presentation software, presentation devices, email, and Internet searches. On the other hand, in terms of technology tools/application, the science teachers indicate low comfort level with and the use of new and/or unfamiliar forms of technologies such as blogs and data collection telecollaborative activities. Findings of the study have valuable implications for teacher educators, science teachers, administrators, practitioners, and educational policy makers. The SIT-TIPPS instrument can make valuable contributions to preservice teacher education and inservice teacher trainings. For example, the perceptions and practices factors of the instrument can be used to diagnose the gap between teachers? perceptions and actual classroom practices regarding the use of technology for scientific inquiry purposes. The sections that measure science teachers? comfort levels and the degree to which they integrate inquiry skills and technology tools can help identify what skills teacher candidates or classroom teachers are missing. This, in turn, can help colleges of education and teacher-training institutes design more effective science education programs that nurture teachers' skills and knowledge base for implementing technology and scientific inquiry in their classrooms.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ugur Baslanti.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Swain, Colleen R.
Local: Co-adviser: Dana, Thomas M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022581:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022581/00001

Material Information

Title: Development and Validation of Scientific Inquiry with Technology Teachers' Perceptions and Practices Scale (SIT-TIPPS)
Physical Description: 1 online resource (169 p.)
Language: english
Creator: Baslanti, Ugur
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: inquiry, instrument, learning, perception, practice, scale, science, scientific, sit, survey, teacher, teaching, technology, tipps
Teaching and Learning -- Dissertations, Academic -- UF
Genre: Curriculum and Instruction (ISC) thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This study was based on the premise technology can enhance the quality of scientific inquiry-based instruction and explored the question of how science teachers use technology to attain the goals of scientific inquiry-based instruction. The instrument developed in this study (SIT-TIPPS) can serve as a useful tool for science teachers and science teacher educators regarding the integration of technology in scientific inquiry-based learning environments. For this purpose, 715 middle and high school science teachers were surveyed for their perceptions about implementing scientific inquiry using technology and the degree to which they use technology for such a goal. Results explored whether relationships exist and the degree of these relationships among a set of variables related to teachers' use of technology for scientific inquiry purposes. Study results supported the validity and reliability of the SIT-TIPPS instrument. It also demonstrated significant relationships among teachers' self-reported perceptions and practices regarding the use of technology to attain the goals of scientific inquiry and the level of preparedness and frequency of using inquiry skills and technology tools in instruction. Analysis of the survey responses also documented science teachers have a variety of skills in using inquiry skills in instruction such as supporting students to explain cause-effect relationships and to discuss scientific explanations/models/ideas with others. However, they report low comfort level and use of inquiry skills such as supporting students to identify their own misconceptions of science content; to find biases and flaws in their scientific explanations; to test scientific explanations against current scientific knowledge; and to critique experiments. Regarding technology tools science teachers report more frequent use of most commonly used technology applications/tools such as word processing, spreadsheets, presentation software, presentation devices, email, and Internet searches. On the other hand, in terms of technology tools/application, the science teachers indicate low comfort level with and the use of new and/or unfamiliar forms of technologies such as blogs and data collection telecollaborative activities. Findings of the study have valuable implications for teacher educators, science teachers, administrators, practitioners, and educational policy makers. The SIT-TIPPS instrument can make valuable contributions to preservice teacher education and inservice teacher trainings. For example, the perceptions and practices factors of the instrument can be used to diagnose the gap between teachers? perceptions and actual classroom practices regarding the use of technology for scientific inquiry purposes. The sections that measure science teachers? comfort levels and the degree to which they integrate inquiry skills and technology tools can help identify what skills teacher candidates or classroom teachers are missing. This, in turn, can help colleges of education and teacher-training institutes design more effective science education programs that nurture teachers' skills and knowledge base for implementing technology and scientific inquiry in their classrooms.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ugur Baslanti.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Swain, Colleen R.
Local: Co-adviser: Dana, Thomas M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022581:00001


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DEVELOPMENT AND VALIDATION OF SCIENTIFIC INQUIRY WITH TECHNOLOGY:
TEACHERS' PERCEPTIONS AND PRACTICES SCALE (SIT-TIPPS)





















By

UGUR BASLANTI


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008



































2008 Ugur Baslanti




























To my wife Tezcan and my daughter Berra









ACKNOWLEDGMENTS

I would like to express my gratitude to all the people who had supported me during this

dissertation process. First of all, I would like to thank my wife, Tezcan; and my family members

and friends for their unwavering support and for being a constant source of encouragement

during this journey. Their presence and love led me this far.

I thank my committee members for their support and guidance. Dr. Colleen Swain and Dr.

Thomas Dana continually supported me with their expertise and directed my interest toward my

topic and provided guidance and revision suggestions. I am grateful for their professional and

personal insights and consistent encouragement. I thank Dr. Kara Dawson, Dr. David Miller, and

Dr. Rose Pringle for their invaluable input and feedback on my work. I also wish to express my

gratitude to Dr. Troy Sadler, Dr. Dina Mayne, and Dr. Karen Irving for their contribution to my

dissertation study with their expertise during the content validation process.

This research would not have come to fruition without the economic support of my

department, the School of Teaching and Learning (STL) in the College of Education. I very

much appreciate the professional and financial support I have received from my department

during my doctoral study.

Of course, this research would not have been possible without the cooperation of the

science teachers from all corners of the United States of America. Special thanks go to over forty

national science teacher associations who distributed my invitation to their members.

Finally, many people contributed to my educational background. It is impossible to name

each of them individually, but I would like to express my gratitude and love to these people who

helped me shape the educator and researcher that I have become.










TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ..............................................................................................................4

LIST OF TABLES ...................... ......... ................................ ........8

LIST OF ABBREVIATIONS ................ ....... ...................... 10

ABSTRACT .............................. ................................. ............... 1

CHAPTER

1 IN TRODU CTION ................... ......................................... .............. .............. 13

Statement of the Problem ............... ................ .. .......... .. ............ .... ....14
Purpose of the Study ................... .................................................. .. ............. 15
Significance of the Study ............... ...................... ...... ......... ........... ................. 16
Research Questions............................ ....... ...............17
Overarching Questions .................. ...................................................................... ........ 17
Supporting Questions ................................................ ........ 18
Theoretical Framework of the Study ............................................................ ............18
Definition of Terms ................. ............................... 21
D elim stations of the Study ..............................................................................22
Limitations of the Study ........................... ........................ 22
Organization of Chapters ................... ............................. ....... .. ............ ........ 23

2 REVIEW OF THE LITERATURE .............................................. ...............24

Inquiry: A History and Evolving Definition.............................. ................... 24
Why Inquiry? ............................................. .........26
Scientific Inquiry and Teachers ..............................................................................27
Technology and Science Education.................................... ............... 32
T technology and L earning ................................................................................. 36
M models of Technology U se .................................................. ............... 37
Technology in Science Instruction ................. ................................38
T technology and Inquiry ......................... ... ... ...... .........................39
Multiple Examples of Technology Used to Reach Goals of Science Inquiry ..............41
Sum m ary .............. ................... ................................................................ 45

3 M ETHODOLOGY ........................................ ......................... 46

Introduction............... ................... ............................. ......... 46
Research Questions........................ ............. .......... 46
Overarching Questions .............................................. ..... ...47
Supporting Questions .......................................... ...... ..............47
Technology in the SIT-TIPPS ............................................................47










Data Collection ...................... ............. ............ ........48
Instrumentation and Instrument Development Procedures.................................................50
C ontent V alidation P process: ...................................................................................54
Statistical Techniques Used to Answer Study Research Questions .....................................57
Item A analysis and R liability .............................................................59
Factor Analyses ...................................................... ........59
Sample Size ........................................................ 59
Exploratory Factor A nalysis.................................................... 60
M multiple Regression.......................... ........ .............. 64
Sum m ary .............. ................... ................................................................ 66

4 PRESENTATION AND ANALYSIS OF DATA...............................................................84

Stu dy R research Q u estion s ................................................................................................. 84
Overarching Questions .................. ...................................................................... ........ 84
Supporting Questions .......................................... ...... ..............84
D em graphic Reporting of the Sam ple ......................................................... .... ...........85
Demographic Characteristics...................... ........... ......... 85
Teaching Experience .................. ............................ 85
Computer Access and Knowledge.............................................................. ... ....... 87
Computer Access ..................... .................. .87
Source of Com puter Knowledge ............................................. ............... 88
A nsw ering R research Question 1................................................. ............... 89
Answering Research Question 2........................................91........ .........91
Level of Use of Inquiry Skills and Technology Tools ..................................................91
Correlational Analysis ..................................................... 92
Multiple Regression.......................... ....... ..............96
Further Analyses............................... ...... ............... ........98
A Summary of Results in terms of Study Research Questions ............................................103

5 FINDINGS, CONCLUSIONS, IMPLICATIONS AND SUGGESTIONS FOR
FU TU R E R E SEA R CH ...................................................17..........

Overarching Questions ........... ..... .... .........................118
Supporting Questions.......... ...................................................118
The SIT-TIPPS Instrument........................ .......... ..... ............... 119
Summary of Findings ................. .............. .................... 120
Characteristics of Science Teachers .......................... ......................121
A Snapshot of Science Teachers' Scientific Inquiry and Technology Skills..............122
Source of Computer Knowledge ................. ..............................123
A additional D em graphic Findings ........................................ ................. 124
Study Implications ...................................... .............. ........ ..... 125
Possibilities for Future Research .................................. .......................... ........ 131
Conclusion......................................................................... .........132






6









APPENDIX

A RESEARCH STUDY INFORMED CONSENT FORM ...................................... 135

B SU R V E Y IN STR U M EN T .............................................................................................. 140

C LITERATURE BASE USED TO DEVELOP THE ITEM POOL ......................................149

L IST O F R E F E R E N C E S ....................................................................................................... 158

BIOGRAPHICAL SKETCH ................................................................... ........... 169












































7









LIST OF TABLES

Table page

3-1 Descriptive statistics and reliability index of the SIT-TIPPS instrument..........................67

3-2 Item analysis results ................... .......... ..... .................... ...... .. .............. 68

3-3 Parallel analysis: PAF/common factor analysis & random normal data generation
(N=557, Nvariables=50) ................................................... 69

3-4 Breakdown of 50 items into categories and related factors..................... .................69

3-5 K M O and B artlett's T est ..............................................................70

3-6 Communalities ...................................... ................................... ........ 71

3-7 Total variance explained: PAF with promax rotation.......................... ...........72

3-8 Pattern m atrix ..................................................................................... 73

3-9 Structure matrix ...................... ................................ 75

3-10 Factor correlation m atrix..................................................76

3-11 Communalities ................ ............. .................................. ......... 77

3-12 Total variance explained ........................................................................................ ... ......... 78

3-13 Pattern m atrix ................... ................. .................. .......... .............. 79

3-14 Structure matrix ...................... ................................ 8 1

3-15 Factor correlation m atrix..................................................82

3-16 Regression analysis summary for teachers' perceptions factor...................................82

3-17 Regression analysis summary for teachers' practices factor ...........................................82

3-18 Regression analysis summary for frequency of using inquiry skills .............. ...............83

3-19 Regression analysis summary for frequency of using technology tools ............................83

4-1 Participant characteristics based on gender (n=598) ................. ................. ...107

4-2 Participant characteristics based on race/ethnicity (n=590)................. ...................107

4-3 Distribution of participants based on states (n=601) .......................................................108









4-4 Number of years of teaching experience: Categorized (n=583)......................................109

4-5 Grade levels taught by science teachers (n=715)......................................................109

4-6 Courses taught by science teachers (n=715)................................................109

4-7 Science teachers' frequency of use of scientific inquiry skills..............................110

4-8 Science teachers' frequency of use of technology tools..........................111

4-9 Science teachers' level of preparedness of scientific inquiry skills............... ...............112

4-11 Pearson Product-Moment Correlation between variables......................... .................114

4-12 The number of computers in classrooms .................................. 115

4-13 Percent of teachers reporting taking educational technology classes .............. ..............115

4-14 T-test results for subscales based on presence of science lab in classroom..................115

4-15 T-test results for subscales based on presence of computer lab in school.................... 116

4-16 ANOVA results between subscales and selected variables............ ... ...........116

4-17 Summary of Post Hoc (Tukey) ANOVA results for significant differences ...................116

C-i Definition and skills table of the essential features of scientific inquiry for the
develop ent of the instrum ent.......................................................................... ............. 150

C-2 Essential features of scientific inquiry for the development of the instrument. ............155









LIST OF ABBREVIATIONS

American Association for the Advancement of Science

National Research Council

Scientific Inquiry with Technology: Teachers' Perceptions and Practices
Survey


AAAS

NRC

SIT-TIPPS









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

DEVELOPMENT AND VALIDATION OF SCIENTIFIC INQUIRY WITH TECHNOLOGY:
TEACHERS' PERCEPTIONS AND PRACTICES SCALE (SIT-TIPPS)

By

Ugur Baslanti

August 2008

Chair: Colleen Swain
Cochair: Thomas Dana
Major: Curriculum and Instruction

This study was based on the premise technology can enhance the quality of scientific

inquiry-based instruction and explored the question of how science teachers use technology to

attain the goals of scientific inquiry-based instruction. The instrument developed in this study

(SIT-TIPPS) can serve as a useful tool for science teachers and science teacher educators

regarding the integration of technology in scientific inquiry-based learning environments. For

this purpose, 715 middle and high school science teachers' were surveyed for their perceptions

about implementing scientific inquiry using technology and the degree to which they use

technology for such a goal. Results explored whether relationships exist and the degree of these

relationships among a set of variables related to teachers' use of technology for scientific inquiry

purposes. Study results supported the validity and reliability of the SIT-TIPPS instrument. It also

demonstrated significant relationships among teachers' self-reported perceptions and practices

regarding the use of technology to attain the goals of scientific inquiry and the level of

preparedness and frequency of using inquiry skills and technology tools in instruction. Analysis

of the survey responses also documented science teachers have a variety of skills in using inquiry

skills in instruction such as supporting students to explain cause-effect relationships and to









discuss scientific explanations/models/ideas with others. However, they report low comfort level

and use of inquiry skills such as supporting students to identify their own misconceptions of

science content; to find biases and flaws in their scientific explanations; to test scientific

explanations against current scientific knowledge; and to critique experiments. Regarding

technology tools science teachers report more frequent use of most commonly used technology

applications/tools such as word processing, spreadsheets, presentation software, presentation

devices, email, and Internet searches. On the other hand, in terms of technology

tools/application, the science teachers indicate low comfort level with and the use of new and/or

unfamiliar forms of technologies such as blogs and data collection telecollaborative activities.

Findings of the study have valuable implications for teacher educators, science teachers,

administrators, practitioners, and educational policy makers. The SIT-TIPPS instrument can

make valuable contributions to preservice teacher education and inservice teacher training. For

example, the perceptions and practices factors of the instrument can be used to diagnose the gap

between teachers' perceptions and actual classroom practices regarding the use of technology for

scientific inquiry purposes. The sections that measure science teachers' comfort levels and the

degree to which they integrate inquiry skills and technology tools can help identify what skills

teacher candidates or classroom teachers are missing. This, in turn, can help colleges of

education and teacher-training institutes design more effective science education programs that

nurture teachers' skills and knowledge base for implementing technology and scientific inquiry

in their classrooms.









CHAPTER 1
INTRODUCTION

There have been many efforts throughout the history of science education to improve

teaching and learning in elementary and secondary schools (Abrams, 1998). The National

Research Council and American Association for the Advancement of Science have contributed

to efforts by publishing reports such as the National Science Education Standards (1996),

Science for All Americans (1990), and Benchmarks for Science Literacy (1993) that emphasized

student learning, the nature of science, science literacy, and scientific inquiry. Despite these

efforts, current science curricula in the United States and many other countries has failed to

prepare students for the kinds of experiences they will need to become successful science

learners (Linn, Davis, & Bell, 2004). Many school science curricula still encourage the

philosophical mindset of the 20th century (Bencze & Hodson, 1999) and teachers, scientists, and

curriculum developers hesitate to give students freedom to investigate their own problems

(Abrams, 1998). The National Research Council (2000, p. 17) reported "teachers were still using

traditional, didactic methods" and "students were mastering disconnected facts in lieu of broader

understandings, critical reasoning, and problem-solving skills."

In addition, the National Commission on Mathematics and Science Teaching for the 21st

Century (2005) reports "children are losing the ability to respond not just to the challenges

already presented by the 21st century but to its potential as well" (p. 4). These reports clearly

show today's students will need to acquire a new set of skills for the 21st century (Kozma &

Schank, 1998). They need to be prepared for a rapidly changing world by: (a) learning how to

think about their knowledge base and to apply it flexibly and responsibly (Wiske, et al., 2005)

and (b) learning how to use a variety of tools to search vast amount of information, generate new









data, analyze and interpret data and transform findings into new meanings, and communicate

ideas (Kozma & Schank, 1998).

In educational technology, one goal of this field is to identify effective ways to use

technology tools for higher-order thinking that mesh with the assumptions of scientific inquiry

(NRC, 1996). Research indicates technology offers opportunities to transform inquiry-based

science teaching and learning (Edelson, Gordin, & Pea, 1999; Alagic, Yeotis, Rimmington, &

Koert, 2003; Linn et al., 2004; Williams, Linn, Ammon, & Gearhart, 2004). Some researchers

point out the need for using technology to promote scientific inquiry in science classrooms

(Pederson & Yerrick, 2000; Carin & Bass, 2001; Williams, et al., 2004). Edelson et al., (1999)

believed all the fundamental properties of computing technologies offer benefits for inquiry-

based learning in the sciences.

Researchers note technology is currently being used in science classes to help build a

community of learners (Bransford, Brown, & Roney, 1999; Dede, 2000); engage students in

problem-rich environments to explore and solve problems (Bransford, et al., 1999); develop

math or science concepts as well as collaborative skills (Stables, 1997); deal with misconceptions

(Cognition and Technology Groups at Vanderbilt, 1992; Nickerson, 1995); master more complex

subjects via rich interactions using external resources (Dede, 2000); and help students generate

and test hypotheses and build explanations of scientific phenomena (Spitulnik, Stratford, Krajcik,

& Soloway, 1998). However, there is scant literature available answering the question of how

science teachers use technology to attain the goals of scientific inquiry.

Statement of the Problem

Scientific inquiry has been an overarching goal of science education (AAAS, 1993; NRC,

1996; Flick, 1997; Crawford, 1997; Edelson, et al., 1999) and a central strategy for teaching

science (NRC, 1996) for decades. Although there are certain instructional methods and strategies









that help teachers implement scientific inquiry in their classrooms, the use of technology can also

play a significant role in meeting the goals of scientific inquiry. According to the National

Research Council (1996), a goal for using educational technology in the classroom is to identify

effective ways to use technology tools for higher-order thinking that mesh with the assumptions

of scientific inquiry. The effective uses of educational technologies also have the potential to

transform inquiry-based science teaching and learning (Edelson et al., 1999; Alagic et al., 2003;

Linn et al., 2004; Williams et al., 2004).

When it comes to technology integration, teachers play a key role (Scheffler & Logan,

1999). Yet, research indicates teachers lack good instructional frameworks for effective

implementation of technology into the curriculum (Bitner & Bitner, 2002). In addition to

teachers' lack of experience in implementing technology into their curricula, there is little

literature answering the question of how science teachers use technology to succeed in enacting

the goals of scientific inquiry. A trio of factors including background knowledge with regard to

science content, inquiry-oriented instruction, and technology (Pedersen & Yerrick, 2000;

Williams et al., 2004) make inquiry teaching more challenging for teachers. Moreover, the

pressure from external forces such as parents, administrators and society to use technology in the

classroom contributes to this issue and makes teachers feel compelled to use technology in their

classrooms without any clear agenda (Wiske, Franz, & Breit, 2005).

Purpose of the Study

This study, which was based on the premise technology could enhance the quality of

scientific inquiry-based instruction, attempted to answer the question of how science teachers use

technology to attain the goals of scientific inquiry-based instruction. The instrument developed

in this study can serve as a useful tool for science teachers and science teacher educators in the

integration of technology in scientific inquiry-based learning environments. For this purpose, the









study investigated science teachers' self-reported perceptions about implementing scientific

inquiry using technology and explored the degree to which they use technology for such a goal.

It also examined whether relationships exist, and the degree of these relationships, among a set

of variables related to teachers' use of technology for inquiry purposes.

Significance of the Study

This dissertation study focused on how technology is used when supporting scientific

inquiry instruction in K-12 science classrooms through the development of an instrument about

scientific inquiry and the use of technology. This study attempted to address an essential topic

both in science education and educational technology. Even though there are some instruments

targeting scientific inquiry in science education literature (Bodzin & Beerer, 2003; Brandon &

Taum, 2005; Smolleck, Zembal-Saul, & Yoder, 2006), an instrument specifically targeting

scientific inquiry in science classrooms where technology is used is an area of need in both

fields. Such an instrument is helpful in analyzing the current practice in schools and colleges of

education and in furthering the discussion on how science teachers can use technology to attain

the goals of scientific inquiry. For this purpose, the researcher developed a quantitative

instrument to measure teachers' self-reported perceptions and practices regarding the use of

technology in attaining the goals of scientific inquiry. It connected theory and research from two

fields, science education and educational technology, which can result in a change of daily

practice in science classroom.

The findings of this study also fused various aspects of scientific inquiry applicable to

educational technology to form a common ground to assess teacher's understandings and uses of

science inquiry utilizing technology. In addition, this instrument has the possibility of being used

as a useful tool to assist science teachers in the integration of technology into scientific inquiry-

based instruction. Although science teacher education programs have begun to design teaching









models that infuse technology, research characterizing teachers' instructional use of educational

technology after completing such programs is limited (McNall, 2004). The instrument developed

in this study and its results also contribute to this body of literature and help educators develop

strategies toward enriching initial teacher preparation and professional development

opportunities. In summation, such an instrument could be helpful by providing insight into the

current practice in schools, examine factors that facilitate or hinder the use of technology for

scientific inquiry purposes, provide instructional strategies to enhance inquiry-based science

teaching that utilizes technology, and build upon the established knowledge base in science

education and educational technology.

Research Questions

This study had two overarching research questions and five supporting questions. The

main questions focused on how teachers use technology to implement the goals of scientific

inquiry and the relationships between their self-reported perceptions and practices regarding this

implementation. The supporting questions, however, articulated these relationships by addressing

some teacher demographics and background/professional development variables as well as the

level and frequency of their preparedness and use of certain inquiry tasks and technology tools.

While the total scores obtained from the survey and the relationships between perception and

practice items in the survey were utilized to help answer the overarching questions of the study,

relationships obtained from supporting questions also contributed to answering these overarching

questions. Through the creation of SIT-TIPPS, the study addressed the following research

questions:

Overarching Questions

1. How are teachers using technology to implement the goals of scientific inquiry in their
classrooms?









2. What are the relationships between teachers' perceptions and practices regarding the use of
technology to attain the goals of scientific inquiry?

Supporting Questions

What are the relationships between teachers' self-reported perceptions and practices regarding
the use of technology to attain the goals of scientific inquiry in terms of:

* Teacher demographics and teacher background/professional development variables?

* How often do they support students to engage in certain inquiry skills in their science
classrooms?

* How often do they use certain technology tools in their science classrooms?

* How prepared do teachers feel to support students to engage in certain inquiry skills in their
science classrooms?

* How prepared do teachers feel to use certain technology tools in their science classrooms?

Theoretical Framework of the Study

Essential to the development of any instrument is a solid theoretical foundation from which

issues and concepts are derived (Devellis, 2003). Because this study is interweaving two

academic areas it becomes necessary to blend two established knowledge bases into a theoretical

foundation on which the instrument created in this study, SIT-TIPPS, can stand.

A meta-analysis research study on inquiry-based science instruction has established that

scientific inquiry-based teaching and learning promotes students' abilities to diagnose problems,

critique experiments, distinguish alternatives, plan investigations, research conjectures, search

for information, debate with peers, seek information from experts, and form coherent arguments

(Linn et al., 2004). However, the studies explored in the meta-analysis lack a common definition

for scientific inquiry-based teaching to guide the successful implementation of inquiry-based

methods in classrooms using technology. By adopting the National Research Council's (NRC)

inquiry standards and meshing these standards with research findings in the areas of scientific

inquiry and educational technology, a theoretical framework is formed to effectively base the









integration of scientific inquiry and technology in science classrooms. The theoretical framework

for this study is built on the inquiry standards developed by the National Research Council in

Inquiry and National Science Education Standards (NSES, 2000) and supported by the related

literature.

The following essential features of classroom inquiry as described in the National Science

Education Standards were used as a guideline to develop the Scientific Inquiry with Technology-

Teachers' Perceptions and Practices Survey (SIT-TIPPS) instrument as part of this study:

1. Learners engage in scientifically oriented questions
2. Learners give priority to evidence in responding to questions.
3. Learners formulate explanations from evidence.
4. Learners connect explanations to scientific knowledge, and
5. Learners communicate and justify explanations (p.25).

The National Science Education Standards (2000) define inquiry-based teaching as

experiences that help students acquire concepts of science, skills and abilities of scientific

inquiry, and understandings about scientific inquiry. The research base used in the Standards to

identify the essential features of inquiry in science classrooms is grounded in research on

learning and the kinds of learning environments that promote learning. For this purpose, the

NRC gives a detailed account of pioneering ideas that laid the groundwork for essential features

of classroom inquiry. These features were used to determine whether scientific inquiry is an

integral part of the classroom. These features are:

* Herbart's (1901) ideas about teaching that include starting with students' interest in the
natural world and in interaction with others.

* Dewey's (1910) expansion of the idea of reflective experience in which students begin with a
perplexing situation, formulate a tentative interpretation or hypothesis, test the hypothesis to
arrive at a solution, and act upon the solution. Dewey emphasized processes and methods of
science and the notion of science as a way of thinking and an attitude of mind.

* Schwab's (1960, 1966) emphasis on science as conceptual structures revised as the result of
new evidence. Schwab suggested teachers: (a) provide laboratory experiences before
introducing students to the formal explanations of scientific concepts and principles, (b)









enable students to build and/or refine explanations from evidence, (c) allow students to pose
questions, discover relationships, and propose scientific explanations based on their own
investigations, (d) enable students to build an understanding of what constitutes scientific
knowledge and how scientific knowledge is produced by providing them with readings and
reports about scientific research in which they can discuss the details of the research or read
about alternative explanations, experiments, and assumptions.

* Piaget's (1975) theory of development in which he proposed learners adapt or change their
cognitive structures when they experience a discrepancy between their existing ideas and
ideas they observe in their environments.

* Atkin's and Karplus' (1962) idea of the learning cycle emphasizing the roles of exploration,
invention, and discovery in the teaching and learning processes.

* Bransford's et al., (1999) research findings on how people learn. These findings suggest: (a)
understanding science is more than knowing facts; students should understand the major
concepts, build a strong base of supporting factual information, and know how to apply their
knowledge effectively, (b) students build new knowledge and understanding on what they
already know and believe, (c) students formulate new knowledge by modifying and refining
their current concepts and by adding new concepts to what they already know, (d) learning is
mediated by the social environment in which learners interact with others, (e) effective
learning requires that students take control of their own learning, (f) the ability to apply
knowledge to novel situations, that is, transfer of learning, is affected by the degree to which
students learn with understanding Bransford's et al., study also suggested that learning
should take place in a learner, knowledge, assessment, and community-centered
environments.

Guided by this theoretical framework about the essential features of classroom inquiry

outlined in the National Science Education Standards, the SIT-TIPPS instrument developed in

this study included items targeting: (a) teachers' use of a variety of technology tools in their

classrooms, (b) their self-reported perceptions regarding the use of technology for scientific

inquiry proposes in instruction, and (c) their practices regarding the use of technology for

scientific inquiry proposes in instruction. As noted earlier, in addition to this framework, the

researcher also made use of the related literature regarding best practices in teaching from both

science education and educational technology fields when constructing the items for the survey.

The essential features of the National Science Education Standards dovetail into the

literature allowing teacher perceptions and practices regarding the use of technology in scientific









inquiry-based instruction were examined. This framework, in conjunction with the two bodies of

knowledge, allowed the researcher to develop the constructs and corresponding items for the

instrument.

Definition of Terms

The following terms can be useful in understanding the nature of this study.

Scientific inquiry refers to the activities of students in which they develop knowledge and

understanding of scientific ideas, as well as an understanding of how scientists study the natural

world (NRC, 1996, p.23).

Technology refers to the use of a range of devices and technological processes specifically for

teaching and learning in K-12 settings. For the purposes of the present study, the following

technologies are included in the definition:

1. Computers (desktop, laptop)

2. Presentation devices (such as video projectors, LCD panels, overhead projectors)

3. Whiteboard/Smartboard

4. Wireless communication devices

5. Computer software (such as Word processors, desktop publishing, spreadsheets,
presentation software, databases, simulations, games, graphing and data analysis
software, video and picture editing software)

6. Graphing/scientific calculators

7. Handhelds, GPS (Global positioning systems)

8. Digital data collection devices (such as pH, pressure and temperature probes, digital
microscope, Navigator systems)

9. Videoconferencing, teleconferencing

10. Internet technologies (such as e-mail, websites, online databases, virtual field trips,
online simulations and games, Wikis, blogs, online learning communities)









Delimitations of the Study

This study focuses on the limited domain of science teachers who teach at the middle

and/or high school level. Therefore, it cannot be generalized to other subject areas and/or other

levels of schools such as elementary schools. The study concentrates on middle and high school

science teachers' self-reported perceptions and practices regarding the use of technology for

scientific inquiry purposes, as well as their comfort levels and uses of inquiry skills and

technology tools/applications in classrooms. The term, technology, is limited to the definition

described above and the concept of scientific inquiry is restricted to National Research Council's

five essential features of scientific inquiry as outlined in the National Science Education

Standards. For this reason, the findings of the study may not be generalized to other features of

scientific inquiry, as well as other forms of technology tools/applications.

Only middle and high school science teachers who teach in the United States of America

constituted the sample. The instrument developed in this study and the findings obtained from its

administration may not be generalized to science teachers who are teaching in other countries.

Limitations of the Study

In this study, the term technology is limited to the definition described above and the

concept of scientific inquiry is restricted to National Research Council's five essential features of

scientific inquiry as outlined in the National Science Education Standards. For this reason, the

findings of the study may not be generalized to other features of scientific inquiry, as well as

other forms of technology tools/applications.

The participation in this study was voluntary and by convenience. There is always the

possibility that the response structure from science teachers who volunteered may differ from

those who did not volunteer or were not contacted.









All of the science teachers, who participated in this study, received an invitation from the

researcher in electronic format via direct email or through membership to professional listservs.

Hence, the sample profile does not involve those teachers who do not use email or have no

access to Internet-based professional organizations. This could limit the generalizability of the

results to many middle and high school science teachers in the U.S.

Organization of Chapters

Chapter 1 provides information on the role of technology and scientific inquiry in science

education. This chapter identifies the significance and purpose of the study, the theoretical

framework and research questions. Chapter 2 reviews the literature on scientific inquiry and the

integration of technology in science education. Highlighted are the importance of the relationship

between the goals of science education and the potential technology can offer to attain these

goals. Chapter 3 provides a detailed description of the study design, instrument development

procedure and methodology used in conducting the research. Chapter 4 interprets and discusses

the data. Chapter 5 summarizes the implications of the findings.









CHAPTER 2
REVIEW OF THE LITERATURE

This review of literature provides a brief but through presentation of salient literature and

research in the following areas: scientific inquiry, scientific inquiry and technology, critical

concepts in the integration of technology in scientific inquiry-based instruction, and guidelines

for instrument development.

Inquiry: A History and Evolving Definition

It is nothing short of a miracle that the modern methods of instruction have not yet entirely
strangled the holy curiosity of inquiry.

Albert Einstein

The miracle Einstein was referring to in the above quote seems to have become a reality at

least in science education. As DeBoer (1991) contended, the goal of science education since the

late 1950s has been inquiry. This term became a central strategy for teaching science (NRC,

1996), and is held in high regard among science educators (Sherman & Sherman, 2004). Its

importance in science education as a central goal has a long and established history dating back

to the works of Dewey at the beginning of the twentieth century and Schwab at the turn of the

century (Zembal-Saul & Land, 2002; Abd-El-Khalick, BouJaoude, Duschl, Hofstein, Lederman,

Mamlok, Niaz, Treagust, & Tuan, 2004; Crawford, 1997; Edelson et al., 1999; National

Research Council, 2000, Bodzin & Beerer, 2003). The role of inquiry in science education

remains a perennial term and continues to be strongly emphasized by current reform reports or

documents in the United States (Crawford, 1997; Flick, 1997; Abd-el-Khalick et al., 2004) such

as the National Science Education Standards (NRC, 1996; 2000) and Benchmarks for Science

Literacy (AAAS, 1993). Recommendations include the opportunity for students to use scientific

inquiry and develop inquiry skills (NRC, 1996) and for teachers to establish inquiry-oriented









learning environments (Carin & Bass, 2001) that result in better retention and understanding of

the concepts (Brendzel, 2005).

Inquiry has been a broadly defined and characterized construct in science education (Looi,

1998; Windschitl, 2003). Although its definition varied among science educators, its presence

(Newman, Abell, Hubbard, McDonald, Otaala, & Martini, 2004) and importance was always

accepted and promoted. Carnes (1997) identified three broad classifications of the definition of

inquiry as: science processes or a scientific method; scientific processes and content knowledge;

and scientific processes, attitudes, and knowledge. Looi (1998) also identified three categories

from a different perspective. His categories of inquiry include: active involvement of learners as

in hands-on, experiential or activity-based learning; a discovery approach as in the development

of process skills associated with scientific methods; and promoting metacognitive knowledge

and skills.

The National Science Education Standards (NRC, 1996) defined scientific inquiry as:

The diverse ways in which scientists study the natural world and propose explanations
based on the evidence derived from their work. Inquiry also refers to the activities of
students in which they develop knowledge and understanding of scientific ideas, as well as
an understanding of how scientists study the natural world... Inquiry is a multifaceted
activity that involves making observations; posing questions; examining books and other
sources of information to see what is already known; planning investigations; reviewing
what is already known in light of experimental evidence; using tools to gather, analyze,
and interpret data; proposing answers, explanations, and predictions; and communicating
the results. (p. 23)

In the same respect, Linn et al., (2004, p. 4) described inquiry instruction as "engaging

students in the intentional process of diagnosing problems, critiquing experiments, distinguishing

alternatives, planning investigations, researching conjectures, searching for information, debating

with peers, seeking information from experts, and forming coherent arguments." Abd-el-Khalick

et al., (2004), on the other hand, distinguished between the terms inquiry as means (or inquiry in

science) and inquiry as ends (or inquiry about science). In their descriptions of scientific inquiry









inquiry as means refers to "inquiry as an instructional approach intended to help students

develop understandings of science content" and inquiry as ends refers to "inquiry as an

instructional outcome" which they explained enables students to "learn to do inquiry in the

context of science content and develop epistemological understandings about the nature of

science and the development of scientific knowledge, as well as relevant inquiry skills such as

identifying problems, generating research questions, designing and conducting investigations,

and formulating, communicating, and defending hypothesis, models, and explanations" (p. 398).

Abd-el-Khalick et al., (2004) cautioned us that these aspects of scientific inquiry were often

neglected because of the misconception that students develop understandings about inquiry

implicitly by simply doing science.

Why Inquiry?

Inquiry-based learning environments can provide students with the opportunity to generate

and revise their thinking in interdisciplinary contexts (Myers & Botti, 1997) and achieve the goal

of developing general inquiry abilities, acquiring specific investigation skills, and understanding

science concepts and principles (Edelson et al., 1999). Sherman and Sherman (2004) added that

engaging in inquiry helps students develop an appreciation of "how we know"; an understanding

of the nature of science; and skills to become independent inquirers. According to a recent

review of the literature by the National Research Council (2000), positive effects in cognitive

achievement, process skills, vocabulary knowledge, critical thinking, and attitudes toward

science document the importance of students receiving explicit instruction on skills needed to

engage in inquiry.

The National Science Education Standards (NRC, 1996) emphasized the contributions of

inquiry-based instruction to teaching and learning science. According to NRC (1996),









When engaging in inquiry, students describe objects and events, ask questions, construct
explanations, test those explanations against current scientific knowledge, and
communicate their ideas to others. They identify their assumptions, use critical and logical
thinking, and consider alternative explanations. In this way, students actively develop their
understanding of science by combining scientific knowledge with reasoning and thinking
skills. (p. 2)

In addition, while offering a less content-oriented, metacognitive, collaborative,

argumentative, and communicative learning environment (Berge & Slotta, 2005), learning

through inquiry can also empower students to become independent, lifelong learners by allowing

them to gain an appreciation for discovery (Llewellyn, 2002). It also enables students to develop

their own ideas by building connections between their existing ideas and new ideas (Berge &

Slotta, 2005). Inquiry-based approaches engage students in cognitive processes used by scientists

such as asking questions, developing hypothesis, designing investigations, dealing with data,

drawing inferences, and building theories (Crawford, 2000). Doing this allows learners to

develop a broader understanding of science and improves their critical reasoning and problem

solving skills (Bodzin, 2005). Moreover, inquiry can also contribute to the development of

science content understanding by giving them an opportunity to apply their scientific

understanding in the pursuit of research questions; by uncovering new scientific principles and

refining their preexisting understandings, encouraging them to demand more knowledge

(Edelson et al., 1999).

Scientific Inquiry and Teachers

Benzce and Hodson (1999) reported a common myth about scientific inquiry that is

evident in science curricula which is "scientific inquiry is a simple, algorithmic procedure." Such

a conception of inquiry could lead to insufficient understanding and practice of scientific

inquiry-based instruction. All definitions and strengths of the scientific inquiry approach shed

light on the complex nature of inquiry-based practices.









First of all, inquiry-based learning demands activity and learning in authentic contexts

(Edelson et al., 1999). It achieves this authenticity by engaging students in problem posing,

problem solving, and persuading peers (Roth & Michelle, 1998). Lee, Greene, Odom, Schechter,

and Slatta, (2004) identified ten stages of inquiry: content, developing the question, designing the

experiment or study, defining and representing the problem, observing, exploring and generating

strategies, organizing, analyzing, interpreting, and evaluating [data]. According to Bodzin (2005)

and Brendzel (2005), however, inquiry in today's classrooms can take many different forms and

encompass a range of activities. While some inquiry-based activities provide for observation,

data collection, and analysis; others involve students in the design of open-ended activities based

on either a teacher-posed question or a classroom discussion (Brendzel, 2005).

The National Research Council (1996, p. 25) established five essential features of

classroom inquiry, which also formed the basis for the instrument developed in this study. These

features are:

* Learners are engaged by scientifically oriented questions.

* Learners give priority to evidence, which allows them to develop and evaluate explanations
that address scientifically oriented questions.

* Learners formulate explanations from evidence to address scientifically oriented questions.

* Learners evaluate their explanations in light of alternative explanations, particularly those
reflecting scientific understanding.

* Learners communicate and justify their proposed explanations.

Inquiry teaching requires students to exhibit certain skills. It engages students in the

systematic approach of reasoning in which they are supposed to formulate and test scientific

rules and laws (Looi, 1998); requires them to rely on their explanatory frameworks to develop

research questions and hypothesis (Wichmann Gottdenker, Jonassen, & Milrad, 2003); and

exercise a variety of skills, including formulating questions and hypotheses, observing,









predicting, collecting and analyzing data, classifying, using logical and critical thinking to

formulate conclusions, evaluate alternative explanations, and communicate their findings

(Sherman & Sherman, 2004; Bodzin, 2005). However, research indicates that children have

difficulties in conducting scientific investigations, data gathering, analysis, interpretation, and

communication (Edelson et al., 1999). Edelson et al., (1999) listed some reasons, associated with

required student skills, impeding the successful implementation of inquiry-based learning. They

noted:

* When students are not sufficiently motivated they fail to participate in inquiry activities.

* If students are not able to master data collection and investigation techniques, they cannot
conduct investigations that yield meaningful results.

* If students lack background knowledge and the opportunity to develop it, they will be unable
to complete meaningful investigations.

* If they are unable to organize their work and manage an extended process, they cannot
engage in open-ended inquiry.

Therefore, the role of teachers is crucial for successful enactment of inquiry approaches in

science classrooms.

As outlined so far, inquiry is a broad concept. Thus, the meaning of such a concept when

applied to classroom practice could become muddled and the integrity lost (Crawford, 2000). To

overcome this challenge, teachers need strong content knowledge (Crawford, 2000), pedagogical

content knowledge (Abd-el-Khalick et al., 2004), tools (Bodzin & Beerer, 2003), and assistance

(McNall, 2004; Sherman & Sherman, 2004) to become well versed in inquiry-based instruction.

Although the successful integration of inquiry in science classrooms require teaching skills such

as guiding, challenging, and encouraging student learning (NRC, 1996); organizing materials,

equipment, media, and technology (NRC, 1996); and a range of teaching strategies (Lee et al.,

2004) quite different from typical didactic science instruction (Sandoval et al., 1999), research









indicated that teachers typically found inquiry curricula to be difficult and time consuming to

teach (White & Frederiksen, 1999). According to some studies, preservice science teachers

graduated without conducting a single inquiry in their programs (Windschitl, 2003) and lacked

an understanding of inquiry, skills, and experiences (Newman et al., 2004) as well as training

and support (White & Frederiksen, 1998) to implement inquiry-oriented teaching. They also

have very few operational models (Crawford, 1997) to guide them in the implementation of

scientific inquiry-based instruction.

As highlighted above, the implementation of inquiry-based instruction demands a

significant shift in what teachers are doing in a science lesson (Crawford, 2000; Bodzin &

Beerer, 2003). The focus of much research has mainly been on student behaviors associated with

inquiry instruction and lesser attention given to teacher actions (Carnes, 1997) and to their ability

to make judgments about appropriate student experiences and evaluation in the learning context

(Bencze & Hodson, 1999). However, Fullan (1982) underlined the importance of what teachers

do and think for a successful implementation of educational change. It is certain that the

behaviors and thoughts of teachers must be more explicitly studied (Crawford, 2000) because

they influence (a) knowledge acquisition and interpretation, (b) define and select the task at

hand, (c) interpret course content, and (d) determine assessment (Keys & Bryan, 2001). Keys and

Bryan (2001) also noted teachers' beliefs about the nature of science, student learning, and their

role affect how they plan, assess, and teach an accurate view of inquiry in the classroom.

The teaching of science is becoming more student-centered and requiring more from

teachers in terms of classroom questioning and student involvement in discussions as well as

facilitation and organization of learning using alternative teaching approaches. According to the









National Research Council (2004), the following list of teacher actions is required in an inquiry

classroom:

* Providing experiences, materials, and sources of information for students to use directly.

* Showing the use of instruments or materials that students will need in their inquiry.

* Asking open and person-centered questions to elicit present understandings and how students
are explaining what they find.

* Engaging students in suggesting how to test their ideas or answer their questions through
investigation or finding evidence from secondary sources.

* Helping students with planning so that ideas are fairly tested where necessary.

* Listening to students' ideas and taking them seriously.

* Asking questions that encourage students to think about how to explain what they find.

* Creating opportunities for collaborative learning and dialogic talk.

* Scaffolding alternative ideas that may explain the evidence from their investigation.

The National Research Council (2000) supported the use of different strategies to develop

the knowledge, understandings, and abilities described in the National Science Education content

standards. Such an approach is not only inevitable but also desirable in order for teachers to

implement inquiry in ways that match their own beliefs and teaching styles (Keys & Bryan,

2001).

In addition to the attributes of inquiry-based instruction stated above, discussion and verbal

interactions with peers and teacher (Westbrook, 1997), argumentation (Zembal-Saul & Land,

2002; McDonald, 2004), relating information with prior knowledge and then integrating into

larger knowledge structures (Myers & Botti, 1997), identifying a problem and making a reasoned

judgment based on appropriate evidence (Lee at al., 2004), and learning from the process of

making errors, revising, posing new questions, and retesting (Keys, 1997) are also important

ways to enact inquiry in science classrooms. Schwab (1960, cited in NRC, 2000, p. 21), on the









other hand, recommended teachers provide students with readings and reports about scientific

research where students read about alternative explanations, different and conflicting

experiments, and debates about the use of evidence.

Implementing inquiry is not an easy task. Westbrook (1997) stated it is more than a

procedure or a method. Rather, "it is a process of investigating how or why or what and then

making sense of the resultant findings" (p. 2). Therefore, direct teaching methods may fail in this

process (Westbrook, 1997). Instead, students should be given both freedom and privilege where

freedom allows students to choose content related to their interests, generate their own questions,

invent methodologies, and make sense of data; and privilege involves learners with the ideas and

practices of the scientific community and make these ideas meaningful at an individual level

(Keys, 1997). However, this does not mean that teacher-control is not valued in inquiry

instruction. Flick (1997) noted that skilled science teachers achieve inquiry-based learning in

moderate to highly controlled conditions by explicitly teaching inquiry skills and allowing

students to apply inquiry skills to authentic problems.

One way to achieve this is to use technology effectively in science classrooms. The section

that follows describes the ways in which technology can be incorporated into classroom

instruction, in general and science education, in particular.

Technology and Science Education

The last several decades have seen the rise of three significant educational issues:

standards, the integration of technology, and teacher quality (Wenglinsky, 2005). Because of its

influence in society, the integration of technology has gained people's attention for its potential

to contribute to classroom instruction. Houhgton (1997) contended that as computers and

advanced telecommunications technologies revolutionize nearly every aspect of life, the

attention should not be on whether technology should be incorporated into teaching and learning,









but how to achieve it. Collins (1991) stated education cannot resist the technology movement

because it has already transformed the world and the way education is conducted (Lou, Abrami,

& d'Apollonia, 2001); shaped how people think, learn, and communicate (Gura & Percy, 2005);

and changed the way people do science, handle personal affairs, and run businesses and the way

schooling takes place (Carin & Bass, 2001). Collins (1991) listed eight possible shifts in

instruction in schools due to the integration of technology into classrooms:

1. From whole-class to small-group instruction,

2. From lecture and recitation to coaching,

3. From working with better students to working with weaker students,

4. Toward more engaged students,

5. From assessment based on test performance to assessment based on products, progress, and
effort,

6. From a competitive to a cooperative social culture,

7. From all students learning the same thing to different students learning different things,

8. From the primacy of verbal thinking to the integration of visual and verbal thinking.

However, technology is not a transformative power in and of itself (Pederson & Yerrick,

2000). While properly designed and implemented technologies can potentially improve learning

(Kozma & Schank, 1998) and promote teaching for understanding (Nickerson, 1995), poorly

designed and implemented technologies can hinder learning (Nickerson, 1995; Bransford et al.,

1999). Researchers recommended technology should be used to increase and assist student

learning (Kozma, 1991; Sheffler & Logan, 1999) and should go beyond superficial use (Cooper

& Bull, 1997) by turning learning from simple assimilation into a process of active construction

and supporting collaborative learning (Salomon, 1992).

A Nation at Risk, written by the U.S.A. Research, Inc in 1983, "sparked the movement of

technology integration in schools for the purpose of producing more technologically literate









workforce that is ready to compete in the 21st century world economy" (McNall, 2004, p.1).

Subsequent reports continue to urge educators toward meeting this goal. In 2000, the U.S.

Department of Education released a report "eLearning: Putting a World-class Education at the

Fingertips ofAll Children" to develop strategies for the effective use of technologies in

elementary and secondary education (U.S. Department of Education, 2000, p. 4). In this report,

five national goals for technology were identified:

* All students and teachers will have access to information technology in their classrooms,
schools, communities, and homes.

* All teachers will use technology effectively to help students achieve high academic
standards.

* All students will have technology and information literacy skills.

* Research and evaluation will improve the next generation of technology applications for
teaching and learning.

* Digital content and networked applications will transform teaching and learning.

Regardless of the call for the infusion of technology into the daily learning occurring in

schools, the findings on the outcomes of technology use in schools produced controversial

results. The National Center for Education Statistics (2002) reported no significant change in

student achievement despite an increase in computers in schools. Kracjik, Marx, Blumenfeld,

Soloway, & Fishman (2000), however, noted that although researchers do not have enough

evidence on computers increasing achievement, many studies showed positive effects associated

with computer aided instruction. Moreover, Pollard and Pollard (2004) cited two meta-analytic

studies (Kulik, 1994 & Schater, 2001) in which results indicated technology-rich environments

enabled students to learn more and faster, have more positive attitudes, and improved their

achievement. Lou et al., (2001) conducted a meta-analysis and concluded when computer

technologies were used for small group learning rather than individual learning, it produced more









positive results in terms of learning. Pederson and Yerrick (2000), on the other hand, averred

computers seem to only be used to support existing learning and teaching patterns.

Promising findings have been noted in other significant research studies. For example,

Morgan (1996) stated that fourth and eight-grade students who used computers to play learning

games and for simulation and models scored higher, on average, than others who did not. Berger,

Lu, Belzer, & Voss (1994) reported higher thinking skills for secondary school students who

were exposed to interactive video disk, computer-assisted instruction, and mastery-based

learning. Another study (Spitulnik et al., 1998) indicated students who designed technological

artifacts using hypermedia had better integrated understanding. It is also evident in the literature

that when computer technologies were used for collaborative activity and designed for mindful

engagement of students they created new opportunities for decision making, thinking, and

constructing (Salomon, Perkins, & Globerson, 1991; Salomon, 1992). The results from the

National Assessment of Educational Progress study (also known as the Nation's Report Card)

also revealed positive association between computer use and student performance when

computers are used in a constructivist fashion (Wenglinsky, 2005).

Overall, findings mentioned above shed light on future research directions in the field.

Bebell, Russell, & O'Dwyer (2004) suggested that despite the tendency to examine the effects of

technology on student learning, effects of learning and understanding of how teachers and

students are using technology should take higher consideration. Similarly, Lewis (1999)

addressed the need for more evaluative studies that focus on attempts at integration. Pollard and

Pollard (2004) organized a Delphi panel to examine the future of research in the field. The panel

recommended that research efforts should focus on the role of technology in improving students'

problem solving abilities and helping them accomplish learning tasks rather than on scores on









achievement tests. In addition, inservice and preservice teachers' use of technology has also been

a high priority area in the field (Pollard & Pollard, 2004). Bebell et al., (2004) put that research

on teachers' use of technology lacked a clear definition and stressed the importance of providing

valid measures of technology use among teachers.

Technology and Learning

Duffy and Cunningham (1996) provided a rationale for using technology for learning.

They suggested that learning occurs in context and is an active, social, and a reflective process.

Such a rationale could provide teachers a framework when seeking out ways to effectively

integrate technology into instruction. Technology has the potential to reinvigorate learning by

increasing motivation; providing recontexualized and individualized instruction; improving

writing; encouraging student publishing and research; and transforming the classrooms into a

multiple intelligence-centered learning environment (Gura & Percy, 2005). Computers, for

example, provide great interactivity and have the ability to become any media, to present

information from many different perspectives, and to become reflective (Carin & Bass, 2001).

Technologies can provide scaffolds and tools to enhance learning, give feedback (Bransford et

al., 1999) and use multiple representations, modeling and visualization to enhance learning

(Kozma, 1991; Morgan, 1996; Spitulnik et al., 1998; Dede, 2000; Flick & Bell, 2000; Gura &

Percy, 2005). They also help build a community of learners by bridging teachers, students, and

experts (Bransford et al., 1999; Dede, 2000). Another important use of technology in instruction

is its ability to engage students in problem-rich environments to explore and solve (Bransford et

al., 1999); develop math or science concepts as well as collaborative skills (Stables, 1997); deal

with misconceptions (Cognition and Technology Group at Vanderbilt, 1992; Nickerson, 1995);

and master more complex subjects via rich interactions using external resources (Dede, 2000).

Technology tools are also essential in helping students generate and test hypotheses and build









explanations of scientific phenomena (Spitulnik et al., 1998) and analyze, visualize, solve,

investigate, and communicate information (Loveless et al., 2001; Rieser, Krajcik, Moje, & Marx,

2003; McNall, 2004).

In addition to these potential benefits of technologies, Hooper and Rieber (1995) noted

three principles and their implications for using technology in the classroom: (a) effective

learners actively process lesson content, (b) presenting information from multiple perspectives

increases the durability of instruction, and (c) effective instruction should build upon students

knowledge and experiences and be grounded in meaningful contexts. Therefore, technology tools

should be used in ways to bridge the gaps between students and scientists, to provide meaningful

problems to students, and to help students develop background knowledge and investigation

techniques (Edelson et al., 1999).

Models of Technology Use

There are multiple methods for studying how technology is used in instruction. One model

divides technology into two categories: Effects i/ ith and effects of technology. Effects ii/lh

technology refers to changes that take place as the result of engagement in an intellectual

partnership with a technology tool or peers; and effects of technology refers to long lasting

changes that take place as a result of that intellectual partnership (Salomon et al., 1991; Salamon,

1998). For the purposes of this study, it is important to point out approaches to technology

integration in science classrooms as the goal of the study is to develop an instrument that focuses

on science teachers' perceptions and uses of technology in inquiry-oriented science classrooms.

Carin and Bass (2001) interpreted the Information Society for Technology in Education (ISTE)

technology standards considering how to connect educational technology and science education

and categorized those standards into three areas: (a) standards related to hle/ ning n i/h technology

in which technology is used to enhance productivity, communications, research, problem









solving, and decision making, (b) standards related to learning from technology in which

computer-assisted instruction, tutorials, simulations, and multimedia presentations are used to

enhance science learning, and (c) standards related to learning about technology in which

learning how computing systems operate, learning how to use them in classroom settings, and

considering societal implications of technology use are emphasized. The instrument developed in

this study addressed all three of these standards to varying degrees.

Technology in Science Instruction

There is a strong support in the literature for using technology in science instruction. Flick

and Bell (2000) highlighted this meaningful partnership between the two fields across most of

the twentieth century. This partnership was also enforced by current reforms represented by

institutions such as American Association for the Advancement of Science and National

Research Council (Pederson & Yerrick, 2000). Such reforms encouraged integration of

technology in science education to help students use scientific knowledge to predict, explain, and

model phenomena (Spitulnik et al., 1998). In addition, McNall (2004) reported that the National

Educational Technology Standards (NETS, ISTE) and the National Science Teachers

Association (NSTA) recommended effective use of technology in science instruction to enrich

the learning and teaching of science and to support inquiry. The impact of technology on science

education challenged both science educators and science teachers and changed the ways each

interact with students in their classrooms (Flick & Bell, 2000) and offered new opportunities to

transform science instruction with inquiry projects that improve science, technology, and

language literacy (Linn et al., 2004). By doing this, technology integration is considered to have

an impact on connecting students to the designed world (NRC, 1996), empowering them to learn

(Berger et al., 1994) and to become more active explorers of their environment (Carin & Bass,

2001). Lewis (1999) suggested educational technology to establish itself in relation to other









subjects and noted that the field of educational technology has to understand the integration of

technology to other subjects in the curriculum. The relationship between science education and

technology integration discussed above pinpoints the importance of this perspective.

Technology and Inquiry

Although technology is a powerful tool for learning merely employing it does not produce

the desired effects (Lou et al., 2001). Literature shows that as new technologies became available

to educators, science teachers and educators struggled to find effective ways of using them (Linn,

1998) especially for the purpose of inquiry instruction. In addition, inquiry teaching is

challenging because it requires strong background knowledge with regard to science content,

inquiry-oriented instruction, pedagogy, and technology (Pedersen & Yerrick, 2000; Williams et

al., 2004).

Spitulnik et al., (1998) pointed out that the new vision of science education has become

engaging students in scientific inquiry activities and using technological tools to achieve this

goal. Edelson et al., (1999) believed all of the fundamental properties of computing technologies,

such as the ability to store and manipulate large quantities of information, the ability to permit

interaction in a variety of audio and visual formats, the ability to perform complex computations,

the support for communications, and the ability to give feedback to users offer benefits for

inquiry-based learning. Flick and Bell (2000) noted that activities involving technology should

make connections to student experiences and promote inquiry-based learning. Such learning

demands new teaching methods and technology offers promising solutions (Linn et al., 2004).

The contributions of inquiry-based learning and educational technology to each other are

reciprocal. Namely, recent advances in cognitive science and educational technology

(particularly computer simulations and modeling tools) set the stage for developing more

effective approaches to the teaching of scientific inquiry (White & Frederiksen, 1998). Inquiry,









on the other hand, contributed to the educational technology field because (a) it related

fundamentally to the basic claims of the field, (b) it reminded us that educational technology is

about learning and teaching, and (c) both inquiry and technology shared and conformed to

conceptual frameworks such as situated cognition and constructivism that unite technology with

other school subjects (Lewis, 1999). Carin and Bass (2001) contended that the notion of design

in technology is parallel to science and, as identified by the National Science Education

Standards, technological design facilitates scientific inquiry in defining a problem, designing an

approach, implementing a solution, evaluating the solution, and communicating the problem,

design, and solution to others.

McNall (2004) highlighted the capacity of educational technology tools to support inquiry

learning in science by assisting students in visualizing abstract concepts and engaging them in

rich experiences. According to Alagic et al., (2003), information technologies can play a special

role in inquiry-based learning as the subject of instruction or as a tool for instruction. They noted

that, when used in inquiry-oriented activities, technology is most often used (a) as a tool, (b) in

the context of solving a problem, (c) to augment communication by expanding audiences, or (d)

to broaden collection of representations. Similarly, Looi (1998) stated technology tools in the

form of interactive learning environments could enrich learning (1) as instructive tools, (2) as

constructive tools, (3) as communicative tools, and (4) as situating tools.

Literature provides several strategies regarding the successful integration of technology

and inquiry-based instruction. Edelson et al., (1999) categorized these technologies as tools for

modeling phenomena and processes from the real world (e.g., Model-It, Jackson, Stratford,

Krajcik, & Soloway, 1996; ThinkerTools, White, 1993), visualizing and analyzing quantitative

data (e.g., Tabletop, Hancock et al., 1992; GLOBE, Rock, Blackwell, Miller, & Hardison, 1997),









exchanging data and ideas across distances (e.g., GLOBE, Rock et al., 1997; Kids as Global

Scientists, Songer, 1995), structuring and supporting discussion (e.g., CoVis, Edelson et al.,

1996; CSILE, Scarmadalia & Bereiter, 1994), and providing access to information in the form of

digital collections and libraries (e.g., Knowledge Integration Environment, Linn, Bell, & Hsi,

1998).

Multiple Examples of Technology Used to Reach Goals of Science Inquiry

Although the literature pinpoints the potential contribution of technology to science

education in general, and science inquiry in particular, there are not many examples of

technologies designed to attain the goals of scientific inquiry. This section briefly introduces

some of the well-known examples of technologies, such as the GLOBE, ThinkerTools and

Model-It, used to expose students to scientific inquiry.

As a modeling tool, Model-It, developed by the University of Michigan, provided students

with an open-ended task, where students were able to create their own models to represent some

ecological phenomena. It focused on higher-level concepts and enabled students to ground their

experience and prior knowledge, build representations, and couple actions, effects, and

understanding (Jackson et al., 1996). This technology application supports the development of

qualitative, verbal representations and provides simultaneous, linked textual-to-graphical

representation of relationships. It also helps students connect their actions, the visual feedback

provided, and their own mental representations of the phenomenon (Jackson et al., 1996).

ThinkerTools, developed by White (1993), focused on learning how to construct causal models

based on real-world phenomena rather than learning how to solve well-defined quantitative

problems. In general, ThinkerTools supported strategies such as questioning, predicting,

experimenting, modeling, and applying (White & Frederiksen, 1998). It also enabled students to

develop conceptual models that embody the principles of Newtonian mechanics, and to apply









their models in making predictions, solving problems, and generating explanations (White,

1993). The ThinkerTools environment was shown to be capable of changing students' views of

aptitude for learning and understanding science (White & Frederiksen, 1998) and of enabling all

students to improve their performance on various inquiry and physics measures (White &

Frederiksen, 2000). It was also effective in reducing the performance gap between low and high

achieving students (White & Frederiksen, 1998; 2000). Tabletop is a computer-based data

analysis tool based on animated visual representations and enables students to solve real

problems and to answer authentic questions (Hancock et al., 1992). Vignettes from clinical

sessions of this study illustrated Tabletop stimulated students' interest and increased students'

successful interactions with data creation and data analysis. Students became engaged in subtle

and important questions in data design and data analysis and developed good discussions related

to aggregate reasoning (Hancock et al., 1992). The GLOBE project (Global Learning and

Observations to Benefit the Environment) is a worldwide, hands-on science program for primary

and secondary school students. It creates a partnership between students, their teachers, and the

scientific research community and introduces students to the process of doing real science and

allows them to learn by doing (Rock et al., 1997). The students in the GLOBE project make

scientific measurements in their local environments, report data through the Internet, publish and

present their data on GLOBE, create visuals to present information and collaborate with peers

and scientists. The Kids as Global Scientists (KGS) program uses communication features of the

Internet to create a learning environment that enables students to solve real and complex

problems associated with weather phenomena and supports reflective questioning, investigation,

data collection, analysis, comparisons, predictions, and inquiry-based activities in which students

act as reporters, participants, and providers and data and information (Songer, 1996). Findings









indicated the effectiveness of authentic inquiry science (Lee & Songer, 2003) as well as

significant content and inquiry gains (Songer, Lee, & Kam, 2002) associated with concepts in

biodiversity and the design pattern, formulating scientific explanations from evidence (Songer &

Wnek, 2003) and weather content knowledge (Mistler-Jackson & Songer, 2000). Results also

showed a high positive attitude toward learning science (Kam & Songer, 1998; Mistler-Jackson

& Songer, 2000). With regard to perception of difficulty of learning science, the results showed

there was a statistically significant increase in the percentage of girls who perceived science as

not difficult at all (Kam & Songer, 1998). In addition, a content analysis of the messages in the

electronic discourse environment of the KGS program revealed that it enabled learners to build

an electronic community of science learners and fostered student understanding through specific

tasks, more student-student communication, socializations among participants, sharing personal

experiences and scaffolding by experts (Lee & Songer, 1998). The CoVis (Learning Through

Collaborative Visualization) project is founded on the premise that classroom science learning

should resemble the open-ended, inquiry-based approach of science practice. The project enables

teachers and students to learn science by doing in connection with communities of science and

science educators (Gomez, Gordon, & Karlson, 1995). Computer-Supported Intentional Learning

Environments (CSILE) are designed to support knowledge building in learning communities.

Scarmadalia and Bereiter (1994) noted that CSILE was based on research on intentional learning,

process aspects of expertise, and discourse in knowledge-building communities. It is also based

on solving logistic problems that Scarmadalia and Bereiter thought have the greatest potential for

educational technology. The Knowledge Integration Environment (KIE) is designed for using the

Internet to enhance student understanding of science. It offers students science models that apply

to problems they encounter in their everyday lives and engages them in personally relevant









science projects (Linn et al., 1998). Linn et al., (1998) reported findings indicating significant

gains in students' understanding of the nature of light, heat, and other scientific domains as well

as in students' use of their new knowledge to interpret new problems. In addition to these

technology examples, Edelson et al., (1999) targeted scientific visualization and developed

visualization environments (The Climate Visualizer, The Radiation Budget Visualizer about

global warning, The Greenhouse Effect Visualizer, and WorldWatcher) for the interpretation of

relative weather data in order to support inquiry-based learning. The Inquiry Page is another

web-based tool that provides its users a dynamic and flexible environment, which supports

teaching and learning in diverse educational settings and facilitates real-world application of

inquiry-based learning across subject areas. It engages learners in a learning cycle model based

on Dewey's ideas and enables students to ask, investigate, create, discuss, and reflect (Bruce &

Bishop, 2002; Comstock, Bruce, & Harnish, 2003). It is reported that the Inquiry Page tool was

successful in fostering collaborative inquiry among students (Bruce & Bishop, 2002) and

creating a community of learners including teachers and students in terms of inquiry learning

(Benson & Bruce, 2001). Another web-based inquiry environment is called the Web-Based

Inquiry Science Environment (WISE) where students design solutions to problems, generate

predictions before conducting experiments, use scientific evidence to support theories or

conclusions, debate contemporary science issues, and reconcile differences between new and

prior science ideas (Williams et al., 2004). This free website is designed by the University of

California at Berkeley. Some of the contemporary science issues studied on the website are

earthquakes, malaria, genetically modifies foods, HIV, water quality and thermodynamics. A

research study with 1100 sixth grade students indicated that WISE students were able to achieve









a deeper understanding of the content knowledge and developed students' model-based inquiry

skills (Gobert, Slotta, Pallant, Nagy, & Targum, 2002).

Although there are numerous illustrations where technology have been created to assist the

integration of science in classrooms to promote the goals of scientific inquiry, there is a need for

instruments to assist science teachers and science educators to frame their work in using

technology to promote the goals of scientific inquiry. Hence, this study will develop an

instrument to begin providing this framework on which science teachers can base and evaluate

their work.

Summary

The role of inquiry in science education has been strongly influenced by current reform

reports or documents in the United States (Crawford, 1997; Flick, 1997; Abd-el-Khalick et al.,

2004) such as the National Science Education Standards (NRC, 1996; 2000) and Benchmarks for

Science Literacy (AAAS, 1993). In addition, the use of technological tools to enrich the learning

and teaching of science and to support inquiry was recommended by National Science Teacher

Association and outlined in National Educational Technology Standards of the International

Society for Technology in Education (McNall, 2004). This review of the literature has attempted

to establish a framework to develop an instrument that measures science teachers' self-reported

perceptions and uses of educational technologies to enact scientific inquiry in their classrooms. It

was shown there is little research combining scientific inquiry and technology use by science

teachers. This literature review indicates a need for more research into understanding teachers'

perceptions and uses of technological tools for science inquiry purposes.









CHAPTER 3
METHODOLOGY

This chapter provides an overview of the procedures that were used to conduct the study. It

contains the research questions and a description of the participants, data collection,

instrumentation, and data analysis techniques.

Introduction

This study is based on the premise that technology could enhance the quality of scientific

inquiry-based instruction and attempts to answer the question of how science teachers use

technology to attain the goals of scientific inquiry-based instruction. The instrument developed

in this study can serve as a useful guide for science teachers in the integration of technology in

scientific inquiry-based learning environments. For this purpose, the study investigated science

teachers' self-reported perceptions about implementing scientific inquiry using technology and

explored the degree to which they use technology for such a goal. This study also examined

whether relationships existed, and the degree of these relationships, among a set of variables

related to teachers' use of technology for inquiry purposes.

Research Questions

This study had two overarching research questions and five supporting questions. The

main questions focused on how teachers use technology to implement the goals of scientific

inquiry and the relationships between their self-reported perceptions and practices regarding this

implementation. The supporting questions, however, articulated these relationships by addressing

some teacher demographics and background/professional development variables as well as the

level and frequency of their preparedness and use of certain inquiry tasks and technology tools.

While the total scores obtained from the survey and the relationships between perception and

practice items in the survey were utilized to help answer the overarching questions of the study,









relationships obtained from supporting questions also contributed to answering these overarching

questions. Through the creation of an instrument, the study addressed the following research

questions:

Overarching Questions

1. How are teachers using technology to implement the goals of scientific inquiry in their
classrooms?

2. What are the relationships between teachers' self-reported perceptions and practices
regarding the use of technology to attain the goals of scientific inquiry?

Supporting Questions

What are the relationships between teachers' self-reported perceptions and practices regarding
the use of technology to attain the goals of scientific inquiry in terms of:

* Teacher demographics and teacher background/professional development variables?

* How often do teachers support students to engage in certain inquiry skills in their science
classrooms?

* How often do teachers use certain technology tools in their science classrooms?

* How prepared do teachers feel to support students to engage in certain inquiry skills in their
science classrooms?

* How prepared do teachers feel to use certain technology tools in their science classrooms?

Technology in the SIT-TIPPS

In this study, technology referred to a range of devices and technological processes

specifically used for teaching and learning purposes in K-12 settings. For the purposes of this

study, the following devices, with examples, comprised the definition of technologies:

Computers (desktop, laptop); presentation devices (such as video projectors, LCD panels); Smart

Board/Promethean interactive boards; wireless communication devices (such as PDAs, student

digital response systems); computer software (such as Word processors, desktop publishing,

spreadsheets, presentation software, databases, simulations, educational games, graphing and

data analysis software, video and picture editing software, etc.); graphing/scientific calculators;









Portable Global Positioning Systems (GPS); digital data collection devices (such as pH, pressure

and temperature probes, digital microscopes, Navigator systems); videoconferencing,

teleconferencing; Internet technologies (such as e-mail, websites, online databases, virtual field

trips, online simulations and science games, Wikis, blogs, podcasts, videocasts, Google Earth

and other Google tools, online learning communities); and data collection telecollaborative

activities (such as Journey North, SCOPE, Amazing Space).

Data Collection

The researcher attempted to attract as many participants as possible to the study for a high

turnout rate in order to run a successful factor analysis (at least ten times the number of items)

and to have a representative sample. Because a random selection of participants is very difficult

to achieve in such nationwide studies, the researcher attempted to reach as many science teachers

as possible to have a wider representation from various states and social settings. For this

purpose, middle and high school science teachers from various states in the U.S. were contacted

by the following methods: (1) The researcher visited the official websites of public middle and

high schools listed on the websites of Departments of Education in various states including

Florida, Virginia, Kansas, Wisconsin, Connecticut, and Alabama and the city of San Diego. This

method enabled the researcher to collect individual email addresses of over 4,000 science

teachers. The selection criteria for these states were their geographical distributions in order to

increase sample diversity. (2) The researcher contacted professional science teachers associations

nationwide and statewide to have these organizations disseminate the invitational e-mail message

to their members. This helped the researcher to reach an audience as representative as possible of

the science teachers in the U.S. because, many of these statewide and nationwide organizations

had access to teachers from different parts of the country. (3) Finally, the researcher also sent

email messages to listservs (UFTScienceComm, middleschoolscience,









HighSchoolScienceTeachers, science connection, astroednews, and learningscienceconcepts)

serving science teachers to which he had an online membership.

Data was collected online in the form of a web-based questionnaire (Appendix B). For this

purpose, a professional web service, Survey Monkey, was used, which enabled the researcher to

host the online survey, create email lists, collect data, and report basic statistics such as the

number of responses for each individual item and frequencies. The researcher sent potential

participants, directly or via listservs, an invitational e-mail message to inform them about the

nature of the study and encourage their participation to complete the web-based questionnaire.

The online version of the survey consisted of an interface including the IRB consent form

(Appendix A), definition of technology, and the survey items. The participants were not able to

access the survey unless they read the IRB consent form and agreed to participate in the study by

clicking on the "Press here to start the Survey" button. An American Association for the

Advancement of Science report titled Ethical and legal aspects of human subject research on the

Internet (Frankel & Siang, 1999) and an American Psychological Association (APA) report titled

Psychological research online: Opportunities and challenges (Kraut et al., 2003) recommended

similar practices when getting informed consent from online participants.

Consent form informed all participants that participation in the study was voluntary, they

had a right to withdraw from the study at any point without consequence, they may skip any

question they did not wish to answer, and there were no anticipated risks, compensation, or other

benefits for their participation in the study. It also provided information about how to contact the

principal investigator, the supervisor, and the University of Florida Institutional Review Board

(UF IRB) should they had any questions or concerns. Participants were encouraged to make a









copy of the consent form for their own records as well as given the option to provide their email

addresses if they want the researcher to send them a copy of it in an electronic format.

The online survey started with a question that asked whether a participant is a middle or

high school science teacher in the U.S. This helped identify participants who were not among the

target population of the study and thus, whose data was deleted from the data pool.

Over fifteen hundred people (1548) visited the link provided to them within the invitational

email message. Of these visitors, 254 people (16.4%) responded "no" to the first question, which

asked whether they are a middle or high school science teacher in the United States. Because

these people were not among the targeted population, any data they provided was deleted from

the database. On the other hand, 1294 people (83.6%) indicated that they were middle or high

schools science teachers in the U.S. However, not all of these visitors were treated as

participants. Although 715 of these science teachers (55.3%) provided valuable input and

proceeded to the end of the survey, the rest of the visitors (517, 44.7%) did not submit any input

and just seemed to explore the survey. These people either did not answer any of the survey

items or responded to only a few questions in the beginning of the survey. Therefore, any visitor

who did not answer any of the statements or responded to less than 40 statements (out of 120

statements plus demographics/teacher background questions) was neglected. Therefore, 715

science teachers comprised the sample of the study.

Instrumentation and Instrument Development Procedures

In the development of any instrument, the establishment of validity and reliability is

crucial. Otherwise, the instrument will be ineffective. For this study, the researcher followed the

guidelines set forth by Devellis (2003), Gable and Wolf (1993), and Mueller (1986) to establish

validity and reliability for the SIT-TIPPS.









First, in order to achieve clarity (Devellis, 2003) in defining the construct this study

intended to measure, the researcher made use of the extensive literature and theory related to the

construct, established specificity, and was careful about what to include in the measure. This

helped the researcher specify the goals of the instrument being developed (Mueller, 1986; Gable

& Wolf, 1993; Devellis, 2003) and made sure the researcher had the same understanding of the

instrument content as the respondents (Mueller, 1986). For this purpose, the researcher grounded

the object of measurement in the substantive theories related to the phenomenon (Devellis,

2003). Therefore, in order to develop an instrument that measures science teachers' self-reported

perceptions and uses of technology for scientific inquiry purposes, the researcher meshed the

essential features of scientific inquiry described in the National Science Education Standards

(NRC, 2000) with the extensive literature available on inquiry-based science instruction and the

uses of technology in instruction (see Appendix C). This approach of following theoretically

based conceptual definitions helped the researcher define appropriate operational definitions that

lead to good content and construct validity of the measure (Gable & Wolf, 1993) as described in

the sections that follow.

Specificity or generality of the construct is another factor contributing to the clarity of the

construct being measured (Devellis, 2003). The SIT-TIPPS measured very specific behaviors

regarding the use of technology for inquiry purposes in science classrooms. Moreover, the

specified goals of the SIT-TIPPS were derived from theory and a well-done literature review

guided the process of developing constructs that are distinct from other constructs and clearly

written (see Appendix C).

Based on the purpose of the scale, the researcher constructed a large pool of behaviors (see

Appendix C) that were later transformed into items and categorized under each of the five









essential features of scientific inquiry described by the National Science Education Standards.

These five features, which formed the basis for the development of the scale in this study, were

meshed with technology related constructs and modified to reflect teacher perspective. For

example, the first feature of scientific inquiry that reads "learner engages in scientifically

oriented questions" were transformed into "teacher engages students in scientifically oriented

questions." Then, the researcher outlined specific behaviors based on the National Science

Education Standards (2000) and the extensive literature review for each of the five teacher-

oriented constructs. For instance, some of the behaviors identified for the first category were:

"asking why and how questions", "generating a need to know in students", and "encouraging

students to demand more knowledge." Such behaviors then constituted the items of the scale.

This process yielded a large item pool, which became candidates for eventual inclusion in the

scale (Mueller, 1986; Devellis, 2003). All items making up the scale reflected the construct

underlying them and were chosen cautiously to create a homogenous scale (Devellis, 2003).

This strategy is used to generate items in two different categories: Teacher perceptions and

practices. Because another purpose of the study was to focus on teachers' current practices of

using technology for scientific inquiry purposes, as well as their self-reported perceptions of

using technology for such purposes, the researcher created parallel items for both categories. For

instance, one of the items in the first category (teacher engages students in scientifically oriented

questions) took the form: "I integrate technology to enable students to conduct successful

empirical investigations" in the "teacher practices" category, whereas in the "teacher

perceptions" category it was expressed as "A science teacher should integrate technology to

enable students to conduct successful empirical investigations." The main objective for this

strategy was to look at potential differences between teachers' self-reported perceptions and









actual practices regarding the use of technology for scientific inquiry purposes in their

classrooms.

The internal consistency reliability of the scale is directly related to the number of items in

a scale as well as how strongly the items correlate with one another, when all else is held equal

(Mueller, 1986; Devellis, 2003). For this purpose, the researcher employed as large a pool of

inquiry behaviors as possible and selected as many items as possible from this pool to have high

internal consistency reliability. However, when determining the number of items in the scale, the

researcher also took into consideration the suggestion that specific and tightly conceptualized

objects can be measured by fewer items than the loosely defined and amorphous objects

(Mueller, 1986). Therefore, the object of measurement in this study was kept specific and

conceptualized according to a theoretical framework and the related literature.

In addition, during the construction of the items, the researcher attempted to avoid using

items that are (a) ambiguous, (b) exceptionally lengthy, (c) difficult to read, (d) double-barreled

(conveying two or more ideas), and (e) composed of multiple negatives (Devellis, 2003). In

addition, the researcher also avoided the use of absolutes such as always and never. Feedback

from the 6 content reviewers for the study (see page 65 for their credentials) was very useful at

this process. In contrast to what Mueller (1986) suggested about using both positively and

negatively worded items to prevent the problem of little variance in the scale, the researcher

generated only positively worded items due to the length of the scale and to decrease the level of

cognitive load for the participants. For the same reason, no additional items were included in the

instrument to detect any flaws or problems influenced by other motivations or any measures of

relevant constructs to contribute to the validity of the scale as suggested by Devellis (2003).









Likert scaling was used in the instrument because they are easy to construct, can be highly

reliable, and have been successfully adapted to measure many different kinds of attitudes

(Nunnaly, 1978). The statements in the scale were presented in a 5-point strongly agree/disagree

format, because it is one of the most reliable one (Gable & Wolf, 1993, Mueller, 1986); yet these

statements were fairly (though not extremely) strong to reflect true differences of opinion

(Devellis, 2003). In addition to 5-point strongly agree/disagree format, the researcher also used a

4-point "not adequately prepared/very well prepared" format and a 5-point "never/almost all"

type frequency format for Inquiry skills and technology tools sections of the survey (see survey

in Appendix B). After a review of the items during the content-validity procedure, the scale was

administered to a representative sample of more than 700 science teachers and item-analysis,

alpha reliability, and factor analysis procedures followed to check for the validity and reliability

of the scale being constructed.

Content Validation Process:

The next step in the instrument development procedure was to have the item pool reviewed

by experts who are knowledgeable in inquiry-based science instruction and instructional

technology. This validation strategy was utilized to maximize the content validity of the

instrument by confirming or invalidating the definition of the constructs the study intended to

measure as well as evaluating the items' clarity and conciseness (Devellis, 2003). For this

purpose, the researcher contacted one expert on educational technology to provide insight into

technology related aspects of the scale. Five other experts in science education, as well as in

integrating technology into science classrooms, comprised the content validation team. The

researcher provided the content validators with the working definitions of the constructs

including what is meant by technology in the study, and then asked them to rate each item with

respect to its relevance to the construct (1 being "completely irrelevant, 2 being "somewhat









relevant", and 3 being "highly relevant") as it has been defined (Devellis, 2003) and with respect

to its predetermined category of one of the five essential features of scientific inquiry used in this

study. The content validators were also asked to indicate how certain they felt about their

agreement of the item to the construct (1 being "completely unsure", 2 being "unsure", 3 being

"pretty sure", and 4 being "very sure"). Each content validator, rated the 50 items in the scale

and provided feedback on the contents of the "inquiry skills" and "technology tools" sections of

the instrument.

The experts who participated in the content validation of the scale were:

* Dr. Collen Swain, Educational Technology, School of Teaching and Learning at the
University of Florida (educational technology expert who provided feedback on technology
related content)

* Dr. Tom Dana, Science Education, School of Teaching and Learning at the University of
Florida (content validator)

* Dr. Rose Pringle, Science Education, School of Teaching and Learning at the University of
Florida (content validator)

* Dr. Troy Sadler, Science Education, School of Teaching and Learning at the University of
Florida (content validator)

* Dr. Karen Irving, Science Education, School of Teaching and Learning at the Ohio State
University (content validator)

* Dr. Dina Mayne, South Effingham High School in Georgia, Chemistry Teacher (content
validator).

Before administering the instrument to the study sample, the researcher calculated the

percent agreement among the content validators on whether they agreed with the researcher-

assigned categories to items as well as the average level of certainty and relevance across

validators. Any item whose percent agreement was below 80 percent was deemed "problematic"

and subjected to either revision or deletion. Whether it is more than 80 percent or not, an average

certainty score of less than 3.0 (out of 4.0) and an average relevance score of less than 2.0 (out of









3.0) were selected as additional criteria to determine whether an item needed revision or deletion

from the scale. Moreover, the content validators also provided feedback regarding the

demographic information/teacher background segment and the "inquiry skills" and "technology

tools" segments of the instrument that aimed to collect information on the extent science teachers

use certain inquiry skills and technology tools in their classrooms as well as how well prepared

they feel about these skills and technology tools. Based on the calculations, the researcher found

out that 10 items (5 perception and 5 practice items that were parallel to each other) had percent

agreement scores of less than 80 percent. Then, based on feedback from two experts, some of

these items have been revised to fit into the category that is intended to represent the item and/or

to make them more relevant, understandable, and concise. In addition, content validators' written

comments on some of the statements were also used to make such revisions. For instance, the

item that read "I integrate technology to improve students' abilities to describe scientific

theories, rules, laws, and events" was revised into "I integrate technology to improve students'

skills to check their results against existing scientific knowledge." No other item needed revision

or deletion based solely on relevance and/or certainty level scores because of above cutoff scores

(above 2.0 for relevance and above 3.0 for certainty categories).

After the content validation process was completed, the researcher designed the online

version of the scale and then posted it. The 715 science teachers completed the survey, which

was as large enough to eliminate subject variance (Devellis, 2003) and to successfully run the

item-analysis, reliability, correlation, and factor analysis procedures (Gable & Wolf, 1993). This

is in concert with the criteria that suggested having 6-10 times as many people as there are

statements on the instrument (Gable & Wolf, 1993) or at least five subjects per item (Nunnaly,

1978) as the minimum number of items that can be tolerated.









Once the scale was administered to a large and representative sample, the researcher tested

the performance of items to identify effective functioning. The analysis of the set of items at this

step was (a) factor analysis: to determine the number of latent variables underlying an item set

and explain the variation among the items (Devellis, 2003; Gable & Wolf, 1993), (b) item

analysis: to generate response frequencies, percentages, means, and standard deviations as well

as to identify items to delete from the instrument, and (c) reliability analysis: to indicate the

scale's quality.

A detailed dissemination of the results of these analyses is presented in Chapter 4. As a

summary, the factor analysis results supported a two-factor solution: teachers' practices of

scientific inquiry using technology and their perceptions of such use. In addition, results

provided support for construct validity of the instrument. The Cronbach's alpha reliability value

of .980 for the overall scale (.976 for perceptions factor and .974 for practices factor) indicated

high internal consistency reliability.

Statistical Techniques Used to Answer Study Research Questions

The researcher made use of a variety of statistical methods to answer the overarching and

the supporting questions. Before beginning to answer the research questions, the reliability and

item analysis and exploratory and confirmatory factor analyses were used to determine the

reliability and validity of the SIT-TIPPS instrument. This process was essential in answering the

research questions as all of the overarching and supporting questions depended on the quality of

the scale. The data obtained through teachers' self-reported responses to the components of the

SIT-TIPPS instrument were then analyzed using a variety of statistical methods to answer the

study research questions. The additional methods used were descriptive statistics (e.g.,

frequencies), correlations, multiple regressions, t-tests, and ANOVAs. Interpretations from all of

these methods contributed to the explanation of the first overarching question. For the second









overarching question, the correlations between the 25 items constituting the teachers' self-

reported perceptions factor and the 25 items constituting the teachers' self-reported practices

factor were calculated. Multiple regression method was also used to answer this question because

models indicated that these two factors were dependent on each other. To investigate the effect

of teacher demographics and teacher background/teacher professional development variables on

teachers' perceptions and practices for using technology to enact scientific inquiry, descriptive

statistics, multiple regression, correlations, t-tests, and ANOVAs were used. For example, in

order to explore the relationship between gender and teachers' classroom practices, the

researcher calculated the correlation between the two variables and a t-test was conducted. In

addition, gender was used as an exploratory variable in a multiple regression model where the

teacher practices factor was used a dependent variable. The last four supporting research

questions were represented in the SIT-TIPPS with certain number of items (see Appendix B).

Nine items were used to measure how well prepared teachers' felt to support students to engage

in certain inquiry skills and how often they use these inquiry skills in their classrooms. Twenty-

six items were employed to answer how well prepared teachers' felt to use certain technology

tools/applications and how often they used these tools in their classrooms. In order to answer

these four supporting questions, the researcher used frequency reports, correlations, and multiple

regression models.

According to the criteria set forth by Cohen (1988) for psychological research, a

correlation coefficient ranging between .10 and .30 was considered small; values between .30

and .50 were considered medium; and values between .50 and 1.0 were considered large. In this

study, this criterion was used as a guideline to interpret the correlation coefficients.









Item Analysis and Reliability

The Scientific Inquiry with Technology-Teacher Perceptions and Practices (SIT-TPPS)

Scale developed as part of this study demonstrated high internal consistency (see Table 3-1). The

overall reliability of the scale (including 50 items dealing with perceptions and practices) was

.980. Internal consistency reliabilities for its components were .976 (Teachers' Perceptions), .974

(Teachers' Practices), .924 (level of inquiry skills), .886 (frequency of use of inquiry skills), .932

(level of use of technology tools), and .915 (frequency of use of technology tools).

Table 3-2 reports item analysis results for each individual item including 50 items that

measures teachers' self-reported perceptions and practices of using technology to attain the goals

of scientific inquiry in science classrooms. The statistics show that no item should be deleted and

all of the items contributed well to the reliability of the scale.

Factor Analyses

Sample Size

Factor analytic research requires large samples (Guadagnoli & Velicer, 1988) and the

number of subjects needed to undertake a factor analysis of an instrument depends on the

number of items that are initially included in the instrument. There is, however, very little

agreement among the researchers regarding sample size in factor analysis (Pett, et al., 2003). In

deciding how many subjects to be used in this study, the researcher relied on a rule of thumb,

called the subjects-to-variables (STV) ratio (Bryant & Yarnold, 1995, p. 100). Bryant and

Yarnold (1995, p. 100) and Nunnally (1978) explained that for the results to be reliable and to

replicate if the analysis is repeated using an independent sample, the minimum number of

observations in one's sample should be at least five times the number of variables. Other

researchers suggested more conservative numbers (Gable & Wolf, 1993; Pett, Lackey, &

Sullivan, 2003, p. 148). Some researchers suggested rules in terms of the number of subjects









required. According to Comrey and Lee (1992, p.217), 300 subjects were accepted as "good" and

500 subjects as "very good." Tabachnick and Fidell (2001, p. 588), on the other hand,

recommended at least 300 cases for factor analysis. Based on these criteria, the researcher

administered the instrument resulting in data from 715 science teachers. Because the STV ratio is

715/50, or 14.3, the sample size was sufficiently large by the reliability criterion (Bryant &

Yarnold, 1995, p.100).

The researcher was also careful about the nature of the sample selected. Although selection

of the science teachers for the study was mainly based on convenience, this practice is not

deemed problematic (Fabrigar, Wegener, MacCallum, & Strahan, 1999) unless the sample is

overly homogeneous and its selection is related to measured variables (Fabrigar et al., 1999).

Such a practice was reported to result in low estimates of factor loadings and correlations among

factors (Comrey & Lee, 1992). To prevent this from happening, the researcher collected over

5,000 email addresses from various states, contacted over 40 national science teachers

associations and joined listservs in order to get a sample as large and representative as possible.

Exploratory Factor Analysis

This section describes the objective for selecting the preferred method of extraction and

rotation for factor analysis.

In this study, principal factor analysis (PFA) method was preferred to the commonly used

principal components analysis as suggested by Costelllo and Osborne (2005). PCA is only a data

reduction method (Costelllo & Osborne, 2005) and does not differentiate between common and

unique variance as factor analysis does (Fabrigar et al., 1999). It was suggested that when the

purpose of the study is to identify latent variables which are contributing to the common variance

in a set of measured variables, PFA is the preferred method of extraction (Fabrigar et al., 1999).

Fabrigar et al., (1999) suggested although principal components with varimax rotation and the









Kaiser criterion are the norm, they are not optimal, particularly when data do not meet

assumptions, as is often the case in the social sciences. Based on an analysis of the related

literature, Fabrigar et al., (1999) favored the use of a true factor analysis extraction method (they

preferred maximum likelihood), oblique rotation (such as direct oblimin), and use of scree plots

plus multiple test runs to determine the number of meaningful factors in a data set in order to get

optimal results (i.e., results that will generalize to other samples and that reflect the nature of the

population).

In this study, principal axis factoring method was preferred to maximum likelihood (ML),

as one of ML's assumptions is multivariate normality (Fabrigar et al., 1999). A preliminary

analysis of the data in this study did not indicate normally distributed data. Therefore, principal

factors methods were preferred as they did not require any distributional assumptions (Fabrigar

et al., 1999).

As for the method to identify how many factors to retain, the researcher used the Kaiser

criterion of eigenvalues of greater than 1.0 (Fabrigar et al., 1999; Pett et al., 2003) and parallel

analysis (Fabrigar et al., 1999).

As for the rotation method, oblique rotation method was preferred over orthogonal

solutions because the latent variables in the study demonstrated evidence of correlation with each

other (see Table 3-10 & Table 3-15). Oblique methods allow the factors to correlate (Costelllo &

Osborne, 2005) and produce a better estimate of the true factors and a better simple structure

than will an orthogonal rotation (Fabrigar et al., 1999). Promax rotation method was chosen to

achieve this objective.

In addition to using principal axis factoring with promax rotation, the researcher made use

of additional statistics provided in SPSS software to identify any severe multicollinearity (an









assumption of factor analysis) and to determine whether data is appropriate for undertaking an

exploratory factor analysis. Although mild multicollinearity is not a problem for factor analysis

(Field, 2005), the determinant, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy

and Barlett's test of sphericity were also used to understand whether the data were appropriate to

run exploratory factor analysis in the first place.

Table 3-5 presents the KMO measure of sampling adequacy and Barlett's test of

sphericity. The value for KMO was .975 and significance level for Barlett's test of sphericity

was .000 (<.001). It was suggested in the literature that with a KMO value over .5 and a

significant value for Barlett's test ascertain that it would be judicious to proceed with factor

analysis (Pett et al., 2003, p.78). All three statistics suggested that the sample size was sufficient

relative to the number of items in the scale and the correlations among the individual items were

strong enough to suggest that the correlation matrix was factorable (Pett et al., 2003, p.78).

After this step, the researcher ran parallel analysis to get a better sense of how many

factors to extract from the factor analysis. As shown in Table 3-3, the results suggested a 6-factor

model. However, a principal axis factoring method with promax rotation in SPSS extracted 5

factors. Because the scale developed in this study was based on a 5-category model (based on

literature review and National Science Education Standards, 2000), the researcher decided to go

with the 5-factor model. However, a preliminary evaluation of this model based on

communalities, total variance explained, pattern matrix, and structure matrix (Tables 3-6 through

3-9, respectively) did not produce interpretable factors considering the nature of the items in the

scale. Communalities of items ranged from .604 to .766, suggesting that a high percentage of

variance in a given item was explained by all five factors. Moreover, Table 3-7 illustrated that

the five factors extracted were able to explain 69.4% of the variance. As shown in pattern (Table









3-8) and structure matrices (Table 3-9), 19 items loaded on the first item and 22 items loaded on

the second item. Only 9 items loaded on two other factors (6 on factor 3; 3 on factor 4). None of

the items loaded on the fifth factor. Because the items were originally constructed based on five

essential features of scientific inquiry proposed by the National Science Education Standards

(2000), as illustrated in Table 3-4, the researcher concluded that a five factor solution was not

statistically supported by the data and that the five essential features of scientific inquiry outlined

in the National Science Education Standards was indeed a conceptual categorization rather than a

data-driven theoretical one.

Therefore the researcher ran a two-factor model using principal axis method with promax

rotation considering the fact that the items in the scale were constructed in terms of teacher

perceptions and practices (see Table 3-4 for the breakdown of items into these two categories).

That is, statements starting with "I integrate" constituted the "practices" items, whereas

statements starting with "A science teacher" constituted the "perceptions items.

A new factor analysis that was limited to a two-factor extraction (see Table 3-10 through

Table 3-15) was able to separate items measuring perceptions from items measuring practices.

The correlation between the two factors was .629 (Table 3-15) and this two-factor solution was

able to explain 61% of the total variation (Table 3-12). Communalities of individual items

ranged between .368 and .752 (Table 3-11). Although some of these communalities seemed

lower than the ones produced in the five-factor model, they were kept due to these items' high

values of interpretability and their contributions to a well-defined factor (either teacher

perceptions or teacher practices). A careful evaluation of values reported both in the pattern and

the structure matrices together demonstrated a perfect distribution of items into categories coded

as teacher perceptions and teacher practices are outlined in Table 3-4.









Hence, this study and the SIT-TIPPS instrument support 2 factors perceptions and

practices. During the instrument development process, the researcher found that although

National Research Council (1996) reports 5 psychological factors in the attainments of teaching

scientific inquiry in the National Science Education Standards, these five factors are not

statistical factors. Models showed that creating the SIT-TIPPS with two factors, perceptions and

practices, created a better model for measuring teachers' self-reported perceptions and practices

regarding the use of technology to enact scientific inquiry.

Multiple Regression

A multiple regression analysis was conducted to examine the degree of association

between various outcome variables and exploratory variables. Four regression models were

tested using the stepwise regression method with SPSS 13.0. For the first two models, the

outcome variables were the teachers' perceptions and practices regarding the use of technology

for scientific inquiry purposes. The exploratory variables for these models were the level of

preparedness for using inquiry skills; the frequency of using inquiry skills in instruction; the

level of preparedness for using technology tools; the frequency of using technology tools in

instruction; gender; race/ethnicity; years of teaching experience; number of grades taught; the

level of grades taught (high numbers meaning that a particular teacher taught higher grades

ranging from 6th grade to 12th grade and higher); number of science courses taught; number of

computers in class; the presence of a computer lab; number of computers in computer lab;

number of science labs; the presence of a science lab in classroom; and previous educational

technology training.

The other two regression equations tested smaller models in which two of the exploratory

variables in the previous two models (the frequency of using inquiry skills in instruction and the

frequency of using technology tools in instruction) served as outcome variables separately in one









of the two models. These models, then, investigated the influence of some of the exploratory

variables (included in the first two models) on both of these two outcome variables separately.

Overall, no multicollinearity problem was observed among the variables because the

variance inflation factor (VIF) values in all six models was less than 2.0, which indicated that

collinearity was not a problem (Miles & Shevlin, 2001).

The first regression model consisted of 16 exploratory variables and the outcome variable:

"Teachers' self-reported perceptions regarding the use of technology for scientific inquiry

purposes." Table 3-16 indicates the unstandardized regression coefficients (b), the standardized

regression coefficients (A), the observed t-values (t), and the p-values (p).

The second regression model consisted of 16 exploratory variables and the outcome

variable: "Teachers' practices regarding the use of technology for scientific inquiry purposes."

Table 3-17 reports the unstandardized regression coefficients (b), the standardized regression

coefficients (A), the observed t-values (t), and the p-values (p) for the model.

The third regression model consisted of 5 exploratory variables (the level of preparedness

for using inquiry skills, years of experience, total number of grades taught, the level of grades

taught, and the number of science courses taught) and the outcome variable: "Frequency of using

scientific inquiry skills in instruction." Table 3-18 reports the unstandardized regression

coefficients (b), the standardized regression coefficients (A), the observed t-values (t), and the p-

values (p) for this model.

The fourth regression model consisted of 8 exploratory variables (the level of preparedness

for using technology tools, years of experience, total number of grades taught, the level of grades

taught, and the number of science courses taught, the number of computers in classroom, the

number of computers in computer lab, and previous educational technology training) and the









outcome variable: "Frequency of using technology tools in instruction." Table 3-19 reports the

unstandardized regression coefficients (b), the standardized regression coefficients (/7), the

observed t-values (t), and the p-values (p) for this model.

Summary

Chapter 3 described the methods used to develop the SIT-TIPPS instrument as well as to

investigate the research questions of the study. The results of these analyses are described in

Chapter 4.









Table 3-1. Descriptive statistics and reliability index of the SIT-TIPPS instrument
Components N Minimum Maximum Mean Std. Dev. Reliability
Perceptions 588 25 125 106.1 15.0 .976
Practices 595 25 125 98.5 17.7 .974
Inquiry skills- 616 9 36 29.4 5.4 .924
Level
Inquiry skills- 606 9 45 32.5 5.8 .886
Frequency
Technology 562 26 104 72.1 15.3 .932
tools-Level
Technology 530 29 130 63.5 16.3 .915
tools-Frequency
Overall .980
(Perceptions &
Practices items)










Table 3-2. Item


Item Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41


analysis results

Scale Mean if
Item Deleted
200.82
200.39
200.48
200.68
200.87
200.70
200.76
200.43
200.94
200.93
200.85
200.62
200.39
200.86
200.91
200.39
201.37
201.18
201.01
200.66
200.52
201.16
200.62
200.86
201.21
200.70
200.65
200.75
200.45
200.66
201.01
200.62
200.68
200.67
200.70
200.66
200.94
200.73
200.66
200.63
200.75


Scale Variance
if Item Deleted
823.47
829.42
825.74
823.87
819.95
822.94
821.06
828.29
815.90
815.82
818.44
822.11
829.07
818.36
815.48
829.31
815.46
814.88
815.69
823.73
825.24
816.30
822.70
817.21
814.29
820.51
823.23
817.35
825.35
820.98
815.60
822.74
821.85
820.54
820.12
820.26
812.66
821.28
820.33
823.40
818.76


Corrected
Item-Total
Correlation
.627
.603
.637
.592
.639
.684
.684
.583
.718
.735
.744
.747
.637
.745
.710
.618
.648
.665
.733
.675
.719
.689
.727
.710
.699
.680
.642
.743
.688
.731
.757
.739
.645
.753
.756
.746
.798
.651
.753
.708
.659


Cronbach's
Alpha if Item
Deleted
.980
.980
.980
.980
.980
.980
.980
.980
.980
.979
.979
.979
.980
.979
.980
.980
.980
.980
.979
.980
.980
.980
.980
.980
.980
.980
.980
.979
.980
.980
.979
.980
.980
.979
.979
.979
.979
.980
.979
.980
.980










Table 3-2. Continued.
42
43
44
45
46
47
48
49
50


200.88
200.75
200.71
200.99
200.85
200.76
200.91
200.64
200.78


820.53
820.96
818.37
814.85
815.38
819.81
814.19
822.03
818.75


.679
.715
.753
.724
.752
.713
.777
.719
.726


.980
.980
.979
.980
.979
.980
.979
.980
.980


Table 3-3. Parallel analysis: PAF/common factor analysis & random normal data generation
(N=557, Nvariables=50)


Root


1.000000
2.000000
3.000000
4.000000
5.000000
6.000000
7.000000
8.000000
9.000000


Raw Data
25.372641
5.327737
1.960368
1.419856
.827231
.628507
.462105
.428912
.381882


Means
.731735
.662761
.614535
.576870
.537975
.506394
.475422
.444682
.415302


Percentile
.799033
.702616
.656174
.618403
.572217
.540146
.507302
.480694
.441793


Table 3-4. Breakdown of 50 items into categories and related factors
Category Perception factor Practice factor
Teacher engages students in 3, 21, 26,29,30 1, 4, 5, 28, 46
scientifically oriented questions


Teacher encourages students to
give priority to evidence

Teacher helps students
formulate explanations from
evidence to address
scientifically oriented questions

Teacher helps students connect
explanations to scientific
knowledge

Teacher encourages students to
communicate and justify their
proposed explanations


2, 8, 13, 16, 44


12, 20, 32, 35, 36,
40, 49



23, 34, 39, 50



6, 42, 43, 47


19, 27, 33, 38, 41


7, 10, 11, 15, 24,
37, 45



9, 14, 25, 48



17, 18, 22, 31









Table 3-5. KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy


Bartlett's Test of
Sphericity


Approx. Chi-Square 28979.8
31
df 1225
Sig. .000










Table 3-6. Communalities
Item number Initial
1 .651
2 .725
3 .772
4 .683
5 .671
6 .682
7 .702
8 .713
9 .750
10 .747
11 .719
12 .767
13 .720
14 .709
15 .706
16 .708
17 .667
18 .696
19 .747
20 .659
21 .751
22 .686
23 .732
24 .713
25 .677
26 .701
27 .684
28 .748
29 .715
30 .741
31 .783
32 .777
33 .772
34 .801
35 .805
36 .821
37 .806
38 .762
39 .818
40 .762
41 .764
42 .775
43 .821


Extraction
.604
.705
.775
.666
.615
.650
.696
.631
.708
.700
.649
.702
.665
.655
.620
.683
.647
.612
.673
.632
.742
.608
.660
.662
.625
.655
.636
.723
.684
.708
.720
.749
.760
.759
.763
.763
.766
.700
.756
.674
.716
.736
.749









Table 3-6. Continued.
44 .807 .753
45 .699 .644
46 .794 .740
47 .799 .751
48 .773 .747
49 .805 .750
50 .757 .713
Extraction method: Principal Axis Factoring.

Table 3-7. Total variance explained: PAF with promax rotation
Rotation
Sums of
Extraction Sums of Squared Squared
Factor Initial Eigenvalues Loadings Loadingsa
% of Cumulati % of Cumulati
Total Variance ve % Total Variance ve % Total
1 25.627 51.254 51.254 25.325 50.651 50.651 20.559
2 5.579 11.157 62.412 5.280 10.559 61.210 20.207
3 2.236 4.472 66.884 1.921 3.842 65.052 13.474
4 1.688 3.377 70.261 1.386 2.773 67.825 7.593
5 1.108 2.216 72.476 .786 1.571 69.397 .846
6 .894 1.789 74.265
7 .727 1.453 75.718
8 .685 1.369 77.088
9 .646 1.293 78.381
10 .571 1.143 79.523
Extraction method: Principal Axis Factoring. a When factors are correlated, sums of squared
loadings cannot be added to obtain a total variance.









Table 3-8. Pattern matrix
Factor
Item number 1 2 3 4 5
1 -.150 .392 .568 .027 -.028
2 .221 -.207 .713 .207 .085
3 .274 -.213 .768 .121 .063
4 -.162 .322 .698 -.084 .188
5 -.222 .505 .359 .279 -.080
6 .428 -.059 .451 .127 -.108
7 -.022 .380 .617 -.128 .087
8 .123 -.074 .446 .483 -.056
9 -.059 .593 .409 -.072 -.125
10 -.051 .625 .335 -.002 -.123
11 .031 .698 .117 .049 .009
12 .673 .033 .078 .197 -.045
13 .324 .033 .091 .556 .078
14 .037 .694 .074 .107 .093
15 .059 .743 .034 -.029 .040
16 .332 .005 .055 .607 .011
17 .112 .736 -.037 -.119 -.286
18 .151 .714 -.018 -.147 -.185
19 .061 .797 -.053 .044 -.049
20 .688 -.010 .028 .178 -.133
21 .596 .034 -.037 .437 -.015
22 .150 .751 -.086 -.071 -.089
23 .703 .038 .067 .108 .019
24 .084 .723 .058 -.077 .227
25 .180 .687 .061 -.198 -.062
26 .780 -.049 .064 .045 .068
27 -.037 .671 .135 -.024 .338
28 .117 .651 .132 -.047 .323
29 .667 -.022 .126 .107 .292
30 .635 .079 -.047 .330 -.066
31 .125 .828 -.074 -.042 .002
32 .835 .003 .054 -.013 .099
33 -.246 .824 -.092 .408 .057
34 .820 .019 .095 -.040 -.011
35 .844 .057 -.003 -.004 -.004
36 .836 .021 -.039 .106 .053
37 .145 .791 -.107 .114 .100
38 -.190 .788 -.069 .352 .074
39 .819 .024 -.006 .094 .016
40 .797 .034 .016 -.011 .059
41 -.180 .806 -.098 .361 .032
42 .839 .084 -.098 -.054 -.242









Table 3-8. Continued.
43 .865 .029 -.028 -.037 -.122
44 .824 .075 -.028 .031 -.058
45 .097 .755 -.087 .088 -.090
46 .040 .830 -.131 .169 .077
47 .901 .034 -.060 -.065 -.011
48 .181 .777 .000 -.121 .146
49 .842 .015 .018 -.036 .202
50 .820 .172 -.087 -.115 .104
Extraction method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization.
Rotation converged in 7 iterations.










Table 3-9. Structure matrix
Factor
Item number 1 2 3 4 5
1 .387 .632 .714 .282 -.053
2 .570 .376 .781 .463 .055
3 .618 .402 .835 .412 .021
4 .360 .591 .750 .204 .146
5 .361 .671 .612 .471 -.069
6 .691 .467 .699 .409 -.134
7 .474 .667 .768 .198 .043
8 .510 .398 .634 .647 -.045
9 .467 .762 .688 .223 -.150
10 .479 .780 .658 .282 -.137
11 .499 .796 .534 .328 .010
12 .810 .513 .530 .486 -.048
13 .601 .445 .463 .727 .113
14 .500 .791 .507 .380 .101
15 .476 .784 .464 .251 .039
16 .596 .418 .437 .754 .051
17 .460 .737 .407 .127 -.292
18 .485 .739 .421 .123 -.196
19 .491 .816 .437 .307 -.039
20 .768 .444 .462 .434 -.135
21 .761 .487 .450 .661 .011
22 .495 .763 .392 .197 -.088
23 .801 .499 .504 .411 .009
24 .481 .778 .464 .230 .219
25 .520 .755 .477 .110 -.082
26 .803 .433 .472 .352 .050
27 .391 .719 .457 .254 .333
28 .524 .775 .521 .281 .313
29 .758 .454 .495 .420 .278
30 .780 .512 .454 .576 -.050
31 .527 .842 .436 .252 .005
32 .859 .492 .500 .331 .076
33 .314 .771 .359 .559 .108
34 .867 .512 .539 .307 -.039
35 .872 .521 .486 .334 -.023
36 .865 .497 .458 .422 .045
37 .566 .851 .440 .401 .117
38 .340 .760 .373 .521 .117
39 .864 .505 .483 .412 .006
40 .818 .480 .461 .313 .039
41 .350 .771 .366 .527 .077
42 .817 .475 .401 .240 -.260









Table 3-9. Continued.
43 .855 .480 .454 .282 -.144
44 .863 .525 .475 .354 -.073
45 .503 .788 .416 .335 -.075
46 .491 .836 .399 .420 .103
47 .863 .478 .428 .268 -.033
48 .562 .838 .479 .214 .136
49 .842 .480 .461 .312 .179
50 .822 .541 .410 .233 .082
Extraction method: Principal Axis Factoring.
Rotation method: Promax with Kaiser Normalization.

Table 3-10. Factor correlation matrix
Factor 1 2 3 4 5
1 1.000 .554 .544 .380 -.023
2 .554 1.000 .552 .327 .006
3 .544 .552 1.000 .327 -.058
4 .380 .327 .327 1.000 .085
5 -.023 .006 -.058 .085 1.000
Extraction method: Principal Axis Factoring.
Rotation method: Promax with Kaiser Normalization.










Table 3-11. Communalities
Item number Initial
1 .651
2 .725
3 .772
4 .683
5 .671
6 .682
7 .702
8 .713
9 .750
10 .747
11 .719
12 .767
13 .720
14 .709
15 .706
16 .708
17 .667
18 .696
19 .747
20 .659
21 .751
22 .686
23 .732
24 .713
25 .677
26 .701
27 .684
28 .748
29 .715
30 .741
31 .783
32 .777
33 .772
34 .801
35 .805
36 .821
37 .806
38 .762
39 .818
40 .762
41 .764
42 .775
43 .821


Extraction
.456
.412
.464
.412
.504
.541
.511
.368
.608
.638
.652
.694
.456
.651
.612
.441
.494
.508
.651
.609
.639
.551
.663
.607
.550
.651
.537
.628
.600
.653
.684
.730
.621
.741
.746
.752
.726
.600
.752
.659
.611
.620
.701









Table 3-11. Continued.
44
45
46
47
48
49
50
Extraction method: Principal


.807 .734
.699 .608
.794 .691
.799 .707
.773 .692
.805 .692
.757 .638
Axis Factoring.


Table 3-12. Total variance explained
Rotation
Sums of
Extraction Sums of Squared Squared
Factor Initial Eigenvalues Loadings Loadingsa


Total
25.627
5.579
2.236
1.688
1.108
.894
.727
.685
.646


% of
Variance Cumu
51.254
11.157
4.472
3.377
2.216
1.789
1.453
1.369
1.293


% of


lative %
51.254
62.412
66.884
70.261
72.476
74.265
75.718
77.088
78.381


Total
25.250
5.213


Variance
50.500
10.425


Cumulative %
50.500
60.925


Total
21.826
21.234


Extraction method: Principal Axis Factoring. a When factors are correlated, sums of squared
loadings cannot be added to obtain a total variance.










Table 3-13. Pattern matrix


Item number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42


Factor a
1
.047
.563
.603
.035
-.008
.661
.131
.478
.036
.040
.051
.805
.572
.061
.015
.591
.033
.064
.017
.800
.768
.059
.793
.024
.096
.849
-.051
.099
.759
.766
.038
.873
-.168
.867
.868
.890
.105
-.126
.882
.821
-.121
.810


2
.644
.116
.115
.619
.715
.111
.625
.178
.756
.773
.774
.045
.148
.767
.772
.109
.681
.671
.796
-.031
.049
.704
.032
.764
.678
-.070
.764
.727
.024
.064
.803
-.030
.883
-.009
-.007
-.037
.782
.848
-.023
-.015
.852
-.038









Table 3-13. Continued.
43 .873 -.059
44 .854 .004
45 .065 .738
46 .010 .825
47 .881 -.066
48 .088 .774
49 .849 -.027
50 .751 .072
Extraction method: Principal Axis Factoring.
Rotation method: Promax with Kaiser Normalization. a Rotation converged in 3 iterations.










Table 3-14. Structure matrix
Factor
Item number 1 2
1 .453 .674
2 .636 .470
3 .675 .494
4 .424 .641
5 .442 .710
6 .731 .527
7 .524 .708
8 .590 .479
9 .512 .779
10 .526 .798
11 .538 .807
12 .833 .551
13 .665 .508
14 .544 .806
15 .501 .782
16 .659 .480
17 .462 .702
18 .486 .711
19 .518 .807
20 .780 .472
21 .799 .532
22 .501 .741
23 .814 .531
24 .505 .779
25 .522 .738
26 .805 .464
27 .429 .731
28 .556 .789
29 .774 .501
30 .807 .546
31 .543 .826
32 .854 .519
33 .388 .777
34 .861 .536
35 .864 .540
36 .867 .523
37 .597 .848
38 .407 .768
39 .867 .532
40 .812 .501
41 .415 .776
42 .787 .472









Table 3-14. Continued.
43 .836 .490
44 .856 .541
45 .529 .778
46 .529 .831
47 .839 .488
48 .575 .829
49 .831 .507
50 .796 .545
Extraction method: Principal Axis Factoring.
Rotation method: Promax with Kaiser Normalization.

Table 3-15. Factor correlation matrix


Factor


1.000
.629


2
.629
1.000


Extraction method: Principal Axis Factoring.
Rotation method: Promax with Kaiser Normalization


Table 3-16. Regression analysis summary for teachers' perceptions factor
Variable b p t-values p-values
Constant 73.43 14.57 .000*
Inquiry skills-frequency .619 .232 4.15 .000*
Technology tools-level .181 .180 3.22 .001*
Note. R2 = .119 (n = 514, p = .000)
*p< .05.

Table 3-17. Regression analysis summary for teachers' practices factor
Variable b p t-values p-values
Constant 38.97 7.86 .000*
Inquiry skills-frequency 1.202 .401 7.99 .000*
Technology tools-level .171 .151 2.58 .010*
Technology tools-frequency .155 .150 2.54 .012*
Note. R = .331 (n = 462, p = .000)
*p< .05.









Table 3-18. Regression analysis summary for frequency of using inquiry skills
Variable b p t-values p-values
Constant 16.70 14.25 .000*
Inauirv skills-level .532 .502 13.62 .000*


Note. R2 =.252 (n = 553, p =.000)
*p< .05.


Table 3-19. Regression analysis summary for frequency of using technology tools
Variable b p t-values p-values
Constant 1.09 .339 .735
Technology tools-level .681 .630 17.50 .000*
Years of experience .261 .153 4.28 .000*
Previous ed tech training 2.33 .151 4.21 .000*
Number of computers in classroom .232 .104 2.99 .003*
Number of computers in computer lab .117 .103 2.95 .003*


Note. R =.499 (n = 428, p =.000)
*p< .05.









CHAPTER 4
PRESENTATION AND ANALYSIS OF DATA

This study developed an instrument, the SIT-TIPPS, that examined middle and high

school science teachers' self-reported perceptions and practices of using technology for

scientific inquiry purposes in their classrooms. After a review of the relevant literature,

specific variables were identified to construct the survey and included in the analysis to

answer the research questions. These variables included teachers' self-reported

perceptions and practices of using technology to attain the goals of scientific inquiry,

level and frequency of integrating certain inquiry skills and technology tools in science

classrooms, gender, race/ethnicity, years of teaching experience, grades taught, science

courses taught, access to computers, computer labs, and science labs, and time period in

which educational technology courses had been taken. This chapter will present the

analysis of data used to answer the study research questions.

Study Research Questions

Overarching Questions

1. How are teachers using technology to implement the goals of scientific inquiry in
their classrooms?

2. What are the relationships between teachers' self-reported perceptions and practices
regarding the use of technology to attain the goals of scientific inquiry?

Supporting Questions

What are the relationships between teachers' self-reported perceptions and practices
regarding the use of technology to attain the goals of scientific inquiry in terms of:

* Teacher demographics and teacher background/professional development variables?

* How often do teachers support students to engage in certain inquiry skills in their
science classrooms?

* How often do teachers use certain technology tools in their science classrooms?









* How prepared do teachers feel to support students to engage in certain inquiry skills
in their science classrooms?

* How prepared do teachers feel to use certain technology tools in their science
classrooms?

Demographic Reporting of the Sample

In order to provide a thorough description of the middle and high school teachers

and the context in which they teach, the demographic responses for study data will be

presented.

Demographic Characteristics

As noted in chapter 3, a total of 715 science teachers' self-reported responses

comprised the data for this study. Of the 598 respondents (83.6%) who reported their

gender, 207 (34.6%) were males and 391 (65.4%) were females (Table 4-1). Of the 590

participants (82.5%) who reported their racial/ethnic identity, 479 (81.2%) were White,

34 (5.8%) were Black, 27 (4.6%) were Multiracial, 26 (4.4%) were Hispanic, 20 (3.4%)

were Asian, and 4 (.7%) were American Indian (Table 4-2).

Virginia and Florida were the two states with the highest number of participants

(Table 4-3). One hundred and sixty five (27.5%) participants were from Virginia and 156

were from Florida (26%). The third highest participation was from Kansas with 50

participants (8.3%).

Teaching Experience

The survey included open-ended statements to collect pertinent information from

science teachers regarding their years of science teaching experience (Table 4-4), grade

levels (Table 4-5) and science courses (Table 4-6) taught, and certification areas. Because

the range of responses science teachers provided on their certification areas was very

wide, it is not reported by category. Therefore, the researcher made the assumption the









information provided about the science courses taught was an indication of areas in

which they were certified.

The number of years served (Table 4-4) as a science teacher ranged from 0 to 44

years with a mean of 13.5 years (n=583). Of the 583 teachers who responded, 244

(41.9%) fell in the 1 to 9-year range; 187 (32.1%) fell in the 10 to 19-year range; 99

(17.0%) fell in the 20 to 29-year range; 49 (8.4%) fell in the 30 to 39-year range; and 4

(.7%) fell in the 40 to 44-year range.

Results indicated that 171 teachers (23.9%) taught grade 6; 238 (33.3%) taught

grade 7; 252 (35.2%) taught grade 8; 345 (48.3%) taught grade 9; 360 (50.3%) taught

grade 10; 378 (52.9%) taught grade 11; 371 (51.9%) taught grade 12; and 130 (18.2%)

taught grades over 12 (Table 4-5). Seventy-one teachers (9.9%) taught only one grade

level; 82 (11.5%) taught two grade levels; 124 (17.3%) taught three grade levels; 149

(20.8%) taught four grade levels; 79 (11.0%) taught five grade levels; 39 (5.5%) taught

six grade levels; 35 (4.9%) taught seven grade levels; and 21 (2.9%) taught eight grade

levels (Table 4-5).

On the survey, when asked about what science courses they taught, science teachers

reported a variety of course names which the researcher then categorized these responses

into five categories: Life sciences, earth sciences, physical/general sciences, physics, and

chemistry. The results showed that 364 teachers (50.9%) taught life science; 283 (39.6%)

taught earth science; 369 (51.6%) taught physical/general sciences; 142 (19.9%) taught

physics; and 194 (27.1%) taught chemistry (Table 4-6). One hundred and sixty seven

(23.4%) teachers taught only one course; 195 (27.3%) taught two courses; 161 (22.5%)









taught three courses; 58 (8.1%) taught four courses; and 16 (2.2%) taught 5 courses

(Table 4-6).

Computer Access and Knowledge

Computer Access

The demographics/teacher background section of the SIT-TIPPS instrument also

asked for information about whether science teachers had computers in their classrooms

and the number of computers, whether they had access to computer labs and the number

of computers in these labs, the number of science labs at their schools, whether they had a

science lab in their classrooms, and whether they had anyone available at their schools to

provide technology support.

The results indicated that 592 out of 599 teachers (98.8%) had computers in their

classrooms. The number of computers in their classrooms ranged from 1 to 37 with a

mean of 6.12 and a standard deviation of 7.63. Many teachers (36.l1%) who specified the

number of computers in their classrooms (n = 590) had 1 computer in their classrooms.

The cumulative percentage calculations indicated that 80.3% of the teachers had 1 to 9

computers in their classrooms (see Table 4-12).

When asked if teachers had access to computer labs, 550 out of 590 teachers

(93.2%) indicated that they had access to computer labs. Responses from those who

specified the number of computers in computer labs (n = 544) showed that the number of

computers in computer labs ranged from 2 to 200 (including all computer labs at their

schools) with a mean of 28.22 and a standard deviation of 12.91.

Science teachers were also asked to report the number of science labs to which they

have access. According to the results, science teachers had access to an average of 2.44

science labs with a standard deviation of 3.72. Of the 514 teachers who responded, 62.8%









reported having access to 1 science lab; 12.5% had 2 labs; 9.7% had 3 labs; and 4.9% had

4 labs. This shows that cumulatively 89.9% of science teachers had 1 to 4 science labs at

their schools. The researcher also asked if the participants had a science lab in their

classrooms. Of the 594 teachers who responded, 72.7% indicated that they had a science

lab within their classrooms.

When asked about if they had anyone available at their school to provide them with

technology support, 93.9% of the teachers reported having someone available to them for

receiving technology support when needed. This indicates that schools are providing

teachers with at least some limited technical support on a daily basis.

Source of Computer Knowledge

The researcher asked science teachers to indicate whether they took educational

technology classes in high school, undergraduate school, graduate school, or in-service or

continuing education courses.

As shown in Table 4-13, the majority of the science teachers (92.3%) reported

receiving educational technology classes during in-service training or continuing

education courses. More teachers reported taking educational technology classes in

graduate school (54.4%) than receiving it in undergraduate school (47.3%). Only 28.3%

of the science teachers took educational technology classes in high school. Because

teachers were able to select more than one option to report at what stage in their career

they took educational technology courses, the researcher attempted a new calculation to

see in how many of these levels (high school, undergraduate school, graduate school, and

in-service or continuing education) a particular science teacher reported taking

educational technology classes. Results indicated that 187 teachers (26.2%) took









educational technology classes at only one of these levels; 204 (28.5%) at two levels; 122

(17.1%) at three levels; and 66 (9.2%) at all of the four levels.

Answering Research Question 1

This research question was answered by exploring supporting questions 2 and 3.

Regarding the statements dealing with teachers' frequency of use of the inquiry skills and

technology tools in instruction, teachers rated their use based on a 5-point Likert scale in

this order: Never; rarely (e.g., a few times a year); sometimes (e.g., once or twice a

month); often (e.g., once or twice a week); and almost or all science lessons.

The results on the frequency of use of these inquiry skills (Table 4-7) indicated that

supporting students to explain cause-effect relationships (M = 4.0, 28.2%) and supporting

students to discuss scientific explanations/ideas/models with others (M = 3.88, 28.2%)

were the two highest rated skills by the teachers. Approximately twenty-eight percent of

the science teachers reported that they integrate these skills in most or all science lessons.

Supporting students to conduct experiments (M = 3.70, 11.5%) and to collect, organize,

and analyze data (M = 3.78, 12.6%) were also among the highest rated skills. Over 12.5%

of the teachers reported integrating them in almost or all science lessons.

The results showed the inquiry skills with the lowest mean scores were: to support

students to identify their own misconceptions of science content (M = 3.10), to find

biases and flaws in their scientific explanations (M = 3.10), to test scientific explanations

against current scientific knowledge (M = 3.12), and to critique experiments (M = 3.17).

About 21% of teachers reported that they felt either not adequately prepared or somewhat

prepared to implement these skills in their instruction. Regarding the frequency with

which they integrated these skills in their lessons, the results were almost parallel. Having

the options "never", "rarely" (e.g., a few times a year), and "sometimes" (e.g., once or









twice a month) treated as "low integration" of inquiry skills in instruction, critiquing

experiments was the least integrated skill (M = 3.20). Of the 624 teachers who rated this

skill, 59.2% selected the never, rarely, and sometimes options. The other two lowest rated

skills in this category were testing scientific explanations against current scientific

knowledge (M = 3.31) and finding biases or flaws in scientific explanations (M = 3.39)

with 56.5% selecting never, rarely, and sometimes options for the former and 53.5%

selecting the same options for the latter.

When asked about how often they used technology tools (Table 4-8). Teachers'

responses showed that word processing (M = 4.16), which was the tool they felt more

prepared to use, was again the most used technology tool in lessons. About 47% of the

teachers reported they used word processing in most or all science lessons. The other

highly rated technologies from high to low were presentation devices (M = 4.12, 45.6%);

presentation software (M = 3.86, 38.1%); Internet searches (M = 3.67, 25.4%); and email

(M = 3.52, 34.7%) (Table 4-8).

The least frequently used technologies by science teachers were from lowest to

highest were videoconferencing, teleconferencing (M = 1.29); portable Global

Positioning Systems (M = 1.44); blogs (M = 1.45); data collection, telecollaborative

activities (M = 1.50); video editing (M = 1.54); wikis (M = 1.56); podcasts, videocasts

(M = 1.63); wireless communication devices (M = 1.69); image/picture editing (M =

2.00); and virtual experiences (M = 2.16). When "never" and "rarely" (e.g, a few times a

year) options were considered a "non-use", 93.1% of teachers reported almost never

using videoconferencing, teleconferencing in their instruction. The percentages were

89.7% for portable Global Positioning Systems; 88.1% for blogs; 87.5% for data









collection, telecollaborative activities; 86.7% for video editing; 84.7% for wikis; 82.6%

for podcasts, videocasts; 80.4% for wireless communication devices; 69.7% for

image/picture editing; and 65.9% for virtual experiences.

Answering Research Question 2

As mentioned in the methodology section, frequencies, correlations, multiple

regression models, t-tests, and ANOVAs were used to determine the relationship between

teachers' perceptions and practices of using technology tools/applications for scientific

inquiry purposes. This research question used teachers' self-reported responses from

supporting questions 4 and 5 along with data from research questions 1.

Level of Use of Inquiry Skills and Technology Tools

Regarding the statements dealing with teachers' level of preparedness, science

teachers rated their level of inquiry skills and technology tools based on a 4-point Likert

scale ranging from not adequately prepared to very well prepared (i.e., "1", not

adequately prepared; "2", somewhat prepared; "3", fairly well prepared; and "4", very

well prepared).

When asked about how well prepared they felt about supporting students to achieve

certain inquiry tasks, science teachers felt "very well prepared" in supporting students to

collect, organize, and analyze data (M = 3.51, 58.7%), explain cause-effect relationships

(M = 3.42, 52.1%), and conduct experiments (M = 3.41, 53.4%). The rest of the inquiry

skills produced lower mean scores and percentages (see Table 4-9).

Regarding science teachers self-reported level of preparedness in using certain

technology tools, the results of the study indicated that (Table 4-10) science teachers felt

more prepared to use (from highest to lowest) word processing (M = 3.85); e-mail (M =

3.80); Internet searches (M = 3.70); presentation software (M = 3.69); spreadsheets (M =









3.56); and presentation devices (M = 3.55). This equated to 87% of the teachers feeling

very well prepared to use word processing in their classrooms; 84.4% to use email;

75.6% to use Internet searches; 68.1% to use spreadsheet; and 67.5% to use presentation

devices.

On the other hand, teachers felt less prepared for using certain technologies in their

classrooms. Using the options "not adequately prepared" and "somewhat prepared"

considered together, teachers rated (from lowest to highest) low on data collection

telecollaborative activities (M = 1.85, 75.6%); videoconferencing, teleconferencing (M =

1.86, 74.5%); portable Global Positioning Systems (M = 2.00, 69.7); video editing (M =

2.00, 69.3%); podcasts, videocasts (M = 2.10, 65.6%); blogs (M = 2.13, 64.8%); wikis

(M = 2.13, 63.3%); wireless communication devices (M = 2.16, 63.3%); Smart

Board/Promethean interactive boards (M = 2.34, 56.2%); and webpage design (M = 2.37,

56.4%).

Correlational Analysis

Correlations were computed among the teachers' self-reported perceptions and

practices, teachers' level of preparedness in using certain scientific inquiry skills and

technology in their classrooms, the frequency of use of these inquiry skills and

technology tools during instruction, number of years of experience teaching science, total

number of grades taught, the level of grades taught (high numbers meaning that a

particular teacher taught higher grades ranging from 6th grade to 12th grade and higher),

the number of computers in classroom and computer labs, and the total number of

educational opportunities (high school, undergraduate school, graduate school, and/or in-

service training or continuing education) in which they received educational technology

training. Table 4-11 reports the Pearson Product-Moment Correlation coefficients and









level of significance among the aforementioned variables. Some of the correlations will

not be discussed in the chapter because they do not offer a practical interpretation. Only

the correlations that appear in bold will be reported and interpreted throughout this text.

Teachers' self-reported perceptions regarding the use of technology for scientific

inquiry purposes in science classrooms were positively correlated with: Teacher's

practices of using technology for scientific inquiry purposes in their own classrooms (r=

.649, p<.001); the level of preparedness for using certain inquiry skills (r= .225, p<.001);

the frequency of using certain inquiry skills (r= .267,p<.001); the level of preparedness

for using certain technology tools in instruction (r= .233, p<.001); the frequency of using

certain technology tools in instruction (r= .189, p<.00 1); and the total number of

educational levels in which they received educational technology training (r= .136, p=

.001).

Teacher's practices of using technology for scientific inquiry purposes in their own

classrooms were positively correlated with: Teachers' self-reported perceptions regarding

the use of technology for scientific inquiry purposes (r= .649,p<.001); the level of

preparedness for using certain inquiry skills (r= .283, p<.001); the frequency of using

certain inquiry skills (r= .456, p<.001); the level of preparedness for using certain

technology tools in instruction (r= .384,p<.001); the frequency of using certain

technology tools in instruction (r= .443, p<.001); the number of computers in class (r=

.109, p= .010); the number of computers in computer lab (r= .136, p= .002); and the total

number of educational levels in which they received educational technology training (r=

.157, p= .001).









The level of preparedness for using certain inquiry skills was significantly

correlated with: Teachers' self-reported perceptions regarding the use of technology for

scientific inquiry purposes (r= .225, p<.001); teachers' practices regarding the use of

technology for scientific inquiry purposes (r= .283,p<.001); the frequency of using

certain inquiry skills (r= .522, p<.00); the level of preparedness for using certain

technology tools in instruction (r= .448,p<.001); the frequency of using certain

technology tools in instruction (r= .275, p<.001); total number of grades taught (r= .151,

p<.001); and the level of grades taught (r= .144, p<.001).

The frequency of using certain inquiry skills variable was positively correlated

with: Teachers' self-reported perceptions regarding the use of technology for scientific

inquiry purposes (r= .267, p<.001); teachers' practices regarding the use of technology

for scientific inquiry purposes (r= .456, p<.001); the level of preparedness for using

certain inquiry skills (r= .522, p<.00); the level of preparedness for using certain

technology tools in instruction (r= .423,p<.001); the frequency of using certain

technology tools in instruction (r= .450, p<.001); and the number of computers in

computer lab (r= .088, p= .043).

The level of preparedness for using certain technology tools in instruction variable

was positively correlated with: Teachers' self-reported perceptions regarding the use of

technology for scientific inquiry purposes (r= .233, p<.001); teachers' practices regarding

the use of technology for scientific inquiry purposes (r= .384,p<.05); the level of

preparedness for using certain inquiry skills (r= .448, p<.001); the frequency of using

certain inquiry skills (r= .423, p<.001); the frequency of using certain technology tools in

instruction (r= .671, p<.001); total number of grades taught (r= 118, p= .005); the level









of grades taught (r= .121, p= .004); and the total number of educational levels in which

they received educational technology training (r= .249, p= .001). It was, however,

negatively correlated with the number of years of teaching experience (r= -.177, p<.001).

The frequency of using certain technology tools in instruction variable was

positively correlated with: Teachers' self-reported perceptions regarding the use of

technology for scientific inquiry purposes (r= .189,p<.001); teachers' practices regarding

the use of technology for scientific inquiry purposes (r= .443, p<.05); the level of

preparedness for using certain inquiry skills (r= .275, p<.001); the frequency of using

certain inquiry skills (r= .450, p<.001); the level of preparedness for using certain

technology tools in instruction (r= .671, p<.001); the total number of grades taught (r=

.112, p= .010); the level of grades taught (r= .120, p= .006); the number of computers in

class (r= .136, p= .002); the number of computers in computer lab (r= .180, p<.001); and

the total number of educational levels in which they received educational technology

training (r= .262, p<.001).

The number of years of teaching experience was positively correlated with the total

number of grades taught (r= .226, p<.001), but negatively correlated with the level of

preparedness for using certain technology tools in instruction (r= -. 177, p<.001) and the

total number of educational levels in which they received educational technology training

(r= -.193, p<.001).

Finally, the level of grades taught was significantly correlated with: The level of

preparedness for using certain inquiry skills (r= .144, p<.001); the level of preparedness

for using certain technology tools in instruction (r= .121, p=.004); the frequency of using









certain technology tools in instruction (r= .120, p=.006); the total number of educational

levels in which they received educational technology training (r= .321, p<.001).

Multiple Regression

Four regression models were tested using the stepwise regression method in order

to examine the degree of association between various outcome variables and exploratory

variables. Results provided additional insight into understanding the relationships

between demographic variables, teachers' comfort levels in using scientific inquiry and

technologies, and their level of integration of these skills and technologies.

Results from the first model showed that R2 of .119 was statistically significant, F

(2,333) = 22.442, p<.001. This model indicated that two exploratory variables (frequency

of using inquiry skills and level of preparedness for using technology tools) were jointly

associated with about 12% of the variance in teachers' self-reported perceptions

regarding the use of technology for inquiry purposes. No other variable was significant at

the p<.05 level. Although the influence of these two exploratory variables on the outcome

variable was small (12%), this observation indicated that as science teachers' frequency

of using inquiry skills in instruction and the level of their preparedness for using

technology tools in instruction get higher, it is likely that their perceptions regarding the

use of technology for scientific inquiry purposes get higher as well.

The second model showed that R2 of .331 was statistically significant, F (3,330) =

54.437, p<.001. This model indicated that three exploratory variables (frequency of using

inquiry skills, level of preparedness for using technology tools, and frequency of using

technology tools in instruction) were jointly associated with about 33% of the variance in

teachers' practices regarding the use of technology for inquiry purposes in their own

classrooms. No other variable was significant at the p<.05 level. This result showed that









as science teachers' frequency of using inquiry skills in instruction, the level of their

preparedness for using technology tools in instruction, and the frequency of using

technology tools in their classrooms get higher, it is likely that their practices regarding

the use of technology for scientific inquiry purposes get higher as well.

The third model showed that R2 of .252 was statistically significant, F (1,551) =

185.63, p<.001. This model indicated that only one exploratory variable (level of

preparedness for using inquiry skills in instruction) was associated with about 25% of the

variance in teachers' frequency of using scientific inquiry skills in their classrooms. No

other variable was significant at the p<.05 level. This result showed that as science

teachers' level of preparedness for using inquiry get higher, it is likely that their

frequency of using certain scientific inquiry skills in instruction get higher as well.

The fourth model showed that R2 of .499 was statistically significant, F (5,422) =

84.07, p<.001. This model indicated that 5 exploratory variables (level of preparedness

for using inquiry skills in instruction, years of experience, previous educational

technology training, the number of computers in classroom, and the number of computers

in computer lab) were associated with about 50% of the variance in teachers' frequency

of using technology tools in their classrooms. No other variable was significant at the

p<.05 level. This result showed that as science teachers' level of preparedness for using

technology tools in classroom, their teaching experiences, the level of educational

technology training they received at various stages during their career (high school,

undergraduate school, graduate school, and in-service training or continuing education),

the number of computers in their classrooms, and the number of computers in computer









labs they have access to get higher, it is likely that their frequency of using technology

tools in instruction get higher as well.

It can be concluded that the results from the multiple regression analysis models

indicate a certain degree of association between some of the demographic variables,

teachers' self-reported perceptions and practices regarding the use of scientific inquiry

using technology, and their comfort levels with and uses of inquiry skills and

technologies. The results obtained from the four models contribute to answering the

second research question that addresses the relationship between teachers' perceptions

and practices as well as the supporting questions of the study.

Further Analyses

Some of the variables used in the study enabled the researcher to test for significant

differences between group means. The researcher conducted T-tests and ANOVAs to

study the differences between the subsets of certain variables. Using the subsets of

gender, presence of science lab in classroom, and presence of computer lab in school,

significant differences were tested using a T-test with respect to teachers' self-reported

perceptions and practices regarding the use of technology for scientific inquiry purposes;

teacher's level of preparedness for using inquiry skills and technology tools in

instruction; and teachers' frequency of using inquiry skills and technology tools in their

classrooms. An alpha level of .05 was used for all statistical tests. For the ANOVA tests,

a different set of variables (teachers' years of teaching experiences in a categorized

format, the number of different types of science lessons taught, race/ethnicity, and state)

were tested for significant group differences with respect to the six variables previously

mentioned.









Due to large volume of subsets of the data used in T-test and ANOVA statistics,

only significant results were reported in Tables 4-14, 4-15, 4-16, and 4-17. Cohen's dfor

significant t-test results and partial eta-squared (r2) for significant ANOVA results were

also computed to report effect sizes.

The independent-samples t-test analysis indicated that there was not any differences

between males (n=207) and females (n=391) in terms of teachers' self-reported

perceptions (t--.363, df=558, p=.717, two-tailed) and practices (t--.083, df=560, p=.934,

two-tailed) regarding the use of technology for scientific inquiry purposes; teacher's level

of preparedness for using inquiry skills (t--.089, df=584, p=.373, two-tailed) and

technology tools in instruction (t=.146, df=577, p=.884, two-tailed); and teachers'

frequency of using inquiry skills (t=-.200, df=543, p=.845, two-tailed) and technology

tools in their classrooms (t=.161, df=518, p=.872, two-tailed).

To test whether there were significant differences in terms of having (or not) a

science lab in the classroom, four variables were studied: Teachers' self-reported

perceptions and practices regarding the use of technology for scientific inquiry purposes;

teacher's level of preparedness for using technology tools in instruction; and teachers'

frequency of using technology tools in their classrooms.

Results showed that teachers' self-reported perceptions regarding the use of

technology for scientific inquiry purposes did not differ significantly based on whether

there was a science lab in their classrooms (t=-1.520, df=554, p=.129, two-tailed). This

means that teachers who have a science lab in their classrooms did not differ significantly

from those who do not on their perceptions regarding the use of technology for scientific

inquiry purposes in instruction. They did, however, have significantly higher means with









regard to teachers' practices factor (t-2.250, df=556, p=.025, two-tailed, Cohen's d=.22);

teacher's level of preparedness for using technology tools in instruction (t-2.003, df=541,

p=.046, two-tailed, Cohen's d=.19); and teachers' frequency of using technology tools in

their classrooms (t=2.744, df=517, p=.006, two-tailed, Cohen's d=.27).

Teachers who had a computer lab in their schools scored significantly higher than

those who did not on teacher's level of preparedness for using technology tools in

instruction (t-2.749, df=537, p=.006, two-tailed, Cohen's d=.39) and teachers' frequency

of using technology tools in their classrooms (t-2.103, df=513, p=.036, two-tailed,

Cohen's d=.47). Both groups of science teachers did not differ significantly on teachers'

perceptions (t=.140, df=552, p=.888, two-tailed) and practices (t=1.342, df=554, p=.180,

two-tailed) factors. All of the Cohen's d effect sizes reported were less than .50, which

indicated small effect sizes for significant t-test results, based on criteria suggested by

Cohen (1988, p.25).

The effect of race/ethnicity was not statistically significant for teachers' self-

reported perceptions regarding the use of technology for scientific inquiry purposes, F(5,

547)=1.468, p=.198; teacher's level of preparedness for using technology tools in

instruction, F(5, 533)=1.945, p=.085; teachers' frequency of using inquiry skills, F(5,

565)=1.264, p=.278; and teachers' frequency of using technology tools in their

classrooms, F(5, 508)=1.745, p=.123. However, with an alpha level .05, the effect of

race/ethnicity was statistically significant for teachers' practices regarding the use of

technology for scientific inquiry purposes, F(5, 548)=2.653,p=.022, r2=.024, and

teacher's level of preparedness for using inquiry skills in instruction, F(5, 572)=3.016,

p=.011, r2=.026.









A post hoc Tukey test was conducted to determine which race/ethnicity categories

indicated significant differences. Results indicated that although the overall F value for

teachers' practices factor was significant at the .05 level, none of the race/ethnicity

categories showed statistical difference among each other. For teacher's level of

preparedness for using inquiry skills, however, results demonstrated that Hispanic

(Mean=32.31, SD=3.93) teachers had significantly higher scores than their Asian

counterparts (Mean=26.95, SD=6.96),p=.009, SE=1.57.

The effect of the number of different types of science lessons taught was not

statistically significant for teachers' self-reported perceptions, F(5, 582)=1.600, p=.158,

and practices, F(5, 589)=2.145, p=.059, regarding the use of technology for scientific

inquiry purposes; teacher's level of preparedness for using inquiry skills in instruction,

F(5, 610)=1.125, p=.346; teachers' frequency of using inquiry skills, F(5, 600)=1.244,

p=.287; teacher's level of preparedness for using technology tools, F(5, 556)=1.745,

p=.123; and teachers' frequency of using technology tools in their classrooms, F(5,

524)=.818,p=.537.

In order to investigate if teachers from different states had significant differences

with each other in terms of variables discussed above, the researcher selected only six

states (Connecticut, Florida, Kansas, Michigan, Virginia, Wisconsin) from which more

than 29 science teachers participated in the study. The effect of state where science

teachers were teaching at the time of the study was not statistically significant for

teachers' perceptions, F(5, 438)=1.628, p=.15 1, and practices, F(5, 433)=1.203, p=.307,

regarding the use of technology for scientific inquiry purposes; teacher's level of

preparedness for using inquiry skills in instruction, F(5, 449)=2.047, p=.071; teacher's









level of preparedness for using technology tools, F(5, 415)=1.065, p=.379; and teachers'

frequency of using technology tools in their classrooms, F(5, 396)=1.550, p=.173.

Only teachers' frequency of using inquiry skills was significant at the .05 level,

F(5, 445)=3.168, p=.008, 2 =.034. A post hoc Tukey test was conducted to determine if

teachers teaching in various states had significant differences among them in terms of six

variables listed above. Results indicated significant difference between only those

teachers who taught in Florida and Kansas. Science teachers from Florida (Mean=33.66,

SD=5.32) had significantly higher scores than those who teach in Kansas (Mean=30.29,

SD=5.04), p=.004, SE=.93.

The effect of years of science teaching experience was not statistically significant

with teachers' perceptions, F(4, 543)=.693, p=.597, and practices, F(4, 545)=.615,

p=.652, regarding the use of technology for scientific inquiry purposes; teacher's level of

preparedness for using inquiry skills in instruction, F(4, 566)=1.777, p=.132; teachers'

frequency of using inquiry skills, F(4, 559)=2.129, p=.076; and teachers' frequency of

using technology tools in their classrooms, F(4, 504)=.251, p=.909. With an alpha level

.05, the effect of years of science teaching experience was statistically significant for

teacher's level of preparedness for using technology tools, F(4, 528)=4.167, p=.002,

r2=.031. A post hoc Tukey test was used to determine which experience categories

indicated statistically significant mean scores. According to the results, science teachers

who have 1 to 9 years of experience (Mean=74.88, SD=14.89) had significantly higher

mean scores than those who taught science for 20 to 29 years, (Mean=69.47, SD=14.97),

p=.034, SE=1.88, as well as those who taught science for 30 to 39 years, (Mean=67.67,

SD=15.33), p=.034, SE=2.26. These results demonstrated that those science teachers who









are new in the field and less than 10 years of experience with respect to those who have

20 to 39 years of experience reported higher level of preparedness for using technology

tools in instruction. All of the partial eta-squared values to report effect sizes were less

than .06, which indicated small effect sizes for significant ANOVAs, based on criteria

suggested by Cohen (1988, p.285).

A Summary of Results in terms of Study Research Questions

The researcher made use of a variety of methods to answer the overarching and the

supporting questions. First of all, reliability and item analysis and exploratory and

confirmatory factor analyses were used to determine the reliability and validity of the

SIT-TIPPS instrument. This process was essential in answering the research questions as

all of the overarching and supporting questions depended on the quality of the scale. The

data obtained through teachers' responses to the components of the SIT-TIPPS

instrument were analyzed using a variety of statistical methods to answer the research

questions of the study. The SIT-TIPPS indicated high reliability (see Table 3-1 & 3-2)

and good content and construct validities based on expert judgment and factor analysis

results.

The additional methods used to answer the study's questions were descriptive

statistics (e.g., frequencies), correlations, multiple regressions, t-tests, and ANOVAs.

Interpretations from all of these methods contributed to the explanation of the first

overarching question, which dealt with how science teachers are using technology to

implement the goals of scientific inquiry in their classrooms. It can be concluded that

science teachers who scored high in the SIT-TIPPS instrument used technology

tools/applications to engage students in scientifically oriented questions; to encourage

students to give priority to evidence in responding to questions; to enable students to









formulate explanations from evidence; to enable students to connect explanations to

scientific knowledge; and to encourage students to communicate and justify their

explanations. The data provided by the SIT-TIPPS instrument provides an in-depth

snapshot to describe how the five essential features of scientific inquiry as presented in

the National Science Education Standards (1996) are being used by middle and high

school science teachers.

For the second overarching question, the correlations between the 25 items

constituting the teachers' perceptions factor and the 25 items constituting the teachers'

practices factor were calculated. Multiple regression method was used as well to answer

this question because models indicated that these two factors were dependent on each

other. Correlation analysis indicated a positive significant correlation between teachers'

perceptions and practices regarding the use of technology for scientific inquiry purposes.

The first supporting question investigated the effect of teacher demographics and

teacher background/teacher professional development variables on teachers' self-reported

perceptions and practices for using technology to enact scientific inquiry. For this

purpose, descriptive statistics, multiple regressions, correlations, t-tests, and ANOVAs

were used. In general, the results in this category indicated that as science teachers get

more years of teaching experience, get exposed to educational technology training at

different stages during their career, and get access to more computers in their classrooms

and computer labs, they are more likely to use technology tools in their classrooms.

Teachers' gender, race/ethnicity, state in which science teachers are teaching, and the

number of different types of science courses a science teacher taught did not have any









significant effect on their perceptions and practices regarding the use of technology for

scientific inquiry purposes.

The last four supporting questions enabled the researcher to explore the second

overarching research question. Nine items from the SIT-TIPPS were used to measure

how well prepared teachers' feel to support students to engage in certain inquiry skills

and how often they use these inquiry skills in their classrooms. Twenty-six items were

employed to answer how well prepared teachers' feel to use certain technology

tools/applications and how often they use these tools/applications in their classrooms. In

order to answer these four supporting questions, the researcher used frequency reports,

correlations, and multiple regression models. In general, results in this category showed

science teachers felt very well prepared in supporting students to collect, organize, and

analyze data; explain cause-effect relationships; and conduct experiments. However, they

felt less comfortable in supporting students to identify their own misconceptions of

science content; find biases and flaws in their scientific explanations; test scientific

explanations against current scientific knowledge; and critique experiments. In terms of

how frequently these skills are integrated into science classrooms, teachers seemed to

more frequently support students to explain cause-effect relationships; to discuss

scientific explanations/ideas/models with others, conduct experiments; and to collect,

organize, and analyze data. However, they less frequently support students to find biases

and flaws in scientific explanations; to test scientific explanations against current

scientific knowledge; and to critique experiments.

In addition, teachers' frequency of using inquiry skills and their level of

preparedness for using technology tools are jointly associated with about 12% of the









variance in teachers' self-reported perceptions regarding the use of technology for inquiry

purposes. About 33% of the variance in teachers' practices regarding the use of

technology for scientific inquiry purposes was explained by how frequently teachers used

inquiry skills and technology tools in instruction and how well prepared they feel to use

technology tools. The extent to which science teachers felt better prepared to use

scientific inquiry skills was associated with about 25% of the variance in how frequently

they use these skills in instruction. Moreover, results also indicated that as science

teachers feel more prepared to use technology tools in their lessons, get more years of

teaching experience, get exposed to educational training at different stages during their

career, get access to more computers in their classrooms and computer labs, they will

more likely to use technology tools in their science courses.









Table 4-1. Participant characteristics based on gender (n=598)


Gender


Male
Female


Table 4-2. Participant characteristics based on race/ethnicity (n=590)


Race/Ethnicity
American
Indian
Asian
Black
Hispanic
White
Multiracial


%
34.6
65.4


n %


20
34
26
479
27


3.4
5.8
4.4
81.2
4.6










Table 4-3. Distribution of participants based on states (n=601)
States n %
AL 16 2.7
AZ 3 .5
CA 15 2.5
CT 30 5.0
FL 156 26.0
GA 6 1.0
HI 9 1.5
IL 6 1.0
IN 1 .2
IA 13 2.2
KS 50 8.3
KY 1 .2
LA 1 .2
MD 2 .3
MA 2 .3
MI 36 6.0
MN 1 .2
MO 6 1.0
MT 1 .2
NE 1 .2
NH 6 1.0
NJ 3 .5
NM 7 1.2
NY 5 .8
NC 6 1.0
OH 5 .8
OK 1 .2
PA 1 .2
SC 6 1.0
TN 1 .2
TX 5 .8
VA 165 27.5
WA 2 .3
WV 1 .2
WI 29 4.8
WY 1 .2
DC 1 .2









Table 4-4. Number of years of teaching experience: Categorized (n=583)
Categories n %
1-9 244 41.9
10-19 187 32.1
20-29 99 17.0
30-39 49 8.4
40-44 4 .7

Table 4-5. Grade levels taught by science teachers (n=715)
Characteristics n %
Grade levels taught
6 171 23.9
7 238 33.3
8 252 35.2
9 345 48.3
10 360 50.3
11 378 52.9
12 371 51.9
Over 12 130 18.2
Total number of grades taught
1 71 9.9
2 82 11.5
3 124 17.3
4 149 20.8
5 79 11.0
6 39 5.5
7 35 4.9
8 21 2.9

Table 4-6. Courses taught by science teachers (n=715)
Characteristics n %
Course name
Life Sciences 364 50.9
Earth Sciences 283 39.6
Physical/General Sciences 369 51.6
Physics 142 19.9
Chemistry 194 27.1
Total number of courses taught
1 167 23.4
2 195 27.3
3 161 22.5
4 58 8.1
5 16 2.2










Table 4-7. Science teachers' frequency of use of scientific inquiry skills


To support students to:

1. Ask researchable questions
2. Conduct experiments
3. Collect, organize, analyze data
4. Identify their own
misconceptions of science
content
5. Explain cause-effect
relationships
6. Test scientific explanations
against current scientific
knowledge
7. Find biases or flaws in their
scientific explanations
8. Discuss scientific
explanations/ideas/models with
others
9. Critique experiments


P .s

1 c o S C )
1 2 3 4 5 n M S.D.


33.2
31.5
25.6
32.8


41.4
52.3
57.6
37.4


14.8
11.5
12.6
18.6


3.59
3.70
3.78
3.62


.3 2.9 21.2 47.4 28.2 624 4.00 .80

3.2 14.9 38.4 34.7 8.8 623 3.31. .94


2.6 15.2 35.7 33.9 12.6 625 3.39 .97

1.3 6.6 23.2 40.7 28.2 624 3.88 .94


3.4 21.2 34.6 33.8 7.1 622 3.20 .96










Table 4-8. Science teachers' frequency of use of technology tools


5
.


n M S.D.


1. Presentation devices (such as
video projectors, LCD
panels)
2. Smart Board/Promethean
interactive boards
3. Wireless communication
devices (e.g., PDAs, student
digital response systems)
4. Graphing/scientific
calculators
5. Portable Global Positioning
Systems (GPS)
6. Digital data collection
devices (e.g., pH, pressure
and temperature probes,
digital microscopes,
Navigator systems)
7. Videoconferencing,
teleconferencing
8. Word processing (e.g., Word)
9. Spreadsheets (e.g., Excel)
10. Presentation software (e.g.,
Power Point)
11. Database software
12. Educational games
13. Virtual experiences (e.g.,
Google Earth and Starry
Night, a virtual planetarium)
14. Graphing and data analysis
software
15. Video editing
16. Image/picture editing
17. E-mail
18. Webpage design
19. Accessing online databases
20. Internet searches
21. Online simulations
22. Online science games
23. Wikis
24. Blogs
25. Podcasts, videocasts
26. Data collection
telecollaborative activities
(e.g., Journey North,
SCOPE, Amazing Space)


2.5 5.4 15.1 31.4


45.6 609 4.12


53.0 13.4 7.0 11.1 15.4 610 2.23

62.3 18.1 10.7 5.8 3.1 608 1.69


30.6 16.8 20.1 19.1 13.3 607 2.68


69.9 19.8


7.6 1.8 1.0 607 1.44


22.8 26.1 30.2 15.8


81.3 11.8


5.1 609 2.54


3.9 2.0 1.0 611 1.29


1.6 4.9 16.6 29.7
7.9 14.5 28.9 30.7
3.5 11.4 18.6 28.5

31.0 23.7 23.5 13.7
20.5 27.1 32.8 14.0
33.2 32.7 21.9 9.4


26.0 22.7 30.3 15.1

64.1 22.6 9.6 2.6
45.9 23.8 18.6 7.5
15.2 10.4 16.4 23.4
51.5 21.9 12.1 8.6
23.7 28.0 26.8 14.9
2.7 10.6 29.4 32.0
14.0 22.3 36.1 18.8
28.2 27.0 29.7 10.1
68.1 16.6 9.0 4.3
74.1 14.0 6.5 4.0
62.8 19.8 11.5 3.3
691. 18.4 7.4 3.8


47.1 609 4.16
17.9 605 3.36
38.1 607 3.86

8.1 604 2.44
5.6 609 2.57
2.8 608 2.16


5.8 603 2.52

1.0 605 1.54
4.2 601 2.00
34.7 599 3.52
5.8 602 1.95
6.6 604 2.53
25.4 603 3.67
8.8 601 2.86
5.0 603 2.37
2.0 598 1.56
1.5 602 1.45
2.5 600 1.63
1.3 598 1.50










Table 4-9. Science teachers' level of preparedness of scientific inquiry skills


To support students to:


1. Ask researchable questions
2. Conduct experiments
3. Collect, organize, analyze data
4. Identify their own
misconceptions of science
content
5. Explain cause-effect
relationships
6. Test scientific explanations
against current scientific
knowledge
7. Find biases or flaws in their
scientific explanations
8. Discuss scientific
explanations/ideas/models with
others
9. Critique experiments


Sa C ) CC
CA >
1 2 3 4 n M S.D. a
1 2 3 4 n M S.D.


44.0
36.0
34.6
46.3


42.1
53.4
58.7
33.2


3.26
3.41
3.51
3.10


1.3 7.5 39.1 52.1 626 3.42 .69

2.2 18.7 43.9 35.1 626 3.12 .78


4.0 18.4 41.6 36.0 625 3.10 .83

2.4 9.1 40.7 47.8 627 3.34 .74


4.3 15.7 38.2 41.8 624 3.17 .85










Table 4-10. Science teachers' level of preparedness of using technology tools


CA
1U 23 3
3.i c3 U c >ic
Sa g a T
o~ ~
1 a 3 a Fa a
i 2 3


4


n M S.D.


1. Presentation devices (such as video
projectors, LCD panels)
2. Smart Board/Promethean interactive
boards
3. Wireless communication devices
(such as PDAs, student digital
response systems)
4. Graphing/scientific calculators
5. Portable Global Positioning Systems
(GPS)
6. Digital data collection devices (such
as pH, pressure and temperature
probes, digital microscopes,
Navigator systems)
7. Videoconferencing, teleconferencing
8. Word processing (e.g., Word)
9. Spreadsheets (e.g., Excel)
10. Presentation software (e.g., Power
Point)
11. Database software
12. Educational games
13. Virtual experiences (such as
Google Earth and Starry Night, a
virtual planetarium)
14. Graphing and data analysis software
15. Video editing
16. Image/picture editing
17. E-mail
18. Webpage design
19. Accessing online databases
20. Internet searches
21. Online simulations
22. Online science games
23. Wikis
24. Blogs
25. Podcasts, videocasts
26. Data collection telecollaborative
activities (e.g., Journey North,
SCOPE, Amazing Space)


3.1 6.7 22.7 67.5 616 3.55 .75

33.3 22.9 20.3 23.4 615 2.34 1.2

37.9 25.4 19.7 16.9 614 2.16 1.1


18.7 25.3 26.3 29.7 616 2.67 1.1
42.9 26.8 17.5 12.9 613 2.00 1.1

12.2 22.2 29.3 36.3 617 2.90 1.0


26.0
1.6
7.5
4.9


8.5
87.6
68.1
77.3


1.86
3.85
3.56
3.69


11.4 18.8 28.6 41.2 616 3.00
7.9 19.1 31.8 41.2 611 3.06
14.1 27.8 28.3 29.8 615 2.74


12.7
42.1
20.6
1.1
27.2
9.8
1.0
6.4
10.0
40.3
38.0
37.3
48.9


27.6
27.2
28.1
2.4
29.2
21.2
3.6
15.5
17.6
23.0
26.8
28.3
26.7


28.1
18.9
25.8
12.1
23.5
30.1
19.9
29.8
30.3
19.6
19.3
21.2
15.5


31.6
11.7
25.5
84.4
20.2
38.9
75.6
48.4
42.1
17.1
15.9
13.2
9.0


2.79
2.00
2.56
3.80
2.37
2.98
3.70
3.20
3.04
2.13
2.13
2.10
1.85












Table 4-11. Pearson Pro
Variables
Perceptions
Practices
Inquiry skills-level
Inquiry skills-
frequency
Technology tools-
level
Technology tools-
frequency
Years of experience
Number of grades
taught
Grade level
Number of computers


)duct-Moment Correlation between variables
1 2 3 4 5
1 .649** .225** .267** .233**
1 .283** .456** .384*
1 .522** .448**
1 .423**


6
.189**
.443*
.275**
.450**


7
-.019
.359
.078
.064


.671** -
.177**


.117** .249**


.112** .120** .136** .180**


.226** .202**
1 .982**


-.022 .087* .321**
1 .045 -.013


in class
Number of computers
in computer lab
Previous ed tech
training
Note. *p<.05, **p<.O00 (2-tailed)


8
.087**
.024
.151**
.065


9
.077
.011
.144**
.050


10
-.006
.109*
.007
.070


11
.084
.136**
.062
.088*


12
.136**
.157**
.042
.069


.118** .121** .061


.110**
-.041


.262**

-.193**
.343**


.035
.107*


-.120**









Table 4-12. The number of computers in classrooms
Number of Computers n % Cumulative %
1 213 36.1 36.1
2 86 14.6 50.7
3 38 6.4 57.1
4 29 4.9 62
5 32 5.4 67.5
6 14 2.4 69.8
7 27 4.6 74.4
8 20 3.4 77.8
9 15 2.5 80.3

Table 4-13. Percent of teachers reporting taking educational technology classes
n % Total
Level of educational technology training
High school 152 28.3 538
Undergraduate school 261 47.3 552
Graduate school 288 54.4 529
In-service training or continuing 568 92.3 568
education courses
Frequency of educational technology training
1 187 26.2
2 204 28.5
3 122 17.1
4 66 9.2

Table 4-14. T-test results for subscales based on presence of science lab in classroom
Subscale vs. lab presence N Mean SD t df P Cohen's d
Practices
Lab present 409 99.83 16.79 2.25* 556 .025 .22
Lab not-present 149 96.13 18.17
Technology tools-level
Lab present 395 72.90 14.76 2.00* 541 .046 .19
Lab not-present 148 70.00 15.86
Technology tools-frequency
Lab present 377 64.65 16.39 2.74* 517 .006 .27
Lab not-present 142 60.25 15.95
* p< .05, two-tailed









Table 4-15. T-test results for subscales based on presence of computer lab in school
Subscale vs. lab presence N Mean SD t df P Cohen's d
Technology tools-level
Lab present 502 72.73 14.91 2.75* 537 .006 .47
Lab not-present 37 65.73 15.57
Technology tools-frequency
Lab present 479 63.79 16.47 2.10* 513 .036 .39
Lab not-present 36 57.86 13.90
* p< .05, two-tailed

Table 4-16. ANOVA results between subscales and selected variables
Source df df F rr P
(between) (within)
Ethnicity x Inquiry skills-level 5 572 3.016 .026 .011*
Experience-categorized x Technology 4 528 4.167 .031 .002*
tools-level
State x Inquiry skills-frequency 5 445 3.168 .034 .008*
p<.05

Table 4-17. Summary of Post Hoc (Tukey) ANOVA results for significant differences
Source N Mean SD SE P
Ethnicity x Inquiry skills-level
Asian 19 26.95 6.96 1.60
Hispanic 26 32.31 3.93 .77
Asian x Hispanic 1.57 .009*
Experience-categorized x Technology tools-
level


1-9
20-29
30-39
1-9 x 20-29
1-9 x 30-39
State x Inquiry skills-frequency
FL
KS
FL x KS
* p<.05


224
85
46


74.88
69.47
67.67


14.89
14.97
15.33


.99
1.62
2.26
1.88
2.39


.034*
.023*


151 33.66 5.32 .43
48 30.29 5.04 .72
.93 .004*









CHAPTER 5
FINDINGS, CONCLUSIONS, IMPLICATIONS AND SUGGESTIONS FOR FUTURE
RESEARCH

There have been many efforts throughout the history of science education to improve

teaching and learning in elementary and secondary schools (Abrams, 1998). The National

Research Council and American Association for the Advancement of Science have contributed

to efforts by publishing reports such as the National Science Education Standards (1996),

Science for All Americans (1990), and Benchmarks for Science Literacy (1993) that emphasized

student learning, the nature of science, science literacy, and scientific inquiry. Despite these

efforts, the current science curriculum in the United States and many other countries has failed to

prepare students for the kinds of experiences they will need to become successful science

learners (Linn et al., 2004).

Scientific inquiry has been an overarching goal of science education (AAAS, 1993; NRC,

1996; Flick, 1997; Crawford, 1997; Edelson, et al., 1999) and a central strategy for teaching

science (NRC, 1996) for decades. Although there are certain instructional methods and strategies

that help teachers implement scientific inquiry in their classrooms, the use of technology can also

play a significant role in meeting the goals of scientific inquiry. According to the National

Research Council (1996), a goal for using educational technology in the classroom is to identify

effective ways to use technology tools for higher-order thinking that mesh with the assumptions

of scientific inquiry.

When it comes to technology integration, teachers play a key role (Scheffler & Logan,

1999). Yet, research indicates teachers lack good instructional frameworks for effective

implementation of technology into the curriculum (Bitner & Bitner, 2002). In addition to that,

there is little literature available on answering the question of how science teachers use

technology to succeed in enacting the goals of scientific inquiry. There is clearly a need to









understand how science teachers use technology as they strive to attain the goals of scientific

inquiry in their classrooms and to provide teachers with a framework for using technology in

scientific inquiry-based instruction.

This study, which is based on the premise technology could enhance the quality of

scientific inquiry-based instruction, attempted to answer the question of how science teachers use

technology to attain the goals of scientific inquiry-based instruction through the development of

an instrument about scientific inquiry and the use of technology for that purpose. It was an

attempt to address an essential topic both in science education and educational technology.

Through the creation of an instrument, the SIT-TIPPS, the study addressed the following

research questions:

Overarching Questions

1. How are teachers using technology to implement the goals of scientific inquiry in their
classrooms?

2. What are the relationships between teachers' self-reported perceptions and practices
regarding the use of technology to attain the goals of scientific inquiry?

Supporting Questions

What are the relationships between teachers' self-reported perceptions and practices regarding
the use of technology to attain the goals of scientific inquiry in terms of:

* Teacher demographics and teacher background/professional development variables?

* How often do teachers support students to engage in certain inquiry skills in their science
classrooms?

* How often do teachers use certain technology tools in their science classrooms?

* How prepared do teachers feel to support students to engage in certain inquiry skills in their
science classrooms?

* How prepared do teachers feel to use certain technology tools in their science classrooms?









The SIT-TIPPS Instrument

One of the main purposes of this study was to validate and develop an instrument called

the Scientific Inquiry with Technology-Teachers' Perceptions and Practices Scale (SIT-TIPPS).

The theoretical framework and extensive literature base of the items as well as experts'

agreement on the contents of the items contributed to the content validity of the instrument. In

addition, a successful two-factor solution using exploratory factor analysis provided support for

the construct validity of the SIT-TIPPS. Therefore, the SIT-TIPPS instrument could be used to

identify middle and high school science teachers' self-reported perceptions and practices

regarding the use of technology to enact scientific inquiry in their classrooms and to explore to

what extent science teachers feel comfortable to use certain scientific inquiry skills and

technology tools in their lessons. This is important in assessing the needs of science teachers in

terms of professional development and designing effective preservice and inservice training

programs.

One of the initial expectations of the researcher when generating the item pool for the SIT-

TIPPS was to observe a five-factor solution after factor analysis. This was because the first

attempt by the researcher to generate the SIT-TIPPS items based on the five essential features of

scientific inquiry as described in the National Science Education Standards (NRC, 1996). The

factors are: Teachers engage students in scientifically oriented questions; enable students to give

priority to evidence in responding to questions; encourage students to formulate explanations

from evidence; enable learners to connect explanations to scientific knowledge; and encourage

learners to communicate and justify explanations. The factor analysis procedure did not produce

a five-factor solution as expected. Instead, it produced a two-factor solution in the form of

perceptions and practices. Although all of the items in the scale, measuring either perceptions or

practices of science teachers regarding the use of technology to attain the goals of scientific









inquiry, were generated based on these five features of scientific inquiry and their contents were

verified by five content experts, the inability of factor analysis to yield five factors might indicate

that science teachers' opinions and perspectives regarding these five components are not

distinctive. Although the National Science Education Standards (NRC, 1996) reports five

psychological factors in the attainments of teaching scientific inquiry, this study provided

evidence that these five factors are not statistical factors. Creating the SIT-TIPPS with two

factors, perception and practice, created a better model for understanding science teachers'

perceptions and practices regarding the use technology to attain the goals of scientific inquiry in

instruction.

Summary of Findings

The results of the study demonstrated that the SIT-TIPPS is a useful tool in analyzing self-

reported current practice of middle and high school science teachers regarding the use of

technology for scientific inquiry purposes and in furthering the discussion on how science

teachers can use technology to attain the goals of scientific inquiry. It measured middle and high

school science teachers' self-reported perceptions and practices regarding the use of technology

in attaining the goals of scientific inquiry. In doing so, it connected theory and research from two

fields, science education and educational technology, which can result in a change of daily

practice in science classroom. Even though there are some instruments targeting scientific

inquiry in science education literature (Bodzin & Beerer, 2003; Brandon & Taum, 2005), an

instrument specifically targeting scientific inquiry in science classrooms where technology is

used is an area of need in both fields. The instrument developed in this study and the research

questions it attempted to answer is a contribution to this body of literature and the fields of

science education and educational technology.









This study had two overarching research questions and five supporting questions. The

main questions focused on how teachers use technology to implement the goals of scientific

inquiry and the relationships between their self-reported perceptions and practices regarding this

implementation. The supporting questions articulated these relationships by addressing some

teacher demographics and background/professional development variables as well as the level

and frequency of their preparedness and use of certain inquiry tasks and technology tools. While

the total scores obtained from the SIT-TIPPS and the relationships between perception and

practice items were utilized to help answer the overarching questions of the study, relationships

obtained from supporting questions also contributed to answering these overarching questions.

Conclusions were drawn from the results of the study in relation to these questions.

Characteristics of Science Teachers

A national sample of middle and high school science teachers was targeted to participate

in the study. About 65% of the science teachers were females and the majority (81.2%) were

white. This profile of science teachers parallels that of another large-scale national study called

the National Survey of Science and Mathematics Education (Weiss et al., 2001; Smith et al.,

2002) in which about 2500 middle and high school science teachers were surveyed from 1977 to

2000. In this survey, about 64% of the science teachers (grades 5-12) were females and about 81

percent were white. This similarity in profiles in terms of gender and race/ethnicity might

indicate a continuing trend among middle and high school science teachers in the U.S. since

1977. In that respect, this study might be a good contribution to the data from 1977 to 2000 by

demonstrating a similar trend in 2008.

Demographic findings also indicate that the more experienced science teachers get the

more grades (6 through 12 and above) they tend to teach. However, teachers with less teaching

experience (or younger for that matter) seem to receive more educational technology training at









more levels of various educational settings and in-service/continuing education and seem to feel

more prepared to use technology tools in instruction.

Data also showed that the level of grades taught significantly correlates with the total

number of educational levels in which teachers received educational technology training. Hence,

it appears that teachers who teach more grade levels receive more educational technology

training. The level of grade taught positively correlates with the level of preparedness for using

inquiry skills, the level of using technology tools, and frequency of using technology tools in

instruction. This means that teachers who teach higher-level grades indicate a higher level of

preparedness and frequency for using inquiry skills and technology tools in their lessons. This

could be reflective of the potential differences in preservice and inservice experiences at the

middle school and high school level. This finding is in line with the results of the 2000 National

Survey of Mathematics and Science Education study (Weiss, et al., 2001), which indicated

higher percentages of high school science teachers feeling very well qualified to teach science

processing skills.

A Snapshot of Science Teachers' Scientific Inquiry and Technology Skills

Study results gave a clear picture of science teachers' strengths and areas in which

improvements are needed in the areas of scientific inquiry and technology integration. Table 5-1

and Table 5-2 list teachers' weaknesses and strengths in terms of their self-reported comfort

levels and actual practices in using inquiry skills and technology tools/applications in instruction.

Middle and high school science teachers that participated in this study do feel prepared to

support students in "traditional" aspects of scientific inquiry. However, in areas which might

challenge content knowledge, such as identifying misconceptions of science content, there is

great discomfort. When looking at the tasks listed in the weakness column of Table 5-1, a









concern of science content is clear. This results in students not engaging in scientific inquiry

tasks that challenge and expand their content knowledge.

Regarding middle and high school science teachers' self-reported comfort levels and

integration of technology tools/applications in instruction, it is obvious to see that science

teachers feel very comfortable using common forms of technologies such as word processing,

Internet searches, and spreadsheets. However, they exhibit low comfort level and integration

when it comes to "uncommon" and "new" forms of technologies such as data collection

telecollaborative activities, portable Global Positioning Systems, and Internet 2.0 applications.

The technologies listed in the weakness column of Table 5-2 designate essential tools and

applications that lend themselves to higher-order thinking in classrooms. Hence, the inadequacy

of science teachers' abilities to utilize tools such as these point out an area of concern because it

results in students not benefiting from the transformative and challenging power of these tools to

investigate scientific principles and concepts.

Source of Computer Knowledge

Science teachers' responses to whether they received educational technology training

during high school, undergraduate school, graduate school, or in-service/continuing education

yielded interesting results. The majority of the science teachers (92.3%) reported receiving

educational technology classes during in-service training and continuing education. This

indicates the importance of in-service training provided to science teachers regarding the use of

instructional technologies in classrooms. More educational technology training was received by

science teachers in graduate school than during undergraduate education. Because of teachers'

multiple responses to select a level where they received educational technology training, it was

interesting to see the percentage of teachers who reported having training at certain number of

levels. Although nearly 92% reported getting educational technology training at a various level









during their careers, these findings indicate a low infusion of educational technology training

throughout the career path of a science teacher.

Additional Demographic Findings

Although previous research indicated more male teacher use of computers in classrooms

(Becker, 1994; Chiero, 1997), this study found no differences between male and female science

teachers. According to the results of this study, there is no difference between male and female

science teachers in terms of their perceptions and practices regarding the use of technology for

scientific inquiry purposes; how prepared they feel to use inquiry skills and technology tools in

instruction; and how often they used inquiry skills and technology tools during lessons.

Having a computer lab at school also plays an important role for science teachers.

Teachers who have a computer lab in their schools scored significantly higher than those who

did not on how well prepared they feel to use technology tools and how frequently they use these

tools in instruction. Both groups, on the other hand, do not differ in terms of their perceptions

and practices regarding the use of technology for scientific inquiry purposes.

Teachers' race/ethnicity does not produce significant results for teachers' self-reported

perceptions and practices in using technology for inquiry purposes, their level of preparedness

for using technology tools, and how often they use inquiry skills and technology tools in

instruction. It is, however, significant for how they practice the use of technology for scientific

inquiry skills in classroom as well as for how well prepared they feel to use inquiry skills in

instruction. Though significant, post hoc ANOVA test did not produce any group differences

between race/ethnicity categories. The only significant mean difference was between Hispanic

and Asian teachers in terms of how well prepared they feel to use inquiry skills. Results indicate

Hispanic teachers feel more prepared to use scientific inquiry skills in instruction than their

Asian counterparts.









The results were not very different for the state in which science teachers are teaching

based on the variables listed above. Only teachers from Florida and Kansas (among Connecticut,

Florida, Kansas, Michigan, Virginia, Wisconsin) differ significantly in terms of how frequently

they use scientific inquiry skills in instruction. Florida teachers use scientific inquiry skills more

in their lessons than Kansas teachers. An overview of the science education standards of both

states reveals Florida science education standards seem to provide more detailed benchmarks to

assist teachers in instruction. However, a more thorough analysis of the standards in two states

are needed to make comparisons and to identify the impact of content standards in both states on

teachers' implementation of inquiry skills in their classrooms.

Science teachers' years of teaching experiences are significant for how confident they

feel to use technology tools in their classrooms. According to the results, science teachers who

have 1 to 9 years of teaching experience reports higher level of confidence in using technology

tools in their lessons than those who have 20 to 29 years of experience and 30 to 39 years of

experience. This demonstrates science teachers who are relatively new and have less than 10

years of experience in the field feel more prepared to use technology tools in their lessons than

their more experienced counterparts (between 20-39 years of experience). Interestingly, the

group of science teachers who have less than 10 years of experience does not demonstrate a

significant difference with those who have 10 to 19 years of teaching experience.

Study Implications

The SIT-TIPPS instrument developed in this study could be used to identify middle and

high school science teachers' self-reported perceptions and practices regarding the use of

technology to enact scientific inquiry in their classrooms and to explore to what extent science

teachers feel comfortable to use certain scientific inquiry skills and technology tools in their









lessons. It also enables one to look at the existence and the degree of relationship among this set

of variables.

The findings of the study have implications that could help researchers, educators, science

teachers and administrators identify how technology is being used by middle and high school

science teachers as well as how well prepared they feel to use scientific inquiry skills and

technology tools during instruction. It also provides information about science teachers' self-

reported perceptions and practices of using technology to attain the goals of scientific inquiry as

set forth by the National Science Education Standards (NRC, 1996) and discussed extensively by

researchers in the field.

Moreover, the SIT-TIPPS instrument could effectively be used as an evaluation tool for

curriculum. For instance, the integration of an exemplary science curriculum such as the

Foundational Approaches in Science Teaching (FAST) that potentially lends itself to students

using the scientific inquiry process could be evaluated using the components of SIT-TIPPS

instrument. The experiments and activities employed by a FAST teacher could be evaluated in

terms of the use of inquiry skills and technology tools/applications addressed in the SIT-TIPPS.

For example, using the SIT-TIPPS one can easily determine the degree to which a FAST teacher

enables students to critique experiments found in FAST or to test their explanations from the

FAST activities against current scientific knowledge. The SIT-TIPPS could enable researchers to

analyze the state of integration of scientific inquiry and technology not only at the classroom

level, but also at the school and district level. The way the SIT-TIPPS instrument was developed

dovetails with the essential features of scientific inquiry that has been an overarching goal in the

science education field (NRC, 1996; Flick, 1997; Crawford, 1997; Edelson et al., 1999) along

with the manner in which technology plays a significant role in meeting the goals of scientific









inquiry. This approach can help researchers from both science education and educational

technology fields analyze how science teachers perceive such a complex form of instruction and

how often they try to implement such practices in their lessons. The findings from such studies

could shed light on school level and district level analysis of the state of scientific inquiry

implementation from teachers' perspective and then could lead to district level policy and

professional development for science teachers. For example, the finding that illustrates science

teachers' inadequacy to use interactive whiteboards, which many districts purchase these days,

highlights the importance of knowing this fact in order to provide professional development to

enable teachers to support students' learning through presentations, demonstrations, and more.

In this respect, another important aspect of the curricular evaluative ability of the SIT-

TIPPS instrument is its emphasis on teachers' role. When it comes to technology teachers play a

key role (Scheffer & Logan, 1999). Yet, research indicates teachers lack solid instructional

frameworks for effective implementation of technology into the curriculum (Bitner & Bitner,

2002). The SIT-TIPPS with its emphasis on teachers' perceptions and practices, and the way it

meshes the essential features of scientific inquiry skills and use of technology tools can be used

by researchers, teacher educators, and administrators to analyze teacher behaviors and practices

with respect to technology use to achieve scientific inquiry.

The SIT-TIPPS can also be a useful tool in teaching preservice science teachers and

helping them understand the complex nature of scientific inquiry and how technology can be

used to facilitate its successful implementation. According to some studies, preservice science

teachers graduated without conducting a single inquiry in their programs (Windschitl, 2003) and

lacked understanding of inquiry, skills, and experiences (Newman et al., 2004) as well as training

and support (White & Frederiksen, 1998) to implement inquiry-oriented teaching. They also









have very few operational models (Crawford, 1997) that they could use during inquiry

instruction. The instrument developed in this study has the potential to support preservice science

teachers to better understand what inquiry skills are expected of students and how technology

can contribute to the fulfillment of these expectations. For instance, one of the scientific inquiry

skills to which students are to be exposed and acquire knowledge about is addressing

misconceptions of science content. Presentation devices and reliable Internet-based applications

could help students identify and overcome their own misconceptions. As noted in the study

findings, when preservice teachers are exposed to such a detailed approach, they can develop a

better understanding of the inquiry skills expected from students and understand ways

technology contribute to its achievement. The findings of the study point out that even inservice

science teachers' perceptions and actual classroom practices for using technology in a science

inquiry oriented classroom differ. A more structured scientific inquiry education/training using

technology in colleges and schools of education designed according to the National Science

Education Standards for scientific inquiry might help close this gap for preservice and inservice

science teachers.

The study highlights areas where professional development activities for science teachers

could focus. For instance, the findings that indicate science teachers' low comfort levels and uses

of inquiry skills such as supporting students to critique experiments and finding biases/flaws in

their reasoning are salient for professional development designers. In terms of technology,

teachers' low comfort levels in and uses of new and/or uncommon forms of technology

tools/applications are also worth paying attention. Teachers are comfortable with technology

tools that have been around for a long time such as email. The same teachers, on the other hand,

report low a comfort level and integration when it comes to a relatively newer or unfamiliar









forms of technology tools such as using Internet 2.0 tools. These technology tools exhibit high

potential to facilitate scientific inquiry in classrooms with the help of technology. These results

indicate the need to train science teachers to be able to use these technology tools and to show

them how these technologies could bring about change in their teaching practices toward better

integration of technology to attain the goals of scientific inquiry.

Professional development activities and preservice teacher education programs should

concentrate more on increasing science teachers' familiarities with these new and unfamiliar

forms of technologies while continuing to encourage the use of most commonly used

technologies. As study findings indicate, when science teachers get more prepared to use

technology for scientific inquiry purposes they tend to use it more often in their classrooms for

the same purposes. If science teachers' familiarity with these technology tools increase, they will

be more likely to appreciate the potential these technology tools carry for their instruction.

Another finding of the study indicates almost all of the classrooms have at least one computer in

them. This tells us that the limited use of these computer tools could be affected by teachers'

inability to use them effectively. Yet, if the computers that are available in classrooms are

lacking the technical requirements to run these programs/applications efficiently, it might also

contribute to teaches' inability to use these tools/applications in their classrooms.

The findings also have implications for school districts, community colleges, colleges and

schools of education, researchers, practitioners, administrators or even politicians who have the

responsibility to provide training to science teachers or make decisions on their behalf. The

components of the SIT-TIPPS instrument could be used by professors and trainers to help

preservice and inservice science teachers identify what they are missing in their recent

knowledge base and skills related to scientific inquiry and technology, and then focus their









training/education on these missing areas. One tangible product of such an understanding, for

example, would be to teach science teachers how to use data collection telecollaborative

activities and how to integrate them to support students to develop hypotheses,

collect/organize/analyze data, make judgments, and critique findings with peers. As this study

indicates such uses are missing in our classrooms.

As the findings of this study also indicates, science teachers' comfort levels with and

exposure to inquiry skills and technology tools/applications matter. Improving scientific inquiry

and technology skills could be achieved by exposing teachers to these skills, technology tools,

and strategies on a regular basis. They need to observe successful implementation examples and

practice these skills before they complete their initial teacher preparation program. For inservice

teachers, this translates to new policies in the form of mandatory scientific inquiry and

technology professional development programs/trainings. As this study illustrates, the higher the

comfort level science teachers have with inquiry skills and technology tools, the more they use

these skills and technologies in their classrooms. This could be done by providing teachers the

opportunity to shadow teachers effectively enacting the goals of scientific inquiry with

technology. Obviously, resources are also important to achieve these objectives. The study's

findings show the relationship between computer access and integration. Therefore, schools and

districts should invest more money, time, and energy on technology along with the professional

development that is required to familiarize teachers with their effective use in the classroom.

This implication is also supported by research. Recent studies on science teachers and

inquiry suggest that professional development programs help science teachers develop inquiry-

based skills and applications in the classroom (Wee, Shepardson, Fast, & Harbor, 2007).

According to new evidence in the literature on the nature of professional development programs,









it is suggested that such programs should enable teachers to experience the benefits of scientific

inquiry-based practices as learners through inquiry-based activities (Spector, Burkett, & Leard,

2007) and should focus on student learning and learning difficulties encountered by students

(Lotter, Harwood, & Bonner, 2006) in an inquiry atmosphere. In this respect, the content of the

SIT-TIPPS instrument addresses all of these aspects of professional development. This is

because it interprets essential features of scientific inquiry from a students' perspective and

encourages teachers to look from the same perspective. The way it details the components of

scientific inquiry is a very useful tool for assessing areas where students are having (or could

have) difficulties or inquiry activities should target in the first place. As suggested in the recent

empirical studies mentioned above, the use of this approach could open new doors to alternative

and effective professional development programs for inservice science teachers and methods

courses for preservice science teachers.

Possibilities for Future Research

Although the researcher was able to attract 715 science teachers nationwide to participate

in the survey and collected enough data to run factor analyses reliably, a larger sample size

would have produced more generalizable information. In addition, the researcher did not make

any attempt to differentiate between middle and high school science teachers when interpreting

the results or developing and validating the SIT-TIPPS instrument. A more homogeneous sample

could have been produced different results.

Participation rate from various states were not equal. Some of the states were represented

in the study with more participants. Because socio-economic structures, beliefs and culture are

engraved in the educational life of different states (or even within a state) findings might exhibit

differences in teachers' perceptions and practices regarding the use of technology and scientific

inquiry in science lessons, a different sample structure and state-wide representation might have









resulted in a different response pattern. This might pinpoint an area of further investigation

regarding the use of the SIT-TIPPS instrument.

As mentioned above, although teachers report high percentage of computer presence

within their classrooms and/or in computer labs, no attempt was made to determine the quality of

this technology infrastructure.

As is true with all new instruments, additional research is needed to support the validity

and reliability of the instrument developed in this dissertation study. A randomly chosen, larger

and more representative sample might produce different results.

Conclusion

Literature indicates scientific inquiry is one of the major goals of science curriculum and

science teachers play a key role in achieving this goal as well as in implementing technology to

support the objectives of scientific inquiry-based instruction. The instrument developed in this

study, the SIT-TIPPS, contributes to this body of literature by indicating how well prepared

teachers self-report their ability to use scientific inquiry skills and technology tools in their

classrooms and how often they use them. Results indicate science teachers are below the

satisfactory level in terms of implementing some of the essential features of scientific inquiry

skills and integrating some new generation of technology tools during science lessons. The

results of the study highlight areas in which preservice science teachers and inservice science

teachers need training and professional development. The researcher contends that if science

teachers' lack certain inquiry skills this would cause their students to lack the same skills.

Therefore, these results should be alarming to those who are in charge of shaping the policies,

initial teacher preparation programs, and professional development activities for future science

teachers.









Results also indicate the importance of providing more educational technology training to

teachers continuously throughout their career. The more training they receive the more likely

they tend to use these technologies in their classrooms and, thus, more often they use these

technologies to facilitate scientific inquiry.

In summary, the SIT-TIPPS instrument can be a highly effective tool for science educators,

teachers, researchers, administrators, and policy makers to diagnose problems associated with

science teachers' perceptions and practices regarding the use of technology tools/applications to

attain the goals of scientific inquiry and to elucidate the factors contributing to their low comfort

levels with the uses of inquiry skills and technology tools in science classrooms. Instruments that

specifically targeting technology use for scientific inquiry purposes are limited in number.

Therefore, this study makes a very valuable contribution to this body of literature by developing

and validating the SIT-TIPPS instrument, which is capable of answering a wide range of

questions dealing with science teachers' perceptions and actual classroom practices regarding the

use of technology for scientific inquiry purposes.









Table 5-1. Teachers' self-reported strengths and weaknesses in scientific inquiry.


Level of
preparedness to
support students
to:


Strengths (from highest to lowest)
Collect, organize, and analyze data


Explain cause-effect relationships

Conduct experiments


Frequency of
integration of
skills to support
students to:


Explain cause-effect relationships


Discuss scientific
explanations/ideas/models with
others
Conduct experiments
Collect, organize, and analyze data


Table 5-2. Teachers' strengths and weaknesses in technology tools/applications.
Strengths (from highest to lowest) Weaknesses (from lowest to highest)
Level of Word processing, Email, Internet Data collection telecollaborative
preparedness to searches, presentation software, activities, videoconferencing,
support students spreadsheets, presentation devices teleconferencing, portable Global
to: Positioning Systems, video editing,
podcasts/videocasts, blogs, wikis,
wireless communication devices,
Smart Board/Promethean interactive
boards, webpage design


Frequency of
integration of
skills to support
students to:


Word processing, presentation
devices, presentation software,
Internet searches, Email


Videoconferencing,
teleconferencing, portable Global
Positioning Systems, blogs, data
collection telecollaborative
activities, videoediting, wikis,
podcasts/videocasts, wireless
communication devices,
image/picture editing, virtual
experiences


Weaknesses (from lowest to highest)
Identify their own misconceptions of
science content


Find biases and flaws in their
scientific explanations
Test scientific explanations against
current scientific knowledge
Critique experiments

Find biases and flaws in their
scientific explanations


Test scientific explanations against
current scientific knowledge

Critique experiments









APPENDIX A
RESEARCH STUDY INFORMED CONSENT FORM

















Title of Protocol: Development and Validation of Scientific Inquiry with Technology Teacher
Practices and Perceptions Scale (SIT-TPPS)
1. Principal Investigator:. Ugur Baslanti UFID #: 8735-6150

Degree / Title: Mailing Address:
323 University Vllg S. #1 Gainesville, FL 32603
Doctoral Candidate in Educational
Technology Email Address & Telephone Number:
Department: baslanti@ufl.edu & (352) 846-5282
School of Teaching and Learning
Co-Investigator(s): UFID#: -


Supervisor: UFID#: N/A
Dr. Colleen Swain

Degree / Title: Mailing Address:
Associate Professor & Graduate Coordinator PO Box 117048 School of Teaching & Learning
University of Florida, Gainesville, FL 32611
Department:
School of Teaching and Learning Email Address & Telephone Number:
(352) 392-9191, ext. 264
Date of Proposed Research: From Oct 15, 2007 to August 1, 2008.

Source of Funding (A copy of the grant proposal must be submitted with this protocol if funding is
involved):
None.

Scientific Purpose of the Study:

The purpose of this study is to develop and validate a survey that focuses on scientific inquiry in
science classrooms where technology is used. It is an attempt to address an essential topic both in
science education and educational technology. Even though there are some instruments targeting
scientific inquiry in science education literature, an instrument specifically targeting scientific inquiry
using technology is an area of need in both fields. Such an instrument will be helpful in analyzing the
current practice in schools and colleges of education and in furthering the discussion on how science
teachers can use technology to attain the goals of scientific inquiry. For this purpose, this study intends
to develop a quantitative instrument that measures teachers' perceptions and practices regarding the
use of technology in attaining the goals of scientific inquiry. It connects theory and research from two
fields; science education and educational technology, which can result in a change of daily practice in
science classroom.

Describe the Research Methodology in Non-Technical Language: (Explain what will be done with
or to the research participant.)

The researcher will contact middle and high school level science teachers nationwide via email or
through national and statewide professional teacher organizations to participate in the study by filling










out the online version of the survey, which will be situated in a fire-walled server at the College of
Education. Some of the participants who live within a reasonable distance to the researcher may be
contacted in their schools and invited to fill out a paper based version of the survey. In either case, the
participants will be given a consent form before they take the survey. In the online version, the
participants will be directed to the online survey from an independent URL, which holds the consent
form. Participants will be directed to the survey after they read the consent form and click on 'Press
here to start the survey" button. Participants will not be able to access the contents of the survey
without submitting their consent. All data will be stored in a fire-walled server at the College of
Education. Only the researcher and the supervisors will have access to data. The survey will also
include a section that presents the operational definition of technology as it relates to the study; and a
section that asks participants to complete demographics as well as a professional background
(teaching experience etc.) information. Please see attached form.

Describe Potential Benefits and Anticipated Risks: (If risk of physical, psychological or economic
harm may be involved, describe the steps taken to protect participant.)
There are no known benefits or risks involved on the participant's behalf.



Describe How Participant(s) Will Be Recruited, the Number and AGE of the Participants, and
Proposed Compensation:
Approximately 500 science teachers at the middle and high school level nationwide will be recruited
through the use of electronic mailing as well as personal contacts. The researcher will also contact
some professional teacher organizations to email an invitation note out to their members. All
participants will be at or over eighteen years old. The participants will not be compensated for
participation in the study.



Describe the Informed Consent Process. Include a Copy of the Informed Consent Document:
The informed consent will be presented to the participants on their computer screen prior to
participating in the study and they can print or save the page for their information if they would like to
do so. These consent forms will be secured in a fire-walled server at the College of Education. Please
see attached form.
If the participants wish to complete the paper version of the survey, the paper version of the informed
consent form will be presented to the participants before they agree to participate in the study. The
signed consent forms will be collected by the researcher and secured in a safe place. Please see
attached form.

Principal Investigator(s) Signature: Supervisor Signature:






Department Chair/Center Director Date:
Signature:










INFORMED CONSENT FORM (Online Version)


Dear Science Teacher,

My name is Ugur Baslanti, and I am a graduate student from the School of Teaching and Learning at the
University of Florida. I would like to invite you to participate in an online study that focuses on scientific
inquiry in science classrooms where technology is used. For this purpose, this study intends to develop a
quantitative instrument that measures teachers' perceptions and practices regarding the use of
technology in attaining the goals of scientific inquiry. Such an instrument will be helpful in analyzing the
current practice in schools and colleges of education and in furthering the discussion on how science
teachers can use technology to attain the goals of scientific inquiry.

The procedure will entail the completion of a short demographic page and a short survey. It will take
approximately 25 minutes to complete the survey.


Participation in this project is completely voluntary. You do not have to answer any questions you do not
wish to answer, and you are free to withdraw your consent and to discontinue your participation at any
time without any consequences. Your identity will be kept confidential to the extent provided by law.


There are no risks or direct benefits from your participation in this study apart from reflecting on your
experience. You will not be compensated in any form for your participation in this study. The measure will
be kept secure and only accessible to Ugur Baslanti and his advisors, Dr. Colleen Swain and Dr. Tom
Dana. You will not be associated with your responses, which will be kept secure. Data will be removed
from the server as soon as practicable. Data will not be shared and it will be stored on a highly secure
and firewalled server, which can only be accessed by the research investigator via password-protected
file transfer protocols.

This study has been approved by the University of Florida Institutional Review Board (IRB). For
questions or concerns about your rights as a research participant, contact the UFIRB office, P.O. Box
112250, University of Florida, Gainesville, FL 32611-2250. Phone: (352) 392-0433.

If you have any questions about this research project, please contact Ugur Baslanti, baslanti@ufl.edu or
Dr. Colleen Swain, Room (352) 392-9191 x 264.

I have read the procedure described above and by clicking the below link, I am voluntarily
agreeing to participate in the survey study, and that I have received a copy of this
description electronically.

Press Here To Start The Survey










INFORMED CONSENT FORM (Paper Version)


Dear Science Teacher,

My name is Ugur Baslanti, and I am a graduate student from the School of Teaching and Learning at the
University of Florida. I would like to invite you to participate in a study that focuses on scientific inquiry in
science classrooms where technology is used. For this purpose, this study intends to develop a
quantitative instrument that measures teachers' perceptions and practices regarding the use of
technology in attaining the goals of scientific inquiry. Such an instrument will be helpful in analyzing the
current practice in schools and colleges of education and in furthering the discussion on how science
teachers can use technology to attain the goals of scientific inquiry.

The procedure will entail the completion of a short demographic page and a short survey. It will take
approximately 25 minutes to complete the survey.


Participation in this project is completely voluntary. You do not have to answer any questions you do not
wish to answer, and you are free to withdraw your consent and to discontinue your participation at any
time without any consequences. Your identity will be kept confidential to the extent provided by law.


There are no risks or direct benefits from your participation in this study apart from reflecting on your
experience. You will not be compensated in any form for your participation in this study. Data will be
accessible only to Ugur Baslanti and his advisors, Dr. Colleen Swain and Dr. Tom Dana and stored in a
safe place at the College of Education.


This study has been approved by the University of Florida Institutional Review Board (IRB). For
questions or concerns about your rights as a research participant, contact the UFIRB office, P.O. Box
112250, University of Florida, Gainesville, FL 32611-2250. Phone: (352) 392-0433.

If you have any questions about this research project, please contact Ugur Baslanti, baslanti@ufl.edu or
Dr. Colleen Swain, Room (352) 392-9191 x 264.









APPENDIX B
SURVEY INSTRUMENT











Scientific Inquiry with Technology Teacher Perceptions and Practices Survey
(SIT-TPPS)

Ugur Baslanti
School of Teaching & Learning
University of Florida
baslanti@ufl.edu


Definition of Technology

In this study, technology refers to a range of devices and technological processes specifically
used for teaching and learning purposes in K-12 settings.

For the purposes of this study, the following devices, with examples, comprise the definition of
technologies:

Computers (desktop, laptop)
Presentation devices (such as video projectors, LCD panels)
Smart Board/Promethean interactive boards
Wireless communication devices (such as PDAs, phones, digital response systems)
Computer software (such as Word processors, desktop publishing, spreadsheets,
presentation software, databases, simulations, games, graphing and data analysis
software, video and picture editing software, etc.)
Graphing/scientific calculators
Portable Global Positioning Systems (GPS)
Digital data collection devices (such as pH, pressure and temperature probes, digital
microscope, Navigator systems)
Videoconferencing, teleconferencing
Internet technologies (such as e-mail, websites, online databases, virtual field trips, online
simulations and games, Wikis, blogs, podcasts, videocasts, Google Earth and other
Google tools, online learning communities)
Data collection telecollaborative activities (such as Journey North, SCOPE, Amazing
Space)
Learning management systems (such as WebCT, Blackboard)











Scientific Inquiry with Technology Teacher Perceptions and Practices Survey (SIT-TPPS)


Indicate how well prepared you currently feel
Indicate how often the following happens in your science
to do each of the following in your science .
.struction
classroom with or without technology


Rarely Sometimes Often
Not Almost or all
To suppr s t t: Somewhat Fairly well Very well (e.g.,a few (e.g.,once (e.g.,once
To support students to: adequately Never science
Support sdens o: adequately prepared prepared prepared times a or twice a or twice a lessons
prepared lessons
year) month) week)

Inquiry Skills
1 Ask researchable questions 1 2 3 4 1 2 3 4 5

2 Conduct experiments 1 2 3 4 1 2 3 4 5

3 Collect, organize, analyze data 1 2 3 4 1 2 3 4 5


Identify their own misconceptions of science
content


5 Explain cause-effect relationships 1 2 3 4 1 2 3 4 5


6 Test scientific explanations against current 1 2 3 4 1 2 3 4 5
6 1 2 3 4 1 2 3 4 5
scientific knowledge

Find biases or flaws in their scientific 1 2 3 4 1 2 3 4 5
7 1 2 3 412 34 5
explanations

8 Discuss scientific explanations/ideas/models 1 2 3 4 1 2 3 4 5
8 1 2 3 4 1 2 3 4 5
with others

9 Critique experiments 1 2 3 4 1 2 3 4 5












Indicate how well prepared you currently feel
Indicate how well prepared you currently feel Indicate how often you use the following in your science
to use each of the following in your science instruction
instruction
classroom


Not .Rarely Sometimes Often Almost or all
Not Almost or all
adequately Somewhat Fairly well Very well Never (e.g.,a few (e.g.,once (e.g.,once science
adequately ,Never science
prepared prepared prepared prepared times a or twice a or twice a lessons
prepared lessons
year) month) week)

Technology tools

1 Presentation devices (such as video projectors, 1 2 3 4 1 2 3 4 5
LCD panels)
2 Smart Board/Promethean interactive boards 1 2 3 4 1 2 3 4 5

Wireless communication devices (such as 1 2 3 4 1 2 3 4 5
PDAs, student digital response systems)

4 Graphing/scientific calculators 1 2 3 4 1 2 3 4 5

5 Portable Global Positioning Systems (GPS) 1 2 3 4 1 2 3 4 5

Digital data collection devices (such as pH,
6 pressure and temperature probes, digital 1 2 3 4 1 2 3 4 5
microscopes, Navigator systems)


7 Videoconferencing, teleconferencing 1 2 3 4 1 2 3 4 5

8 Word processing (e.g., Word) 1 2 3 4 1 2 3 4 5
9 Spreadsheets (e.g., Excel) 1 2 3 4 1 2 3 4 5

10 Presentation software (e.g., Power Point) 1 2 3 4 1 2 3 4 5

11 Database software 1 2 3 4 1 2 3 4 5
12 Educational games 1 2 3 4 1 2 3 4 5












Indicate how well prepared you currently feel
Indicate how well prepared you currently feel Indicate how often you use the following in your science
to use each of the following in your science instruction
instruction
classroom


Not .Rarely Sometimes Often Almost or all
Not Almost or all
adequately Somewhat Fairly well Very well Never (e.g.,a few (e.g.,once (e.g.,once science
adequately ,Never science
prepared prepared prepared prepared times a or twice a or twice a lessons
prepared lessons
year) month) week)

13 Virtual experiences (such as Google Earth and 1 2 3 4 1 2 3 4 5
Starry Night, a virtual planetarium)

14 Graphing and data analysis software 1 2 3 4 1 2 3 4 5

15 Video editing 1 2 3 4 1 2 3 4 5
16 Image/picture editing 1 2 3 4 1 2 3 4 5
17 E-mail 1 2 3 4 1 2 3 4 5
18 Webpage design 1 2 3 4 1 2 3 4 5
19 Accessing online databases 1 2 3 4 1 2 3 4 5
20 Internet searches 1 2 3 4 1 2 3 4 5
21 Online simulations 1 2 3 4 1 2 3 4 5

22 Online science games 1 2 3 4 1 2 3 4 5

23 Wikis 1 2 3 4 1 2 3 4 5

24 Blogs 1 2 3 4 1 2 3 4 5

25 Podcasts, videocasts 1 2 3 4 1 2 3 4 5

Data collection telecollaborative activities 1 2 3 4 1 2 3 4 5
(e.g., Journey North, SCOPE, Amazing Space)













Please indicate your level of agreement with the following statements Strongly Disagree Neutral Agree Strongly
Disagree Agree

1 I integrate technology to enable students to ask scientifically oriented questions. 1 2 3 4 5

2 A science teacher should integrate technology to enable students to obtain evidence from various 1 2 3 4 5
sources.

A science teacher should integrate technology to stimulate students to inquire about scientific 1 2 3 4 5
phenomena.

4 I integrate technology to involve students in authentic/real world scientific issues. 1 2 3 4 5

5 I integrate technology to enable students to conduct successful empirical investigations. 1 2 3 4 5


6 A science teacher should integrate technology to enable students to evaluate their proposed 1 2 3 4 5
explanations based on evidence and scientific knowledge.

7 1I integrate technology to enable students to relate new concepts/ideas with their prior knowledge. 1 2 3 4 5


8 A science teacher should integrate technology to facilitate students' collection, organization, and 1 2 3 4 5
analysis of scientific data.


9 I integrate technology to encourage students to compare the ideas they have developed as a result 1 2 3 4 5
of classroom inquiry against scientific facts.

10 I integrate technology to enable students to formulate explanations and coherent arguments to 1 2 3 4 5
address scientifically oriented questions.

I I integrate technology to enable students to make reasoned judgments based on scientific 1 2 3 4 5
evidence.

12 A science teacher should integrate technology to enable students to formulate explanations of 1 2 3 4 5
experimental and observational evidence.

A science teacher should integrate technology to enable students to use technology as
13 instruments to make scientific observations. 1 2 3 4 5












Please indicate your level of agreement with the following statements Strongly Disagree Neutral Agree Strongly
Disagree Agree


14 I1 integrate technology to enable students to evaluate scientific explanations. 1 2 3 4 5


15 I integrate technology to improve students' skills to check their results against existing scientific 1 2 3 4 5
knowledge.

16 A science teacher should integrate technology to enable students to use technology as 1 2 3 4 5
instruments to collect data for conducting scientific experiments.

17 I1 integrate technology to enable students to critique experiments with others. 1 2 3 4 5

18I integrate technology to enable students to discuss their scientific explanations/ideas/models 1 2 3 4 5
with others.

19I integrate technology to stimulate my students' skills to develop hypotheses based on their own 1 2 3 4 5
observations and measurements of scientific phenomena.

20 A science teacher should integrate technology to develop students' knowledge and understanding 1 2 3 4 5
of scientific ideas based on data collected from students.

21 A science teacher should integrate technology to encourage students to gather and use data to 1 2 3 4 5
develop explanations for scientific phenomena.

22 I integrate technology to encourage students to justify their scientific arguments with others. 1 2 3 4 5



23 A science teacher should integrate technology to enable students to make connections between 1 2 3 4 5
their results and existing scientific knowledge.


24 I integrate technology to enable students to identify and overcome their misconceptions of 1 2 3 4 5
science content covered.

25 1 integrate technology to enable students to find biases or flaws in the reasoning connecting 1 2 3 4 5
scientific evidence and explanations.













Please indicate your level of agreement with the following statements Strongly Disagree Neutral Agree Strongly
Disagree Agree


26 A science teacher should integrate technology to enable students to ask scientifically oriented 1 2 3 4 5
questions.

27 I integrate technology to enable students to obtain evidence from various sources. 1 2 3 4 5

28 I integrate technology to stimulate students to inquire about scientific phenomena. 1 2 3 4 5


29 A science teacher should integrate technology to involve students in authentic/real world 1 2 3 4 5
scientific issues.

30 A science teacher should integrate technology to enable students to conduct successful empirical 1 2 3 4 5
investigations.

I integrate technology to enable students to evaluate their proposed explanations based on 1 2 3 4 5
evidence and scientific knowledge.

32 A science teacher should integrate technology to enable students to relate new concepts/ideas 1 2 3 4 5
with their prior knowledge.

33 I integrate technology to facilitate students' collection, organization, and analysis of scientific 1 2 3 4 5
data.

34 A science teacher should integrate technology to encourage students to compare the ideas they 1 2 3 4 5
have developed as a result of classroom inquiry against scientific facts.

35 A science teacher should integrate technology to enable students to formulate explanations and 1 2 3 4 5
coherent arguments to address scientifically oriented questions.

36 A science teacher should integrate technology to enable students to make reasoned judgments 1 2 3 4 5
based on scientific evidence.

37 I integrate technology to enable students to formulate explanations of experimental and 1 2 3 4 5
observational evidence.

38 I integrate technology to enable students to use technology as instruments to make scientific 1 2 3 4 5
observations.












Please indicate your level of agreement with the following statements Strongly Disagree Neutral Agree Strongly
Disagree Agree


39 A science teacher should integrate technology to enable students to evaluate scientific 1 2 3 4 5
explanations.

40 A science teacher should integrate technology to improve students' skills to check their results 1 2 3 4 5
against existing scientific knowledge.

41 I integrate technology to enable students to use technology as instruments to collect data for 1 2 3 4 5
conducting scientific experiments.

42 A science teacher should integrate technology to enable students to critique experiments with 1 2 3 4 5
others.

43 A science teacher should integrate technology to enable students to discuss their scientific 1 2 3 4 5
explanations/ideas/models with others.


44 A science teacher should integrate technology to stimulate students' skills to develop hypotheses 1 2 3 4 5
based on their own observations and measurements of scientific phenomena.


45 integrate technology to develop students' knowledge and understanding of scientific ideas 1 2 3 4 5
based on data collected from students.

46 I integrate technology to encourage students to gather and use data to develop explanations for 1 2 3 4 5
scientific phenomena.

47 A science teacher should integrate technology to encourage students to justify their scientific 1 2 3 4 5
arguments with others.

48 I integrate technology to enable students to make connections between their results and existing 1 2 3 4 5
scientific knowledge.
A science teacher should integrate technology to enable students to identify and overcome their
49 misconceptions of science content covered. 1 2 3 4 5

A science teacher should integrate technology to enable students to find biases or flaws in the
50 reasoning connecting scientific evidence and explanations. 1 2 3 4 5









APPENDIX C
LITERATURE BASE USED TO DEVELOP THE ITEM POOL









Table C-1. Definition and skills table of the essential features of scientific inquiry for the development of the instrument {NRC
(2000). Inquiry and the National Science Education Standards: A guide for teaching and learning. Washington, DC:
National Academy Press}
1. Teacher engages students in scientifically oriented questions

A question robust and fruitful enough to drive an inquiry generates a "need to know" in students, stimulating additional questions
of "how" and "why" a phenomenon occurs. The initial question may originate from the learner, the teacher, the instructional
materials, the Web, some other source, or some combination. The teacher plays a critical role in guiding the identification of
questions, particularly when they come from students. Fruitful inquiries evolve from questions that are meaningful and relevant to
students, but they also must be able to be answered by students' observations and scientific knowledge they obtain from reliable
sources. The knowledge and procedures students use to answer the questions must be accessible and manageable, as well as
appropriate to the students' developmental level. Skillful teachers help students focus their questions so that they can experience
both interesting and productive investigations (NRC, 2000, p. 24).

Teacher and student skills & questions to consider Derived from NRC (2000):
Lending to empirical investigation
Leading to gathering and using data to develop explanations for scientific phenomena
Asking why and how questions
Generating a need to know in students, stimulating additional questions of how and why a phenomenon occurs
Stimulate interest in science
Generate/ask questions that center on objects, organisms, and events in the natural world
Questions connect to the science concepts described in the content standards, guide the identification of questions, help students
focus their questions so that they can experience both interesting and productive investigations

Addendum from the literature:
Seeking information from experts
Seeking for information/researching conjectures
Planning/designing/conducting empirical investigations
Encouraging students to demand more knowledge
Developing an appreciation of how we know
Posing real world questions/dealing with authentic problems
Engaging students in identifying/diagnosing problems/defining and representing a problem










2. Teacher encourages students to give priority to evidence, which allows them to develop and evaluate explanations that
address scientifically oriented questions

Students use evidence to develop explanations for scientific phenomena. They observe plants, animal, and rocks, and carefully
describe their characteristics. They take measurements of temperature, distances, and time, and carefully record them. They
observe chemical reactions and moon phases and chart their progress. Or they obtain evidence from their teacher, instructional
materials, the Web, or elsewhere, to "fuel" their inquiries (NRC, 2000, p. 26).

Teacher and student skills & questions to consider Derived from NRC (2000):
The use of empirical evidence as the basis for explanations about how the natural world works
Getting accurate data from observations of phenomena
Obtaining evidence from observations and measurements taken in natural settings such as oceans, or in contrived settings such as
labs
Using senses, instruments
Gather data over a wide range of naturally occurring conditions and over a long enough period of time so that they can infer what
the influence of different factors might be
The accuracy of the evidence gathered is verified by checking measurements, repeating the observations, or gathering different
kinds of data related to the same phenomenon
The evidence is subject to questioning and further investigation
Students use evidence to develop explanations for scientific phenomena
Students observe plants, animals, and rocks, and carefully describe their characteristics
Taking measurements of temperature, distances, and time, and carefully record them
Students obtain evidence from their teachers, instructional materials, the web, to fuel their inquiry

Addendum from the literature:
Making observations
Reviewing what is already known in light of experimental evidence
Using tools to gather, analyze, and interpret data
Predicting, collecting, and analyzing data
Dealing with data
Developing hypotheses
Designing experiment or study
Observing, exploring, and generating strategies










3. Teacher helps students formulate explanations from evidence to address scientifically oriented questions
Explanations are ways to learn about what is unfamiliar by relating what is observed to what is already known. So, explanations go beyond
current knowledge and propose some new understanding. For science, this means building upon the existing knowledge base. For students,
this means building new ideas upon their current understandings. In both cases, the result is proposed new knowledge. For example, students
may use observational and other evidence to propose an explanation for the phases of the moon; for why plants die under certain conditions
and thrive in others; and for the relationship of diet to health (NRC, 2000, p. 26).
Teacher and student skills & questions to consider Derived from NRC (2000):
Emphasizes the path from evidence to explanation rather than the criteria for and characteristics of the evidence.
Scientific explanations are based on reason. They provide causes for effects and establish relationships based on evidence and logical
argument. They must be consistent with experimental and observational evidence about nature, they respect rules of evidence, are open to
criticism, and require the use of various cognitive processes generally associated with science e.g. classification, analysis, inference, and
prediction, and general processes such as critical reasoning and logic. Explanations are ways to learn about what is unfamiliar by relating what
is observed to what is already known. So, explanations go beyond current knowledge and propose some new understanding. Building upon the
existing knowledge base; building new ideas upon their current understandings: The result is proposed knowledge. (e.g.) students may use
observational and other evidence to propose an explanation for the phases of the moon; for why plants die under certain conditions and thrive
in others; relationship of diet to health
Addendum from the literature:
Forming coherent arguments
The activities of students in which they develop knowledge and understanding of scientific ideas, as well as an understanding of how scientists
study the natural world
Developing epistemological understanding about the nature of science and the development of scientific knowledge
Developing a broader understanding of science
Constructing/formulating explanations
Formulating and testing scientific rules and laws
Interpreting, evaluating
Students describe objects and events
Drawing inferences
Using logical & critical thinking to formulate conclusions
Identifying one's own assumptions
Improving students' critical reasoning and problem solving skills
Enabling students to develop their own ideas by building connections between their existing ideas and new ideas
Uncovering new scientific principles and refining their preexisting understandings
Relating information with prior knowledge and then integrating into larger knowledge structures
Making a reasoned judgment based on appropriate evidence
Dealing with misconceptions









4. Teacher helps students connect explanations to scientific knowledge. Teachers help students evaluate their explanations
in light of alternative explanations, particularly those reflecting scientific understanding

Alternative explanations may be reviewed as students engage in dialogues, compare results, or check their results with those
proposed by the teacher or instructional materials. An essential component of this characteristic is ensuring that students make the
connection between their results and scientific knowledge appropriate to their level of development. That is, student explanations
should ultimately be consistent with currently accepted scientific knowledge (NRC, 2000, p. 27).

Teacher and student skills & questions to consider Derived from NRC (2000):
Evaluation and possible elimination or revision of explanations is one feature that distinguishes scientific from other forms of
inquiry and subsequent explanations
Does the evidence support the proposed explanation?
Does the explanation adequately answer the questions?
Are there any apparent biases or flaws in the reasoning connecting evidence and explanation?
Can other reasonable explanations be derived from the evidence?
Alternative explanations may be reviewed as students engage in dialogues, compare results, or check their results with these
proposed by the teacher or instructional materials
Students make the connections between their results and scientific knowledge appropriate to their level of development. That is,
student explanations should ultimately be consistent with currently accepted scientific knowledge

Addendum from the literature:
Students test their explanations against current scientific knowledge
Proposing answers, explanations, and predictions
Distinguishing alternatives
Evaluating/Considering alternative explanations
Building theories









5. Teacher encourages students to communicate and justify their proposed explanations


Having students share their explanations provides others the opportunity to ask questions, examine evidence, identify faulty
reasoning, point out statements that go beyond the evidence, and suggest alternative explanations for the same observations. Sharing
explanations can bring into question or fortify the connections students have made among the evidence, existing scientific
knowledge, and their proposed explanations. As a result, students can resolve contradictions and solidify an empirically based
argument (NRC, 2000, p. 27).

Teacher and student skills & questions to consider Derived from NRC (2000):

Having students share their explanations provides others the opportunity to ask questions, examine evidence, identify faulty
reasoning, point out statements that go beyond the evidence, and suggest alternative explanations for the same observations
Sharing explanations can bring into question or fortify the connections students have made among the evidence, existing scientific
knowledge, and their proposed explanations. As a result, students can resolve contradictions and solidify an empirically based
argument

Addendum from the literature:
Communicating the results, findings, and ideas
Critiquing experiments
Debating with peers
Communicating and defending hypotheses, models, and explanations
Persuading peers
Argumentation











Table C-2. Essential features of scientific inquiry for the development of the instrument. (1) Teacher engages students in scientifically
oriented questions. (2) Teacher encourages students to give priority to evidence. (3) Teacher helps students formulate
explanations from evidence to address scientifically oriented questions. (4) Teacher helps students connect explanations to
scientific knowledge. (5) Teacher encourages students to communicate and justify their proposed explanations
1 2 3 4 5


posing (real world) questions
(NRC, 1996; Crawford, 2000;
Roth & Michelle, 1998; Keys,
1997)


developing question, defining
and representing problem (Lee
et al., 2004)

examining books and other
sources of information to see
what is already known (NRC,
1996)


seeking information from
experts (Linn et al., 2004)



researching conjectures (Linn
et al., 2004)



searching for information (Linn
et al., 2004)

planning (designing and
conducting) investigations
(NRC, 1996; Linn et al., 2004;
Abd-el-Khalick et al., 2004);
Crawford, 2000; Keys (1997)


making observations (NRC,
1996; Brendzel, 2005; Sherman
& Sherman, 2004; Bodzin, 2005)



reviewing what is already known
in light of experimental evidence
(NRC, 1996)

using tools to gather, analyze,
and interpret data (NRC, 1996;
Brendzel, 2005; Keys, 1997)

developing hypothesis
(Wichmann et al., 2003;
Crawford, 2000; Sherman &
Sherman, 2004; Bodzin, 2005)


dealing with data (Crawford,
2000)


predicting, collecting and
analyzing data (Sherman &
Sherman, 2004; Bodzin, 2005)



designing experiment or study
(iTee et al 2004)


the activities of students in which
they develop knowledge and
understanding of scientific ideas,
as well as an understanding of
how scientists study the natural
world (NRC, 1996)

forming coherent arguments
(Linn et al., 2004)


develop epistemological
understandings about the nature
of science and the development
of scientific knowledge (Abd-el-
Khalick et al., 2004)
formulating explanations (Abd-
el-Khalick et al., 2004; Keys,
1997)

using logical and critical
thinking to formulate
conclusions (Sherman &
Sherman, 2004; Bodzin, 2005)

formulate and test scientific rules
and laws (Looi, 1998)



an understanding of the nature of
science (Sherman & Sherman
(2004)


proposing answers, explanations,
and predictions (NRC, 1996)



distinguishing alternatives (Linn
et al., 2004)


test those explanations against
current scientific knowledge
(NRC, 1996)

considering alternative
explanations (NRC, 1996;
Schwab, 1960, cited in NRC,
2000, p.21)


building theories (Crawford,
2000)


evaluate alternative explanations
(Sherman & Sherman, 2004;
Bodzin, 2005).


communicating the results,
findings (their ideas) (NRC,
1996; Sherman & Sherman,
2004; Bodzin, 2005).


critiquing experiments (Linn et
al., 2004)



debating with peers (Linn et al.,
(2004)


communicating, and defending
hypothesis, models, and
explanations (Abd-el-Khalick
et al., 2004)


persuading peers (Roth &
Michelle, 1998)


discussion-verbal interactions
with peers and teacher-
(Westbrook, 1997)



argumentation (Zembal-Saul &
Land 2002- McDonald 2004)











Table C-2. Continued.
engaging students in the
intentional process of
diagnosing problems (Linn et
al., 2004; Lee et al., 2004)

relevant inquiry skills such as
identifying problems (Abd-el-
Khalick et al., 2004)

generating research questions,
(Abd-el-Khalick et al., 2004)

engaging in inquiry helps
students develop an
appreciation of "how we know"
(Sherman & Sherman, 2004)

encouraging them to demand
more knowledge (Edelson et
al., 1999)

applying their scientific
understanding in the pursuit of
research questions (Edelson et
al., 1999)


authentic problems (Flick,
1997)


observing, exploring and
generating strategies, organizing,
analyzing (Lee et al., 2004)


interdisciplinary contexts (Myers
& Botti, 1997)


interpreting, and evaluating (Lee
et al., 2004)


debates about the use of
evidence (Schwab, 1960, cited
in NRC, 2000, p.21)


developing a broader
understanding of science
(Bodzin, 2005)


students describe objects and
events (NRC, 1996)


construct explanations (NRC,
1996)



identifying assumptions (NRC,
1996)



use critical and logical thinking,
(NRC, 1996)


improves their critical reasoning
and problem solving skills
(Bodzin, 2005)

enables students to develop their
own ideas by building
connections between their
existing ideas and new ideas
(Berge & Slotta, 2005)

drawing inferences (Crawford,
2000)











Table C-2. Continued.
uncovering new scientific
principles and refining their
preexisting understandings
(Edelson et al., 1999)

relating information with prior
knowledge and then integrating
into larger knowledge structures
(Myers & Botti, 1997)

making a reasoned judgment
based on appropriate evidence
(Lee at al., 2004)

deal with misconceptions
(Cognition and Technology
Group at Vanderbilt, 1992;
Nickerson, 1995)









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BIOGRAPHICAL SKETCH

Ugur Baslanti was born in Istanbul, Turkey, in 1975. He received his Bachelor of Science

degree in chemistry education and his Master of Science degree in secondary school science and

mathematics education with an emphasis on gifted education from Bogazici University in

Istanbul. He worked as a teaching and research assistant at this university for three years, but

also taught chemistry, physics, and mathematics part-time in a private educational institution. In

2002 and 2004 he taught chemistry in a summer program for the gifted organized by the Center

for Talented Youth at the Johns Hopkins University.

He started working toward his doctorate degree in educational technology in the School of

Teaching and Learning at the University of Florida in 2002. During his doctoral studies Ugur

taught Instructional Technology Lab and Technology Integrated in Mathematics Curriculum to

undergraduate and graduate pre-service teachers for two years and served as a research assistant

on a variety of educational grants including Preparing Tomorrow's Teachers to Use Technology

(PT3) and Classroom Connectivity. His areas of interest are gifted underachievers, teacher

education, and the integration of technology into mathematics and science instruction.





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1 DEVELOPMENT AND VALIDATION OF SCIE NTIFIC INQUIRY WITH TECHNOLOGY: TEACHERS PERCEPTIONS AND PRACTICES SCALE (SIT-TIPPS) By UGUR BASLANTI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Ugur Baslanti

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3 To my wife Tezcan and my daughter Berra

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4 ACKNOWLEDGMENTS I would like to express my gr atitude to all the people w ho had supported m e during this dissertation process. First of all, I would like to thank my wife, Tezcan; and my family members and friends for their unwavering support and fo r being a constant source of encouragement during this journey. Their presen ce and love led me this far. I thank my committee members for their support and guidance. Dr. Colleen Swain and Dr. Thomas Dana continually supported me with their expertise and directed my interest toward my topic and provided guidance and revision suggestions. I am grateful for their professional and personal insights and consistent encouragement. I thank Dr. Kara Dawson, Dr. David Miller, and Dr. Rose Pringle for their invaluable input and fe edback on my work. I also wish to express my gratitude to Dr. Troy Sa dler, Dr. Dina Mayne, and Dr. Karen Irving for their contribution to my dissertation study with thei r expertise during the cont ent validation process. This research would not have come to fruition without the economic support of my department, the School of Teaching and Learning (STL) in the College of Education. I very much appreciate the professional and financia l support I have received from my department during my doctoral study. Of course, this research would not have b een possible without the cooperation of the science teachers from all corners of the United Stat es of America. Special thanks go to over forty national science teacher associ ations who distributed my i nvitation to their members. Finally, many people contributed to my educational background. It is impossible to name each of them individually, but I would like to express my gratitude and love to these people who helped me shape the educator and researcher that I have become.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF ABBREVIATIONS........................................................................................................ 10 ABSTRACT...................................................................................................................................11 CHAP TER 1 INTRODUCTION..................................................................................................................13 Statement of the Problem....................................................................................................... .14 Purpose of the Study........................................................................................................... ....15 Significance of the Study........................................................................................................16 Research Questions............................................................................................................. ....17 Overarching Questions....................................................................................................17 Supporting Questions...................................................................................................... 18 Theoretical Framework of the Study...................................................................................... 18 Definition of Terms................................................................................................................21 Delimitations of the Study..................................................................................................... .22 Limitations of the Study....................................................................................................... ..22 Organization of Chapters........................................................................................................23 2 REVIEW OF THE LITERATURE........................................................................................ 24 Inquiry: A History and Evolving Definition ...........................................................................24 Why Inquiry?..........................................................................................................................26 Scientific Inquiry and Teachers.............................................................................................. 27 Technology and Science Education........................................................................................ 32 Technology and Learning................................................................................................ 36 Models of Technology Use............................................................................................. 37 Technology in Science Instruction.................................................................................. 38 Technology and Inquiry.................................................................................................. 39 Multiple Examples of Technology Used to Reach Goals of Science Inq uiry................. 41 Summary.................................................................................................................................45 3 METHODOLOGY................................................................................................................. 46 Introduction................................................................................................................... ..........46 Research Questions............................................................................................................. ....46 Overarching Questions....................................................................................................47 Supporting Questions...................................................................................................... 47 Technology in the SIT-TIPPS................................................................................................ 47

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6 Data Collection.......................................................................................................................48 Instrumentation and Instrume nt Developm ent Procedures..................................................... 50 Content Validation Process:...................................................................................................54 Statistical Techniques Used to Answer Study Research Questions ....................................... 57 Item Analysis and Reliability................................................................................................. 59 Factor Analyses......................................................................................................................59 Sample Size.....................................................................................................................59 Exploratory Factor Analysis............................................................................................ 60 Multiple Regression............................................................................................................ ....64 Summary.................................................................................................................................66 4 PRESENTATION AND ANALYSIS OF DATA.................................................................. 84 Study Research Questions......................................................................................................84 Overarching Questions....................................................................................................84 Supporting Questions...................................................................................................... 84 Demographic Reporting of the Sample.................................................................................. 85 Demographic Characteristics........................................................................................... 85 Teaching Experience....................................................................................................... 85 Computer Access and Knowledge.......................................................................................... 87 Computer Access............................................................................................................. 87 Source of Computer Knowledge..................................................................................... 88 Answering Research Question 1............................................................................................. 89 Answering Research Question 2............................................................................................. 91 Level of Use of Inquiry Skills and Technology Tools.................................................... 91 Correlational Analysis..................................................................................................... 92 Multiple Re gression.........................................................................................................96 Further Analyses............................................................................................................... ......98 A Summary of Results in terms of Study Research Questions............................................103 5 FINDINGS, CONCLUSIONS, IMPLICATIONS AND SUGGESTIONS FOR FUTURE RESEARCH ......................................................................................................... 117 Overarching Questions.........................................................................................................118 Supporting Questions........................................................................................................... .118 The SIT-TIPPS Instrument...................................................................................................119 Summary of Findings........................................................................................................... 120 Characteristics of Science Teachers..............................................................................121 A Snapshot of Science Teachers Scie ntific Inquiry and Tech nology Skills................ 122 Source of Computer Knowledge................................................................................... 123 Additional Demographic Findings................................................................................ 124 Study Implications................................................................................................................125 Possibilities for Future Research.......................................................................................... 131 Conclusion............................................................................................................................132

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7 APPENDIX A RESEARCH STUDY INFORMED CONSENT FORM..................................................... 135 B SURVEY INSTRUMENT....................................................................................................140 C LITERATURE BASE USED TO DEVELOP THE ITEM POOL ...................................... 149 LIST OF REFERENCES.............................................................................................................158 BIOGRAPHICAL SKETCH.......................................................................................................169

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8 LIST OF TABLES Table page 3-1 Descriptive statistics and reliabi lity index of the SIT-TIPPS instrum ent.......................... 673-2 Item analysis results...........................................................................................................683-3 Parallel analysis: PAF/common factor analysis & ra ndom normal data generation (N=557, Nvariables=50)....................................................................................................693-4 Breakdown of 50 items into categories and related factors............................................... 693-5 KMO and Bartlett's Test....................................................................................................703-6 Communalities.............................................................................................................. .....713-7 Total variance explained: PAF with promax rotation........................................................ 723-8 Pattern matrix............................................................................................................. ........733-9 Structure matrix........................................................................................................... ......753-10 Factor correlation matrix................................................................................................. ...763-11 Communalities............................................................................................................. ......773-12 Total variance explained.................................................................................................. ..783-13 Pattern matrix............................................................................................................ .........793-14 Structure matrix.......................................................................................................... .......813-15 Factor correlation matrix................................................................................................. ...823-16 Regression analysis summary fo r teachers perceptions factor......................................... 823-17 Regression analysis summary for teachers practices factor............................................. 823-18 Regression analysis summary for frequency of using inquiry skills................................. 833-19 Regression analysis summary for frequency of using technology tools............................ 834-1 Participant characteristics based on gender (n=598).......................................................1074-2 Participant characteristics based on race/ethnicity (n=590)............................................. 1074-3 Distribution of participan ts based on states (n=601).......................................................108

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9 4-4 Number of years of teaching experience: Categorized (n=583) ......................................1094-5 Grade levels taught by science teachers (n=715)............................................................. 1094-6 Courses taught by sc ience teachers (n=715).................................................................... 1094-7 Science teachers frequency of use of scientific inquiry skills........................................ 1104-8 Science teachers frequency of use of technology tools..................................................1114-9 Science teachers level of prepar edness of scientific inquiry skills................................. 1124-11 Pearson Product-Moment Co rrelation between variables................................................1144-12 The number of computers in classrooms......................................................................... 1154-13 Percent of teachers reporting ta king educational technology classes.............................. 1154-14 T-test results for subscales based on presence of science lab in classroom..................... 1154-15 T-test results for subscales based on presence of computer lab in school....................... 1164-16 ANOVA results between subscal es and selected variables............................................ 1164-17 Summary of Post Hoc (Tukey) ANO VA results for significant differences................... 116C-1 Definition and skills table of the essentia l features of scientific inquiry for the development of the instrument......................................................................................... 150C-2 Essential features of scientific inquir y for the development of the instrument............... 155

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10 LIST OF ABBREVIATIONS AAAS American Association for the Advancement of Science NRC National Research Council SIT-TIPPS Scientific Inquiry with Technol ogy: Teachers Perceptions and Practices Survey

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11 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DEVELOPMENT AND VALIDATION OF SCIE NTIFIC INQUIRY WITH TECHNOLOGY: TEACHERS PERCEPTIONS AND PRACTICES SCALE (SIT-TIPPS) By Ugur Baslanti August 2008 Chair: Colleen Swain Cochair: Thomas Dana Major: Curriculum and Instruction This study was based on the premise technolo gy can enhance the quality of scientific inquiry-based instruction and explored the ques tion of how science teach ers use technology to attain the goals of scientific inquiry-based instruction. The inst rument developed in this study (SIT-TIPPS) can serve as a useful tool for science teachers and science teacher educators regarding the integration of tec hnology in scientific inquiry-bas ed learning environments. For this purpose, 715 middle and high school science t eachers were surveyed for their perceptions about implementing scientific inquiry using technology and the degree to which they use technology for such a goal. Results explored whethe r relationships exist and the degree of these relationships among a set of variab les related to teachers use of technology for scientific inquiry purposes. Study results supported the validity and reli ability of the SIT-TIPPS instrument. It also demonstrated significant relationships among teachers self-reported perceptions and practices regarding the use of technology to attain the go als of scientific inqui ry and the level of preparedness and frequency of using inquiry ski lls and technology tools in instruction. Analysis of the survey responses also documented science t eachers have a variety of skills in using inquiry skills in instruction such as supporting student s to explain cause-effect relationships and to

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12 discuss scientific explanations/models/ideas with others. However, they report low comfort level and use of inquiry skills such as supporting st udents to identify their own misconceptions of science content; to find biases and flaws in thei r scientific explanations; to test scientific explanations against current sc ientific knowledge; and to critique experiments. Regarding technology tools science teachers report more frequent use of most commonly used technology applications/tools such as word processing, sp readsheets, presentation software, presentation devices, email, and Internet searches. On the other hand, in terms of technology tools/application, the science teac hers indicate low comfort level with and the use of new and/or unfamiliar forms of technologies such as blogs a nd data collection teleco llaborative activities. Findings of the study have valuable implications for teacher educators, science teachers, administrators, practitioners, and educationa l policy makers. The SIT-TIPPS instrument can make valuable contributions to preservice teacher education and inservi ce teacher trainings. For example, the perceptions and practic es factors of the instrument can be used to diagnose the gap between teachers perceptions a nd actual classroom practices re garding the use of technology for scientific inquiry purposes. The se ctions that measure science t eachers comfort levels and the degree to which they integrate inquiry skills and technology tools can help identify what skills teacher candidates or classroom teachers are missing. This, in turn, can help colleges of education and teacher-training institutes design more effective science education programs that nurture teachers skills and knowledge base for implementing technology and scientific inquiry in their classrooms.

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13 CHAPTER 1 INTRODUCTION There have been m any efforts throughout the history of science education to improve teaching and learning in elementary and s econdary schools (Abrams, 1998). The National Research Council and American Association for the Advancement of Scie nce have contributed to efforts by publishing reports such as the National Science Education Standards (1996), Science for All Americans (1990), and Benchmarks for Science Literacy (1993) that emphasized student learning, the nature of sc ience, science literacy, and sc ientific inquiry. Despite these efforts, current science curricula in the United States and many other countries has failed to prepare students for the kinds of experiences they will need to become successful science learners (Linn, Davis, & Bell, 2004). Many school science cu rricula still encourage the philosophical mindset of the 20th century (Bencze & Hodson, 1999) and teachers, scientists, and curriculum developers hesitate to give stude nts freedom to investigate their own problems (Abrams, 1998). The National Research Council ( 2000, p. 17) reported teachers were still using traditional, didactic methods and students were mastering disconnected facts in lieu of broader understandings, critical reasoning, and problem-solving skills. In addition, the National Commission on Math ematics and Science Teaching for the 21st Century (2005) reports children are losing the ability to respond not just to the challenges already presented by the 21st century but to its potential as well (p. 4) These reports clearly show todays students will need to acquire a new set of skills for the 21st century (Kozma & Schank, 1998). They need to be prepared for a ra pidly changing world by: (a) learning how to think about their knowledge base and to apply it flexibly and re sponsibly (Wiske, et al., 2005) and (b) learning how to use a variety of tools to search vast amount of information, generate new

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14 data, analyze and interpret data and transfor m findings into new meanings, and communicate ideas (Kozma & Schank, 1998). In educational technology, one goa l of this field is to iden tify effective ways to use technology tools for higher-order th inking that mesh with the assu mptions of scientific inquiry (NRC, 1996). Research indicates technology offe rs opportunities to tr ansform inquiry-based science teaching and learning (Edelson, Gordi n, & Pea, 1999; Alagic, Yeotis, Rimmington, & Koert, 2003; Linn et al., 2004; Williams, Linn, Ammon, & Gearhart, 2004). Some researchers point out the need for using technology to prom ote scientific inquiry in science classrooms (Pederson & Yerrick, 2000; Cari n & Bass, 2001; Williams, et al ., 2004). Edelson et al., (1999) believed all the fundamental prope rties of computing technologies offer benefits for inquirybased learning in the sciences. Researchers note technology is currently being used in science classes to help build a community of learners (Bransford, Brown, & Roney, 1999; Dede, 2000); engage students in problem-rich environments to explore and so lve problems (Bransford, et al., 1999); develop math or science concepts as well as collaborative skills (Stables, 1997); d eal with misconceptions (Cognition and Technology Groups at Vanderbilt, 1992; Nickerson, 1995); master more complex subjects via rich interactions using external resources (Dede, 2000); and help students generate and test hypotheses and build expl anations of scientific phenomena (Spitulnik, Stratford, Krajcik, & Soloway, 1998). However, there is scant litera ture available answeri ng the question of how science teachers use technol ogy to attain the goals of scientific inquiry. Statement of the Problem Scientific inquiry has been an overarching goal of science education (AAAS, 1993; NRC, 1996; Flick, 1997; Crawford, 1997; E delson, et al., 1999) and a central strategy for teaching science (NRC, 1996) for decades. Although there are certain instructional me thods and strategies

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15 that help teachers implement scientific inquiry in their classrooms, the use of technology can also play a significant role in meeting the goals of scientific inquiry. According to the National Research Council (1996), a goal for using educationa l technology in the classroom is to identify effective ways to use technology tools for higherorder thinking that mesh with the assumptions of scientific inquiry. The effec tive uses of educational technologi es also have the potential to transform inquiry-based science teaching and learning (Edelson et al., 1999; Alagic et al., 2003; Linn et al., 2004; Williams et al., 2004). When it comes to technology integration, teachers play a key role (Scheffler & Logan, 1999). Yet, research indicates teachers lack good instructional frameworks for effective implementation of technology into the curriculum (Bitner & Bitner, 2002). In addition to teachers lack of experience in implementing technology into their curricula, there is little literature answering the question of how scienc e teachers use technology to succeed in enacting the goals of scientific inquir y. A trio of factors including background knowledge with regard to science content, inquiry-oriented instruct ion, and technology (Pedersen & Yerrick, 2000; Williams et al., 2004) make inquiry teaching mo re challenging for teachers. Moreover, the pressure from external forces such as parents, administrators and society to use technology in the classroom contributes to this i ssue and makes teachers feel compe lled to use technology in their classrooms without any clear agenda (Wiske, Franz, & Breit, 2005). Purpose of the Study This study, which was based on the prem ise technology could enhance the quality of scientific inquiry-based instruction, attempted to answer the question of how science teachers use technology to attain the goals of scientific inquiry-ba sed instruction. The instrument developed in this study can serve as a useful tool for sc ience teachers and science teacher educators in the integration of technology in scientific inquiry-based le arning environments. For this purpose, the

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16 study investigated science teachers self-report ed perceptions about implementing scientific inquiry using technology and explored the degree to which they use technology for such a goal. It also examined whether relationships exist, and the degree of these relationships, among a set of variables related to teachers use of technolog y for inquiry purposes. Significance of the Study This dissertation study focused on how technology is used when supporting scientific inquiry instruction in K-12 science classroom s through the development of an instrument about scientific inquiry and the use of technology. This study attempted to address an essential topic both in science education and educational te chnology. Even though there are some instruments targeting scientific inquiry in science educat ion literature (Bodzin & Beerer, 2003; Brandon & Taum, 2005; Smolleck, Zembal-Saul, & Yoder, 2006), an instrument specifically targeting scientific inquiry in science cl assrooms where technology is used is an area of need in both fields. Such an instrument is helpful in analyz ing the current practice in schools and colleges of education and in furthering the discussion on how science teachers can use technology to attain the goals of scientific inquir y. For this purpose, the resear cher developed a quantitative instrument to measure teachers self-reported pe rceptions and practices regarding the use of technology in attaining the goals of scientific inquiry. It connected theory and research from two fields, science education and educational tec hnology, which can result in a change of daily practice in science classroom. The findings of this study also fused various aspects of scientific inquiry applicable to educational technology to form a common ground to assess teachers understandings and uses of science inquiry utilizing technology. In addition, this instrument has the possibility of being used as a useful tool to assist scie nce teachers in the integration of technology into scientific inquirybased instruction. Although science teacher educ ation programs have begun to design teaching

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17 models that infuse technology, re search characterizing teachers in structional use of educational technology after completing such programs is lim ited (McNall, 2004). The instrument developed in this study and its results also contribute to this body of literature and help educators develop strategies toward enriching initial teacher preparation and professional development opportunities. In summation, such an instrument could be helpful by providing insight into the current practice in schools, examine factors that facilitate or hinder the use of technology for scientific inquiry purposes, provi de instructional strategies to enhance inquiry-based science teaching that utilizes technology, and build upon the established knowle dge base in science education and educat ional technology. Research Questions This study had two overarching research que stions and five supporting questions. The m ain questions focused on how teachers use te chnology to implement the goals of scientific inquiry and the relationships betw een their self-reported perceptions and practices regarding this implementation. The supporting qu estions, however, articulated th ese relationships by addressing some teacher demographics and background/professional development variables as well as the level and frequency of their prep aredness and use of certain inqui ry tasks and technology tools. While the total scores obtained from the surv ey and the relationships between perception and practice items in the survey were utilized to help answer the overarching questions of the study, relationships obtained from suppor ting questions also contributed to answering these overarching questions. Through the creation of SIT-TIPPS, the study addressed the following research questions: Overarching Questions 1. How are teachers using technology to im plement the goals of scientif ic inquiry in their classrooms?

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18 2. What are the relationships between teachers pe rceptions and practices regarding the use of technology to attain the goals of scientific inquiry? Supporting Questions What are the relationsh ips between teachers se lf-reported perceptions and practices regarding the use of technology to atta in the goals of scientific inquiry in terms of: Teacher demographics and teacher backgr ound/professional development variables? How often do they support students to engage in certain inquiry sk ills in their science classrooms? How often do they use certain technol ogy tools in their science classrooms? How prepared do teachers feel to support students to engage in certain inquiry skills in their science classrooms? How prepared do teachers feel to use certain technology tools in their science classrooms? Theoretical Framework of the Study Essential to the developm ent of any instrument is a solid theoretical foundation from which issues and concepts are derived (Devellis, 2003). Because this study is interweaving two academic areas it becomes necessary to blend tw o established knowledge bases into a theoretical foundation on which the instrument created in this study, SIT-TIPPS, can stand. A meta-analysis research study on inquiry-based science instruction has established that scientific inquiry-based teaching and learning prom otes students abilities to diagnose problems, critique experiments, distinguish alternatives, plan investigations research conjectures, search for information, debate with peers, seek informa tion from experts, and form coherent arguments (Linn et al., 2004). However, the studies explored in the meta-analysis lack a common definition for scientific inquiry-based teaching to guide the successful implementation of inquiry-based methods in classrooms using technology. By adopting the National Research Councils (NRC) inquiry standards and meshing these standards with research findings in the areas of scientific inquiry and educational technology, a theoretical framework is formed to effectively base the

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19 integration of scientific inquiry and technology in science classr ooms. The theoretical framework for this study is built on the inquiry standard s developed by the National Research Council in Inquiry and National Science Education Standards (NSES, 2000) and supported by the related literature. The following essential features of classroom inquiry as described in the National Science Education Standards were used as a guideline to develop the Scientific Inquiry with TechnologyTeachers Perceptions and Practices Survey (SIT-TIPPS) instrument as part of this study: 1. Learners engage in scientif ically oriented questions 2. Learners give priority to ev idence in responding to questions. 3. Learners formulate explanations from evidence. 4. Learners connect explanations to scientific knowledge, and 5. Learners communicate and ju stify explanations (p.25). The National Science Education Standards (2000) define inquiry-based teaching as experiences that help students acquire concepts of science, skills and abilities of scientific inquiry, and understandings about sc ientific inquiry. The research base used in the Standards to identify the essential features of inquiry in science classrooms is grounded in research on learning and the kinds of learning environments that promote learning. For this purpose, the NRC gives a detailed account of pioneering ideas that laid the groundwork for essential features of classroom inquiry. These features were used to determine whether scie ntific inquiry is an integral part of the classr oom. These features are: Herbarts (1901) ideas about teac hing that include starting with students interest in the natural world and in in teraction with others. Deweys (1910) expansion of the idea of reflective experience in which students begin with a perplexing situation, formulate a tentative interpretati on or hypothesis, test the hypothesis to arrive at a solution, and act upon the solution. Dewey emphasized processes and methods of science and the notion of science as a wa y of thinking and an attitude of mind. Schwabs (1960, 1966) emphasis on science as conceptual structures revi sed as the result of new evidence. Schwab suggested teachers: (a ) provide laboratory experiences before introducing students to the formal explanations of scientific concepts and principles, (b)

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20 enable students to build and/or refine explanations from evidence, (c) allow students to pose questions, discover relationships, and propose sc ientific explanations based on their own investigations, (d) enable students to build an understanding of what constitutes scientific knowledge and how scientific knowledge is prod uced by providing them with readings and reports about scientific research in which they can discuss the details of the research or read about alternative explanations, experiments, and assumptions. Piagets (1975) theory of development in whic h he proposed learners adapt or change their cognitive structures when they experience a discrepancy between their existing ideas and ideas they observe in their environments. Atkins and Karplus (1962) id ea of the learning cycle emphasi zing the roles of exploration, invention, and discovery in the t eaching and learning processes. Bransfords et al., (1999) resear ch findings on how people learn. These findings suggest: (a) understanding science is more than knowing facts; st udents should understand the major concepts, build a strong base of supporting fact ual information, and know how to apply their knowledge effectively, (b) students build new knowledge and understanding on what they already know and believe, (c) students formulate new knowledge by modifying and refining their current concepts and by adding new concepts to what they already know, (d) learning is mediated by the social environment in which l earners interact with others, (e) effective learning requires that students take control of their own le arning, (f) the ability to apply knowledge to novel situations, that is, transfer of learning, is affected by the degree to which students learn with understanding Bransfords et al., study also suggested that learning should take place in a learner, knowledge assessment, and community-centered environments. Guided by this theoretical framework about th e essential features of classroom inquiry outlined in the National Science Education Standards, the SIT-TIPPS instrument developed in this study included items targeting: (a) teachers use of a variety of technology tools in their classrooms, (b) their self-reported perceptions re garding the use of technology for scientific inquiry proposes in instruction, and (c) their practices regarding the use of technology for scientific inquiry proposes in in struction. As noted earlier, in addition to this framework, the researcher also made use of th e related literature regarding best practices in teaching from both science education and educationa l technology fields when constructing the items for the survey. The essential features of the National Sc ience Education Standa rds dovetail into the literature allowing teacher perceptions and practices regarding the use of technology in scientific

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21 inquiry-based instruction were examined. This framework, in conjunction with the two bodies of knowledge, allowed the researcher to develop th e constructs and corresponding items for the instrument. Definition of Terms The following term s can be useful in unde rstanding the nature of this study. Scientific inquiry refers to the activities of student s in which they develop knowledge and understanding of scientific ideas, as well as an u nderstanding of how scien tists study the natural world (NRC, 1996, p.23). Technology refers to the use of a range of devices and technological processes specifically for teaching and learning in K-12 settings. For the purposes of the present study, the following technologies are included in the definition: 1. Computers (desktop, laptop) 2. Presentation devices (such as video projectors, LCD pa nels, overhead projectors) 3. Whiteboard/Smartboard 4. Wireless communication devices 5. Computer software (such as Word pro cessors, desktop publishing, spreadsheets, presentation software, databases, simula tions, games, graphing and data analysis software, video and picture editing software) 6. Graphing/scientific calculators 7. Handhelds, GPS (Global positioning systems) 8. Digital data collection devices (such as pH, pressure and temperature probes, digital microscope, Navigator systems) 9. Videoconferencing, teleconferencing 10. Internet technologies (such as e-mail, websites, online databases, virtual field trips, online simulations and games, Wikis, blogs, online learning communities)

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22 Delimitations of the Study This study focuses on th e limited domain of science teachers who teach at the middle and/or high school level. Therefor e, it cannot be generalized to other subject areas and/or other levels of schools such as elementary schools. The study concentrates on middle and high school science teachers self-reported perceptions and practices regarding th e use of technology for scientific inquiry purposes, as well as their comfort levels an d uses of inquiry skills and technology tools/applications in classrooms. The term, technology, is limited to the definition described above and the concept of scientific inqu iry is restricted to Na tional Research Councils five essential features of scie ntific inquiry as outlined in the National Science Education Standards. For this reason, the findings of the st udy may not be generalized to other features of scientific inquiry, as well as other fo rms of technology tools/applications. Only middle and high school science teachers who teach in the United States of America constituted the sample. The instrument developed in this study and the findings obtained from its administration may not be genera lized to science teachers who ar e teaching in other countries. Limitations of the Study In this study, the term technology is limited to the definition described above and the concept of scientific inquiry is restricted to National Research Councils five essential features of scientific inquiry as ou tlined in the National Science Educat ion Standards. For this reason, the findings of the study may not be ge neralized to other features of scientific inquiry, as well as other forms of technology tools/applications. The participation in this st udy was voluntary and by conveni ence. There is always the possibility that the response structure from science teachers who volunteered may differ from those who did not volunteer or were not contacted.

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23 All of the science teachers, who participated in this study, re ceived an invitation from the researcher in electronic format vi a direct email or through membersh ip to professional listservs. Hence, the sample profile does not involve t hose teachers who do not use email or have no access to Internet-based professional organizations This could limit the generalizability of the results to many middle and high school science teachers in the U.S. Organization of Chapters Chapter 1 provides inform ation on the role of technology and scientific inquiry in science education. This chapter identif ies the significance and purpose of the study, the theoretical framework and research questions. Chapter 2 reviews the literature on scientific inquiry and the integration of technology in science education. Hi ghlighted are the importance of the relationship between the goals of science e ducation and the potent ial technology can offer to attain these goals. Chapter 3 provides a detailed descripti on of the study design, instrument development procedure and methodology used in conducting the re search. Chapter 4 inte rprets and discusses the data. Chapter 5 summarizes the implications of the findings.

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24 CHAPTER 2 REVIEW OF THE LITERATURE This rev iew of literature provides a brief but through presentation of salient literature and research in the following areas : scientific inquiry, scientific inquiry and technology, critical concepts in the integration of technology in scie ntific inquiry-based instruction, and guidelines for instrument development. Inquiry: A History and Evolving Definition It is nothing short of a miracl e that the modern methods of in struction have not yet entirely strangled the holy curiosity of inquiry. Albert Einstein The miracle Einstein was referring to in the above quote seems to have become a reality at least in science educa tion. As DeBoer (1991) co ntended, the goal of scie nce education since the late 1950s has been inquiry. This term became a central strategy for teaching science (NRC, 1996), and is held in high regard among scie nce educators (Sherman & Sherman, 2004). Its importance in science education as a central goal has a long and established history dating back to the works of Dewey at the beginning of the tw entieth century and Schwab at the turn of the century (Zembal-Saul & Land, 2002; Abd-El-Kha lick, BouJaoude, Duschl, Hofstein, Lederman, Mamlok, Niaz, Treagust, & Tuan, 2004; Craw ford, 1997; Edelson et al., 1999; National Research Council, 2000, Bodzin & Beerer, 2003). Th e role of inquiry in science education remains a perennial term and continues to be strongly emphasized by current reform reports or documents in the United States (Crawford, 1997 ; Flick, 1997; Abd-el-Kha lick et al., 2004) such as the National Science Education Standard s (NRC, 1996; 2000) and Benchmarks for Science Literacy (AAAS, 1993). Recommendations include th e opportunity for student s to use scientific inquiry and develop inquiry skills (NRC, 1996) a nd for teachers to establish inquiry-oriented

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25 learning environments (Carin & Bass, 2001) that result in better rete ntion and understanding of the concepts (Brendzel, 2005). Inquiry has been a broadly defined and charac terized construct in science education (Looi, 1998; Windschitl, 2003). Although its definition varied among scienc e educators, its presence (Newman, Abell, Hubbard, McDonald, Otaala, & Martini, 2004) and importance was always accepted and promoted. Carnes (1997) identified th ree broad classifications of the definition of inquiry as: science processes or a scientific me thod; scientific processes and content knowledge; and scientific processes, attit udes, and knowledge. Looi (1998) al so identified three categories from a different perspective. His categories of in quiry include: active involve ment of learners as in hands-on, experiential or act ivity-based learning; a discovery approach as in the development of process skills associated with scientific methods; and promoting metacognitive knowledge and skills. The National Science Education Standards (N RC, 1996) defined scientific inquiry as: The diverse ways in which sc ientists study the natural wo rld and propose explanations based on the evidence derived from their work. Inquiry also refers to the activ ities of students in which they develop knowledge and understanding of scientific ideas, as well as an understanding of how scientists study the natural world Inquiry is a multifaceted activity that involves making observations; posing questions; examining books and other sources of information to see what is alre ady known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, expl anations, and predicti ons; and communicating the results. (p. 23) In the same respect, Linn et al., (2004, p. 4) described inquiry inst ruction as engaging students in the intentional process of diagnosi ng problems, critiquing experiments, distinguishing alternatives, planning investiga tions, researching conjectures, searching for information, debating with peers, seeking information from experts, and forming coherent arguments. Abd-el-Khalick et al., (2004), on the other hand, di stinguished between the terms inquiry as means (or inquiry in science) and inquiry as ends (or inquiry about scien ce). In their descriptions of scientific inquiry

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26 inquiry as means refers to inquiry as an instructiona l approach intended to help students develop understandings of science content and inquiry as ends refers to inquiry as an instructional outcome which they explained enables students to learn to do inquiry in the context of science content and develop epistemological underst andings about the nature of science and the development of sc ientific knowledge, as well as re levant inquiry skills such as identifying problems, generating research ques tions, designing and c onducting investigations, and formulating, communicating, and defending hypot hesis, models, and e xplanations (p. 398). Abd-el-Khalick et al., (2004) cauti oned us that these aspects of scientific inquiry were often neglected because of the misc onception that students develop understandings about inquiry implicitly by simply doing science. Why Inquiry? Inquiry-based learning environm ents can provide students with the opportunity to generate and revise their thinking in interdisciplinary c ontexts (Myers & Botti, 1997) and achieve the goal of developing general inquiry abilities, acquiri ng specific investigation skills, and understanding science concepts and principles (Edelson et al., 1999). Sherman and Sherman (2004) added that engaging in inquiry helps students develop an ap preciation of how we know; an understanding of the nature of science; and skills to become independent in quirers. According to a recent review of the literature by the National Rese arch Council (2000), positive effects in cognitive achievement, process skills, vocabulary knowledg e, critical thinking, and attitudes toward science document the importance of students receiving explicit inst ruction on skills needed to engage in inquiry. The National Science Education Standards (N RC, 1996) emphasized th e contributions of inquiry-based instruction to teaching and le arning science. According to NRC (1996),

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27 When engaging in inquiry, students describe objects and events, ask questions, construct explanations, test those explanations against current scie ntific knowledge, and communicate their ideas to others. They identify their assumptions, use critical and logical thinking, and consider alternative explanations. In this way, students actively develop their understanding of science by combining scien tific knowledge with r easoning and thinking skills. (p. 2) In addition, while offering a less contentoriented, metacognitive, collaborative, argumentative, and communicative learning e nvironment (Berge & Slotta, 2005), learning through inquiry can also empower students to be come independent, lifelo ng learners by allowing them to gain an appreciation for discovery (Lle wellyn, 2002). It also enab les students to develop their own ideas by building conn ections between their existing ideas and new ideas (Berge & Slotta, 2005). Inquiry-based approaches engage students in cognitive processes used by scientists such as asking questions, developing hypothesis, designing investigations dealing with data, drawing inferences, and building theories (Cra wford, 2000). Doing this allows learners to develop a broader understanding of science and improves their critical reasoning and problem solving skills (Bodzin, 2005). Moreover, inquiry can also contribute to the development of science content understanding by giving them an opportunity to appl y their scientific understanding in the pursuit of research questio ns; by uncovering new scien tific principles and refining their preexisting unde rstandings, encouraging them to demand more knowledge (Edelson et al., 1999). Scientific Inquiry and Teachers Benzce and Hodson (1999) reported a common my th about scientific inquiry that is evident in science curric ula which is scientific inquiry is a simple, algorithmic procedure. Such a conception of inquiry could lead to insufficient understandi ng and practice of scientific inquiry-based instruction. All definitions and stre ngths of the scientific inquiry approach shed light on the complex nature of inquiry-based practices.

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28 First of all, inquiry-based learning demands activity and learning in authentic contexts (Edelson et al., 1999). It achieves this authen ticity by engaging students in problem posing, problem solving, and persuading peers (Roth & Mi chelle, 1998). Lee, Green e, Odom, Schechter, and Slatta, (2004) identified ten stages of inquiry: c ontent, developing the question, designing the experiment or study, defining and representing the problem, observing, e xploring and generating strategies, organizing, analyzing, interpreting, and evaluating [data]. According to Bodzin (2005) and Brendzel (2005), however, inquiry in today s classrooms can take ma ny different forms and encompass a range of activities. While some inquiry-based activities provide for observation, data collection, and analysis; others involve stud ents in the design of ope n-ended activities based on either a teacher-posed question or a classroom discussion (Brendzel, 2005). The National Research Council (1996, p. 25) es tablished five essential features of classroom inquiry, which also form ed the basis for the instrument developed in this study. These features are: Learners are engaged by scien tifically oriented questions. Learners give priority to evidence, which allo ws them to develop and evaluate explanations that address scientifically oriented questions. Learners formulate explanations from evidence to address scientifically oriented questions. Learners evaluate their explana tions in light of alternative ex planations, particularly those reflecting scientif ic understanding. Learners communicate and justif y their proposed explanations. Inquiry teaching requires student s to exhibit certain skills. It engages students in the systematic approach of reasoning in which they are supposed to formulate and test scientific rules and laws (Looi, 1998); requir es them to rely on their explanatory frameworks to develop research questions and hypothesis (Wichma nn Gottdenker, Jonassen, & Milrad, 2003); and exercise a variety of skills, including fo rmulating questions a nd hypotheses, observing,

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29 predicting, collecting and analyzi ng data, classifying, using logical and critical thinking to formulate conclusions, evaluate alternative explanations, and communicate their findings (Sherman & Sherman, 2004; Bodzin, 2005). Howeve r, research indicates that children have difficulties in conducting scientific investigations data gathering, analysis, interpretation, and communication (Edelson et al., 1999). Edelson et al., (1999) listed some reasons, associated with required student skills, impeding the successful implementation of inquiry-based learning. They noted: When students are not sufficiently motivated they fail to participate in inquiry activities. If students are not able to master data coll ection and investigation techniques, they cannot conduct investigations that yield meaningful results. If students lack background knowle dge and the opportunity to deve lop it, they will be unable to complete meaningful investigations. If they are unable to organize their work a nd manage an extended process, they cannot engage in open-ended inquiry. Therefore, the role of teachers is crucial for successful enactment of inquiry approaches in science classrooms. As outlined so far, inquiry is a broad concept. Thus, the meaning of such a concept when applied to classroom practice c ould become muddled and the inte grity lost (Crawford, 2000). To overcome this challenge, teachers need str ong content knowledge (Crawford, 2000), pedagogical content knowledge (Abd-el-Khalick et al., 2004), tools (Bodzin & Beerer, 2003), and assistance (McNall, 2004; Sherman & Sherman, 2004) to become well versed in inquiry-based instruction. Although the successful integration of inquiry in science classrooms require teaching skills such as guiding, challenging, and enc ouraging student learning (NRC, 1996); organizing materials, equipment, media, and technology (NRC, 1996); and a range of teaching strategies (Lee et al., 2004) quite different from typical didactic science instruc tion (Sandoval et al., 1999), research

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30 indicated that teachers typically found inquiry curricula to be difficult and time consuming to teach (White & Frederiksen, 1999). According to some studies, preservice science teachers graduated without conducting a single inquiry in their programs (Windschitl, 2003) and lacked an understanding of inquiry, skills, and experien ces (Newman et al., 2004) as well as training and support (White & Frederiksen, 1998) to im plement inquiry-oriented teaching. They also have very few operational models (Crawford, 1997) to guide them in the implementation of scientific inquiry-b ased instruction. As highlighted above, the implementation of inquiry-based in struction demands a significant shift in what teachers are doing in a science lesson (Crawford, 2000; Bodzin & Beerer, 2003). The focus of much research has ma inly been on student beha viors associated with inquiry instruction and lesser atte ntion given to teacher actions (C arnes, 1997) and to their ability to make judgments about appropria te student experiences and eval uation in the learning context (Bencze & Hodson, 1999). However, Fullan (1982) unde rlined the importance of what teachers do and think for a successful implementation of educational change. It is certain that the behaviors and thoughts of teachers must be more explicitly studied (Crawford, 2000) because they influence (a) knowledge acqui sition and interpretation, (b) define and select the task at hand, (c) interpret course conten t, and (d) determine assessment (Keys & Bryan, 2001). Keys and Bryan (2001) also noted teachers beliefs about the nature of science, student learning, and their role affect how they plan, assess, and teach an accurate view of inqui ry in the classroom. The teaching of science is becoming more student-centered and requiring more from teachers in terms of classroom questioning and st udent involvement in discussions as well as facilitation and organization of learning using al ternative teaching approa ches. According to the

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31 National Research Council (2004), the following list of teacher actions is required in an inquiry classroom: Providing experiences, materials, and sources of information for students to use directly. Showing the use of instruments or materials that students will n eed in their inquiry. Asking open and person-centered questions to el icit present understand ings and how students are explaining what they find. Engaging students in suggesting how to test th eir ideas or answer their questions through investigation or finding evid ence from secondary sources. Helping students with planning so that ideas are fairly tested where necessary. Listening to students ideas and taking them seriously. Asking questions that encourage students to think about how to explain what they find. Creating opportunities for collaborat ive learning and dialogic talk. Scaffolding alternative ideas that may explai n the evidence from their investigation. The National Research Council (2000) supported th e use of different strategies to develop the knowledge, understandings, and abilities descri bed in the National Science Education content standards. Such an approach is not only inevitable but also desirable in order for teachers to implement inquiry in ways that match their own beliefs and teaching styles (Keys & Bryan, 2001). In addition to the attributes of inquiry-based instruction stat ed above, discussion and verbal interactions with peers and teacher (Westbrook, 1997), argumentation (Zembal-Saul & Land, 2002; McDonald, 2004), relating information with prior knowledge and then integrating into larger knowledge structures (Myers & Botti, 199 7), identifying a problem and making a reasoned judgment based on appropriate evidence (Lee at al., 2004), and learning from the process of making errors, revising, posing ne w questions, and retesting (Keys, 1997) are also important ways to enact inquiry in science classrooms. Schwab (1960, cited in NRC, 2000, p. 21), on the

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32 other hand, recommended teachers provide students with readings and reports about scientific research where students read about altern ative explanations, di fferent and conflicting experiments, and debates about the use of evidence. Implementing inquiry is not an easy task. Westbrook (1997) stated it is more than a procedure or a method. Rather, it is a process of investigating how or why or what and then making sense of the resultant findings (p. 2). Ther efore, direct teaching me thods may fail in this process (Westbrook, 1997). Instead, students should be given both freedom and privilege where freedom allows students to choose cont ent related to their interests, generate their own questions, invent methodologies, and make sense of data; and privilege involves learners with the ideas and practices of the scientific comm unity and make these ideas meani ngful at an individual level (Keys, 1997). However, this does not mean that teacher-control is not valued in inquiry instruction. Flick (1997) noted that skilled scie nce teachers achieve inquiry-based learning in moderate to highly controlled conditions by expl icitly teaching inquiry skills and allowing students to apply inquiry sk ills to authentic problems. One way to achieve this is to use technology e ffectively in science classrooms. The section that follows describes the ways in which te chnology can be incorpor ated into classroom instruction, in general and scie nce education, in particular. Technology and Science Education The last sev eral decades have seen the rise of three significan t educational issues: standards, the integration of technology, and teacher quality (Wenglinsky, 2005). Because of its influence in society, the integration of technology has gained peopl es attention for its potential to contribute to classroom in struction. Houhgton (1997) contended that as computers and advanced telecommunications technologies revol utionize nearly every aspect of life, the attention should not be on whethe r technology should be incorporated into teaching and learning,

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33 but how to achieve it. Collins (1991) stated education cannot resist the technology movement because it has already transformed the world an d the way education is conducted (Lou, Abrami, & dApollonia, 2001); shaped how people thi nk, learn, and communicat e (Gura & Percy, 2005); and changed the way people do science, handle pe rsonal affairs, and run businesses and the way schooling takes place (Carin & Bass, 2001). Collins (1991) listed eight possible shifts in instruction in schools due to the integration of te chnology into classrooms: 1. From whole-class to small-group instruction, 2. From lecture and recitation to coaching, 3. From working with better students to working with weaker students, 4. Toward more engaged students, 5. From assessment based on test performance to assessment based on products, progress, and effort, 6. From a competitive to a c ooperative social culture, 7. From all students learning the same thing to different students lear ning different things, 8. From the primacy of verbal thinking to the integration of visual and verbal thinking. However, technology is not a transformative pow er in and of itself (Pederson & Yerrick, 2000). While properly designed and implemented technologies can potentially improve learning (Kozma & Schank, 1998) and promote teachi ng for understanding (Nickerson, 1995), poorly designed and implemented technologies can hind er learning (Nickerson, 1995; Bransford et al., 1999). Researchers recommended technology should be used to increase and assist student learning (Kozma, 1991; Sheffler & Logan, 1999) and should go beyond superficial use (Cooper & Bull, 1997) by turning learning from simple assi milation into a process of active construction and supporting collaborative learning (Salomon, 1992). A Nation at Risk written by the U.S.A. Research, Inc in 1983, sparked the movement of technology integration in schools for the purpose of producing more tech nologically literate

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34 workforce that is ready to compete in the 21st century world economy (McNall, 2004, p.1). Subsequent reports continue to urge educators toward meeti ng this goal. In 2000, the U.S. Department of Educatio n released a report eLearning: Putting a World-class Education at the Fingertips of All Children to develop strategies for the effective use of technologies in elementary and secondary education (U.S. Depart ment of Education, 2000, p. 4). In this report, five national goals for technology were identified: All students and teachers will have access to information technology in their classrooms, schools, communities, and homes. All teachers will use technology effectivel y to help students achieve high academic standards. All students will have technology an d information literacy skills. Research and evaluation will improve the next generation of technology applications for teaching and learning. Digital content and networke d applications will transf orm teaching and learning. Regardless of the call for the in fusion of technology into the daily learning occurring in schools, the findings on the outcomes of tec hnology use in schools pr oduced controversial results. The National Center for Education St atistics (2002) reported no significant change in student achievement despite an increase in co mputers in schools. Kr acjik, Marx, Blumenfeld, Soloway, & Fishman (2000), however, noted th at although researcher s do not have enough evidence on computers increasing achievement, many studies showed positive effects associated with computer aided instruction. Moreover, Poll ard and Pollard (2004) ci ted two meta-analytic studies (Kulik, 1994 & Schater, 2001) in which re sults indicated technology-rich environments enabled students to learn more and faster, have more positiv e attitudes, and improved their achievement. Lou et al., (2001) conducted a meta-analysis and concluded when computer technologies were used for small group learning ra ther than individual learning, it produced more

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35 positive results in terms of l earning. Pederson and Yerrick (2 000), on the other hand, averred computers seem to only be used to suppor t existing learning and teaching patterns. Promising findings have been noted in other significant research studies. For example, Morgan (1996) stated that fourth and eight-gra de students who used comp uters to play learning games and for simulation and models scored higher on average, than others who did not. Berger, Lu, Belzer, & Voss (1994) repor ted higher thinking skills for secondary school students who were exposed to interactive video disk, com puter-assisted instruction, and mastery-based learning. Another study (Spitulnik et al., 1998) indicated studen ts who designed technological artifacts using hypermedia had bette r integrated understanding. It is also evident in the literature that when computer technologies were used fo r collaborative activity and designed for mindful engagement of students they created new oppor tunities for decision making, thinking, and constructing (Salomon, Perkins, & Globers on, 1991; Salomon, 1992). The results from the National Assessment of Educational Progress study (also known as the Nations Report Card) also revealed positive association between computer use and student performance when computers are used in a construc tivist fashion (Wenglinsky, 2005). Overall, findings mentioned above shed light on future research directions in the field. Bebell, Russell, & ODwyer (2004) suggested that despite the tende ncy to examine the effects of technology on student learning, e ffects of learning and unders tanding of how teachers and students are using technology s hould take higher consideratio n. Similarly, Lewis (1999) addressed the need for more evaluative studies th at focus on attempts at integration. Pollard and Pollard (2004) organized a Delphi panel to examine the future of research in the field. The panel recommended that research efforts should focus on the role of technology in improving students problem solving abilities and helping them acco mplish learning tasks rather than on scores on

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36 achievement tests. In addition, inservice and pres ervice teachers use of technology has also been a high priority area in the field (Pollard & Po llard, 2004). Bebell et al., (2 004) put that research on teachers use of technology lacked a clear de finition and stressed th e importance of providing valid measures of technology use among teachers. Technology and Learning Duffy and Cunningham (1996) provided a ratio nale for using technology for learning. They suggested that learning occurs in context and is an active, so cial, and a reflective process. Such a rationale could provide teachers a fram ework when seeking out ways to effectively integrate technology into instru ction. Technology has the potentia l to reinvigorate learning by increasing motivation; providing recontexualized and individualized instruction; improving writing; encouraging student publishing and research; and transforming the classrooms into a multiple intelligence-centered learning envir onment (Gura & Percy, 2005). Computers, for example, provide great interactivity and have the ability to become any media, to present information from many different perspectives, an d to become reflective (Carin & Bass, 2001). Technologies can provide scaffolds and tools to enhance learning, give feedback (Bransford et al., 1999) and use multiple representations, mode ling and visualization to enhance learning (Kozma, 1991; Morgan, 1996; Spitulnik et al., 1998; Dede, 2000; Flick & Bell, 2000; Gura & Percy, 2005). They also help build a community of learners by bridging teachers, students, and experts (Bransford et al., 1999; Dede, 2000). Anot her important use of technology in instruction is its ability to engage students in problem-rich environments to explore and solve (Bransford et al., 1999); develop math or science concepts as we ll as collaborative skills (Stables, 1997); deal with misconceptions (Cognition and Technology Group at Vanderbilt, 1992; Nickerson, 1995); and master more complex subjects via rich interactions using external resources (Dede, 2000). Technology tools are also essentia l in helping students generate and test hypotheses and build

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37 explanations of scientific phe nomena (Spitulnik et al., 1998) a nd analyze, visualize, solve, investigate, and communicate information (Lovele ss et al., 2001; Rieser, Krajcik, Moje, & Marx, 2003; McNall, 2004). In addition to these potential benefits of technologies, Hooper and Rieber (1995) noted three principles and their implications for us ing technology in the classroom: (a) effective learners actively process lesson content, (b) pr esenting information from multiple perspectives increases the durability of inst ruction, and (c) effec tive instruction should build upon students knowledge and experiences and be grounded in mean ingful contexts. Ther efore, technology tools should be used in ways to bridge the gaps between students and sc ientists, to provide meaningful problems to students, and to help students develop background knowledge and investigation techniques (Edelson et al., 1999). Models of Technology Use There are multip le methods for studying how te chnology is used in instruction. One model divides technology into two categories: Effects with and effects of technology. Effects with technology refers to changes that take place as the result of engagement in an intellectual partnership with a techno logy tool or peers; and effects of technology refers to long lasting changes that take place as a result of that inte llectual partnership (Salomon et al., 1991; Salamon, 1998). For the purposes of this study, it is impor tant to point out approaches to technology integration in science classrooms as the goal of th e study is to develop an instrument that focuses on science teachers perceptions and uses of technology in inquiry-oriented science classrooms. Carin and Bass (2001) interpreted the Informati on Society for Technology in Education (ISTE) technology standards considering how to connect educational technology and science education and categorized those standards into three areas: (a) standards related to learning with technology in which technology is used to enhance pr oductivity, communications, research, problem

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38 solving, and decision making, (b) standards related to learning from technology in which computer-assisted instruction, tutorials, simula tions, and multimedia presentations are used to enhance science learning, a nd (c) standards related to learning about technology in which learning how computing systems operate, learning how to use them in classroom settings, and considering societal implications of technology use are emphasize d. The instrument developed in this study addressed all three of th ese standards to varying degrees. Technology in Science Instruction There is a strong support in the literature for using technology in science instruction. Flick and Bell (2000) highlighted this m eaningful part nership between the two fields across most of the twentieth century. This partnership was also enforced by current reforms represented by institutions such as American Association for the Advancement of Science and National Research Council (Pederson & Yerrick, 2000). Su ch reforms encouraged integration of technology in science education to help students use scientific know ledge to predict, explain, and model phenomena (Spitulnik et al., 1998). In ad dition, McNall (2004) reported that the National Educational Technology Standards (NETS, IS TE) and the National Science Teachers Association (NSTA) recommended effective use of technology in science instruction to enrich the learning and teaching of science and to suppor t inquiry. The impact of technology on science education challenged both scie nce educators and science teach ers and changed the ways each interact with students in thei r classrooms (Flick & Bell, 2000) and offered new opportunities to transform science instruction with inquiry projects that improve science, technology, and language literacy (Linn et al., 2004). By doing this, technology integr ation is considered to have an impact on connecting students to the designe d world (NRC, 1996), empowering them to learn (Berger et al., 1994) and to beco me more active explorers of th eir environment (Carin & Bass, 2001). Lewis (1999) suggested educational technology to establish itself in relation to other

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39 subjects and noted that the field of educatio nal technology has to understand the integration of technology to other subjects in the curriculum. The relationship between science education and technology integration discussed above pinpoints the importance of this perspective. Technology and Inquiry Although technology is a powerful tool for le arning m erely employing it does not produce the desired effects (Lou et al., 2001). Literature shows that as new technol ogies became available to educators, science teachers a nd educators struggled to find eff ective ways of using them (Linn, 1998) especially for the purpose of inquiry in struction. In addition, inquiry teaching is challenging because it requires strong background knowledge with regard to science content, inquiry-oriented instruction, pedagogy, and t echnology (Pedersen & Yerrick, 2000; Williams et al., 2004). Spitulnik et al., (1998) pointe d out that the new vision of science education has become engaging students in scientific inquiry activities and using tec hnological tools to achieve this goal. Edelson et al., (1999) beli eved all of the fundamental prope rties of computing technologies, such as the ability to store and manipulate larg e quantities of information, the ability to permit interaction in a variety of audio and visual form ats, the ability to perform complex computations, the support for communications, and the ability to give feedback to users offer benefits for inquiry-based learning. Flick and Bell (2000) noted that activities involving technology should make connections to student experiences and promote inquiry-based learning. Such learning demands new teaching methods a nd technology offers promising solutions (Linn et al., 2004). The contributions of inquiry-based learning and educational technol ogy to each other are reciprocal. Namely, recent advances in cognitive science and educationa l technology (particularly computer simulations and modeling tools) set the stag e for developing more effective approaches to the teaching of scien tific inquiry (White & Frederiksen, 1998). Inquiry,

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40 on the other hand, contributed to the educational technology fi eld because (a) it related fundamentally to the basic claims of the field, (b) it reminded us that educati onal technology is about learning and teaching, and (c) both inquiry and technology shared and conformed to conceptual frameworks such as situated cognition and constructivism that unite technology with other school subjects (Lewis, 1999). Carin and Ba ss (2001) contended that the notion of design in technology is parallel to science and, as identified by the National Science Education Standards, technological design f acilitates scientific inquiry in defining a problem, designing an approach, implementing a solution, evaluati ng the solution, and commu nicating the problem, design, and solution to others. McNall (2004) highlighted the capacity of e ducational technology tool s to support inquiry learning in science by assisting st udents in visualizing abstract concepts and engaging them in rich experiences. According to Alagic et al., (2003), information technologies can play a special role in inquiry-based learning as the subject of instruction or as a tool for instruction. They noted that, when used in inquiry-oriented activities, t echnology is most often used (a) as a tool, (b) in the context of solving a problem, (c) to augm ent communication by expanding audiences, or (d) to broaden collection of repres entations. Similarly, Looi (1998) stated technology tools in the form of interactive learning environments could en rich learning (1) as instructive tools, (2) as constructive tools, (3) as communicative tools, and (4 ) as situating tools. Literature provides several strategies regard ing the successful inte gration of technology and inquiry-based instruction. Edelson et al., (19 99) categorized these te chnologies as tools for modeling phenomena and processes from the re al world (e.g., Model-It, Jackson, Stratford, Krajcik, & Soloway, 1996; ThinkerTools, White, 1993), visualizing and analyzing quantitative data (e.g., Tabletop, Hancock et al., 1992; GLOBE, Rock, Black well, Miller, & Hardison, 1997),

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41 exchanging data and ideas acros s distances (e.g., GLOBE, Rock et al., 1997; Kids as Global Scientists, Songer, 1995), structuring and suppor ting discussion (e.g., CoVis, Edelson et al., 1996; CSILE, Scarmadalia & Bereiter, 1994), and pr oviding access to information in the form of digital collections and librarie s (e.g., Knowledge Integration E nvironment, Linn, Bell, & Hsi, 1998). Multiple Examples of Technology Used to Reach Goals of Science Inq uiry Although the literature pinpoints the potential contributi on of technology to science education in general, and science inquiry in particular, th ere are not many examples of technologies designed to attain th e goals of scientific inquiry. Th is section briefly introduces some of the well-known examples of technolog ies, such as the GLOBE, ThinkerTools and Model-It, used to expose student s to scientific inquiry. As a modeling tool, Model-It, developed by the University of Michigan, provided students with an open-ended task, where stud ents were able to create their own models to represent some ecological phenomena. It focused on higher-leve l concepts and enabled students to ground their experience and prior knowledge, build repres entations, and couple actions, effects, and understanding (Jackson et al., 1996 ). This technology applicati on supports the development of qualitative, verbal representa tions and provides simultaneous, linked textual-to-graphical representation of relationships. It also helps st udents connect their actio ns, the visual feedback provided, and their own mental representati ons of the phenomenon (Jackson et al., 1996). ThinkerTools, developed by White (1993), focused on learning how to construct causal models based on real-world phenomena rather than le arning how to solve well-defined quantitative problems. In general, ThinkerTools supported strategies such as questioning, predicting, experimenting, modeling, and applying (White & Frederiksen, 1998). It also enabled students to develop conceptual models that embody the principles of Newtonian mechanics, and to apply

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42 their models in making predictions, solving pr oblems, and generating explanations (White, 1993). The ThinkerTools environment was shown to be capable of changing students views of aptitude for learning and understanding science (W hite & Frederiksen, 1998) and of enabling all students to improve their performance on vari ous inquiry and physics measures (White & Frederiksen, 2000). It was also effective in reducing the performance gap between low and high achieving students (White & Frederiksen, 1998; 2000). Tabletop is a computer-based data analysis tool based on animated visual repres entations and enables students to solve real problems and to answer authentic questions (Han cock et al., 1992). Vignettes from clinical sessions of this study illustrated Tabletop stimulated students in terest and increased students successful interactions with data creation and da ta analysis. Students became engaged in subtle and important questions in data design and data analysis and developed good discussions related to aggregate reasoning (Hanco ck et al., 1992). The GLOBE pr oject (Global Learning and Observations to Benefit the Environment) is a worldwide, hands-on scie nce program for primary and secondary school students. It creates a partne rship between students, their teachers, and the scientific research community a nd introduces students to the pr ocess of doing real science and allows them to learn by doing (Rock et al., 1997). The students in th e GLOBE project make scientific measurements in thei r local environments, report data through the Internet, publish and present their data on GLOBE, create visuals to present informa tion and collaborate with peers and scientists. The Kids as Global Scientists (KGS) program uses communication features of the Internet to create a learning environment that enables students to solve real and complex problems associated with weather phenomena a nd supports reflective qu estioning, investigation, data collection, analysis, comparisons, predictions, and inquiry-based activities in which students act as reporters, participants, and providers and data and information (Songer, 1996). Findings

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43 indicated the effectiven ess of authentic inquiry science (L ee & Songer, 2003) as well as significant content and inquiry ga ins (Songer, Lee, & Kam, 2002) associated with concepts in biodiversity and the design patter n, formulating scientific explan ations from evidence (Songer & Wnek, 2003) and weather conten t knowledge (Mistler -Jackson & Songer, 2000). Results also showed a high positive attitude toward learning science (Kam & Songer, 1998; Mistler-Jackson & Songer, 2000). With regard to perception of di fficulty of learning scien ce, the results showed there was a statistically significan t increase in the percentage of girls who perceived science as not difficult at all (Kam & Songer, 1998). In add ition, a content analysis of the messages in the electronic discourse environment of the KGS program revealed that it enab led learners to build an electronic community of scie nce learners and fostered stud ent understanding through specific tasks, more student-student communication, socializations among participants, sharing personal experiences and scaffolding by experts (Lee & Songer, 1998). The CoVis (Learning Through Collaborative Visualization) proj ect is founded on the premise th at classroom sc ience learning should resemble the open-ended, inquiry-based appr oach of science practi ce. The project enables teachers and students to learn science by doing in connection with commun ities of science and science educators (Gomez, Gordon, & Karlson, 1995) Computer-Supported In tentional Learning Environments (CSILE) are designed to support knowledge building in learning communities. Scarmadalia and Bereiter (1994) noted that CSIL E was based on research on intentional learning, process aspects of expertise, and discourse in knowledge-building communities. It is also based on solving logistic problems that Scarmadalia and Bereiter thought have the greatest potential for educational technology. The Knowledge Integration Environment (KIE) is designed for using the Internet to enhance stud ent understanding of science. It offers students science models that apply to problems they encounter in their everyday li ves and engages them in personally relevant

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44 science projects (Linn et al., 1998). Linn et al ., (1998) reported findings indicating significant gains in students understanding of the nature of light, heat, and ot her scientific domains as well as in students use of their new knowledge to interpret new problems. In addition to these technology examples, Edelson et al., (1999) targ eted scientific visualization and developed visualization environments (The Climate Visu alizer, The Radiation B udget Visualizer about global warning, The Greenhouse Effect Visualizer, and WorldWatcher) for the interpretation of relative weather data in order to support inquiry-based learni ng. The Inquiry Page is another web-based tool that provides its users a dyna mic and flexible environment, which supports teaching and learning in diverse educational settings a nd facilitates real-world application of inquiry-based learning across subject areas. It en gages learners in a learning cycle model based on Deweys ideas and enables student s to ask, investigate, create, discuss, and reflect (Bruce & Bishop, 2002; Comstock, Bruce, & Ha rnish, 2003). It is reported th at the Inquiry Page tool was successful in fostering colla borative inquiry among stude nts (Bruce & Bishop, 2002) and creating a community of learners including teachers and stud ents in terms of inquiry learning (Benson & Bruce, 2001). Another web-based in quiry environment is called the Web-Based Inquiry Science Environment (WISE) where stud ents design solutions to problems, generate predictions before conducting ex periments, use scientific ev idence to support theories or conclusions, debate contemporary science issues and reconcile differences between new and prior science ideas (Williams et al., 2004). This free website is designed by the University of California at Berkeley. Some of the contemporar y science issues studied on the website are earthquakes, malaria, genetically modifies foods, HIV, water quality and thermodynamics. A research study with 1100 sixth grad e students indicated that WISE students were able to achieve

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45 a deeper understanding of the content knowledge and developed students model-based inquiry skills (Gobert, Slotta, Palla nt, Nagy, & Targum, 2002). Although there are numerous illustrations where technology have been created to assist the integration of science in classrooms to promote the goals of scientific inqu iry, there is a need for instruments to assist science teachers and science educators to frame their work in using technology to promote the goals of scientific inquiry. Hence, this study will develop an instrument to begin providing this framework on which science teachers can base and evaluate their work. Summary The role of inquiry in science education ha s been strongly influenced by current reform reports or docum ents in the United States (C rawford, 1997; Flick, 1997; A bd-el-Khalick et al., 2004) such as the National Science Education Standards (NRC, 1996; 2000) and Benchmarks for Science Literacy (AAAS, 1993). In addition, the use of technologica l tools to enrich the learning and teaching of science and to support inquiry was recommended by National Science Teacher Association and outlined in National Educati onal Technology Standards of the International Society for Technology in Education (McNall, 2004) This review of the literature has attempted to establish a framework to develop an instrume nt that measures science teachers self-reported perceptions and uses of educationa l technologies to enact scientific inquiry in their classrooms. It was shown there is little resear ch combining scientific inquiry and technology use by science teachers. This literature review indicates a need for more research into understanding teachers perceptions and uses of technological tools for science inquiry purposes.

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46 CHAPTER 3 METHODOLOGY This chapter provides an overview of the procedur es that were used to conduct the study. It contains the research questions and a descri ption of the particip ants, data collection, instrum entation, and data analysis techniques. Introduction This study is based on the prem is e that technology could enhan ce the quality of scientific inquiry-based instruction and attempts to answ er the question of how science teachers use technology to attain the goals of scientific inquiry-ba sed instruction. The instrument developed in this study can serve as a useful guide for sc ience teachers in the integration of technology in scientific inquiry-based learning environments. For this purpose, the study investigated science teachers self-reported perceptions about implem enting scientific inquiry using technology and explored the degree to which they use technology for such a goal. This study also examined whether relationships existed, and the degree of these relationships, among a set of variables related to teachers use of technology for inquiry purposes. Research Questions This study had two overarching research que stions and five supporting questions. The m ain questions focused on how teachers use te chnology to implement the goals of scientific inquiry and the relationships betw een their self-reported perceptions and practices regarding this implementation. The supporting qu estions, however, articulated th ese relationships by addressing some teacher demographics and background/professional development variables as well as the level and frequency of their prep aredness and use of certain inqui ry tasks and technology tools. While the total scores obtained from the surv ey and the relationships between perception and practice items in the survey were utilized to help answer the overarching questions of the study,

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47 relationships obtained from suppor ting questions also contributed to answering these overarching questions. Through the creation of an instrume nt, the study addressed the following research questions: Overarching Questions 1. How are teachers using technology to im plement the goals of scientif ic inquiry in their classrooms? 2. What are the relationships between teacher s self-reported perceptions and practices regarding the use of technology to atta in the goals of sc ientific inquiry? Supporting Questions What are the relationsh ips between teachers se lf-reported perceptions and practices regarding the use of technology to atta in the goals of scientific inquiry in terms of: Teacher demographics and teacher backgr ound/professional development variables? How often do teachers support students to engage in certain inquiry skills in their science classrooms? How often do teachers use certain technol ogy tools in their science classrooms? How prepared do teachers feel to support students to engage in certain inquiry skills in their science classrooms? How prepared do teachers feel to use certain technology tools in their science classrooms? Technology in the SIT-TIPPS In this study, technology re ferred to a range of devices and technological processes specifically used for teaching a nd learning purposes in K-12 setti ngs. For the purposes of this study, the fo llowing devices, with examples, comprised the definition of technologies: Computers (desktop, laptop); presen tation devices (such as video pr ojectors, LCD panels); Smart Board/Promethean interactive boards; wireless communication de vices (such as PDAs, student digital response systems); computer software (such as Word processors, desktop publishing, spreadsheets, presentation software, databases, simulations, educational games, graphing and data analysis software, video and picture editing software, etc.); graphing/scientific calculators;

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48 Portable Global Positioning System s (GPS); digital data collection devices (such as pH, pressure and temperature probes, digital microscope s, Navigator systems); videoconferencing, teleconferencing; Internet techno logies (such as e-mail, websites, online databases, virtual field trips, online simulations and science games, Wi kis, blogs, podcasts, videocasts, Google Earth and other Google tools, online learning commun ities); and data collection telecollaborative activities (such as Journey North, SCOPE, Amazing Space). Data Collection The researcher attem pted to attract as many pa rticipants as possible to the study for a high turnout rate in order to run a successful factor analysis (at le ast ten times the number of items) and to have a representative sample. Because a ra ndom selection of participants is very difficult to achieve in such nationwide st udies, the researcher attempted to reach as many science teachers as possible to have a wider representation from various states and social settings. For this purpose, middle and high school science teachers from various states in the U.S. were contacted by the following methods: (1) The researcher vis ited the official websites of public middle and high schools listed on the websites of Departments of Education in various states including Florida, Virginia, Kansas, Wisconsin, Connecticut and Alabama and the city of San Diego. This method enabled the researcher to collect indi vidual email addresses of over 4,000 science teachers. The selection criteria for these states were their geographical distributions in order to increase sample diversity. (2) The researcher cont acted professional science teachers associations nationwide and statewide to have these organizations disseminate the invitational e-mail message to their members. This helped the researcher to reach an audience as representative as possible of the science teachers in the U.S. because, many of these statewid e and nationwide organizations had access to teachers from different parts of the country. (3) Finally, the researcher also sent email messages to listservs (UFTScienceComm, middleschoolscience,

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49 HighSchoolScienceTeachers, science connection, astroed_news, and learningscienceconcepts) serving science teachers to which he had an online membership. Data was collected online in the form of a web-based questionnaire (Appendix B). For this purpose, a professional web servic e, Survey Monkey, was used, whic h enabled the researcher to host the online survey, create email lists, collect data, and report basic statistics such as the number of responses for each individual item and frequencies. The researcher sent potential participants, directly or via list servs, an invitational e-mail message to inform them about the nature of the study and encourag e their participation to complete the web-based questionnaire. The online version of the survey consisted of an interface including the IRB consent form (Appendix A), definition of technology, and the survey items. The participants were not able to access the survey unless they read the IRB consent form and agreed to participate in the study by clicking on the Press here to start the Surv ey button. An American Association for the Advancement of Science report titled Ethical and legal aspects of human subject research on the Internet (Frankel & Siang, 1999) and an American Ps ychological Associati on (APA) report titled Psychological research online: Opportunities and challenges (Kraut et al., 2003) recommended similar practices when getting informed consent from online participants. Consent form informed all part icipants that participation in the study was voluntary, they had a right to withdraw from the study at any point without consequence, they may skip any question they did not wish to answer, and there were no anticipated risks, compensation, or other benefits for their participation in the study. It also provided info rmation about how to contact the principal investigator, the superv isor, and the University of Fl orida Institutional Review Board (UF IRB) should they had any questions or concerns. Participants were encouraged to make a

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50 copy of the consent form for their own records as well as given the option to provide their email addresses if they want the rese archer to send them a copy of it in an electronic format. The online survey started with a question that asked whether a participant is a middle or high school science teacher in the U.S. This help ed identify participants who were not among the target population of the study and thus, whos e data was deleted from the data pool. Over fifteen hundred people (1548) visited the link provided to them w ithin the invitational email message. Of these visitors, 254 people (16. 4%) responded no to the first question, which asked whether they are a middle or high school sc ience teacher in the United States. Because these people were not among the targeted populati on, any data they provided was deleted from the database. On the other hand, 1294 people (83 .6%) indicated that they were middle or high schools science teachers in the U.S. However, not all of these visito rs were treated as participants. Although 715 of these science teachers (55.3%) provided valuable input and proceeded to the end of the survey, the rest of the visitors (517, 44.7%) did not submit any input and just seemed to explore the survey. These pe ople either did not answer any of the survey items or responded to only a few questions in th e beginning of the survey. Therefore, any visitor who did not answer any of the statements or responded to less than 40 statements (out of 120 statements plus demographics/teacher backgr ound questions) was neglected. Therefore, 715 science teachers comprised the sample of the study. Instrumentation and Instrume nt Development Procedures In the developm ent of any instrument, the es tablishment of validity and reliability is crucial. Otherwise, the instrument will be ineff ective. For this study, the researcher followed the guidelines set forth by Devellis (2003), Gable and Wolf (1993), and Muelle r (1986) to establish validity and reliability for the SIT-TIPPS.

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51 First, in order to achieve clarity (Devellis, 2003) in de fining the construct this study intended to measure, the researcher made use of the extensive literature and theory related to the construct, established specificit y, and was careful about what to include in the measure. This helped the researcher specify the goals of the instrument being develope d (Mueller, 1986; Gable & Wolf, 1993; Devellis, 2003) and made sure the researcher had the same understanding of the instrument content as the respondents (Mueller, 1986). For this purpose, the researcher grounded the object of measurement in the substantive theories related to the phenomenon (Devellis, 2003). Therefore, in order to develop an instrument that measures science teachers self-reported perceptions and uses of technol ogy for scientific inqui ry purposes, the res earcher meshed the essential features of scientif ic inquiry described in the National Science Education Standards (NRC, 2000) with the exte nsive literature availa ble on inquiry-based science instruction and the uses of technology in instruction (see Appendix C). This approach of following theoretically based conceptual definitions helped the researcher define appropriate opera tional definitions that lead to good content and construc t validity of the measure (Gable & Wolf, 1993) as described in the sections that follow. Specificity or generality of the construct is a nother factor contributing to the clarity of the construct being measured (Devellis, 2003). Th e SIT-TIPPS measured very specific behaviors regarding the use of technology for inquiry purposes in scien ce classrooms. Moreover, the specified goals of the SIT-TIPPS were derived fro m theory and a well-done literature review guided the process of developing co nstructs that are di stinct from other c onstructs and clearly written (see Appendix C). Based on the purpose of the scale, the researcher constructed a large pool of behaviors (see Appendix C) that were later transformed into items and categorized under each of the five

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52 essential features of scientific inquiry described by the Nationa l Science Education Standards. These five features, which formed the basis for the development of the scale in this study, were meshed with technology related constructs and m odified to reflect teacher perspective. For example, the first feature of scientific inquiry that reads learner enga ges in scientifically oriented questions were transformed into teacher engages students in scientifically oriented questions. Then, the researcher outlined sp ecific behaviors based on the National Science Education Standards (2000) and the extensive li terature review for each of the five teacheroriented constructs. For instance, some of the behaviors identified for the first category were: asking why and how questions, generating a need to know in students, and encouraging students to demand more knowledge. Such behavi ors then constituted the items of the scale. This process yielded a large item pool, which be came candidates for eventual inclusion in the scale (Mueller, 1986; Devellis, 2003). All items making up the s cale reflected the construct underlying them and were chosen cautiously to create a homogenous sc ale (Devellis, 2003). This strategy is used to generate items in two different categories: Teacher perceptions and practices. Because another purpose of the study was to focus on teachers current practices of using technology for scientific inquiry purposes, as well as thei r self-reported perceptions of using technology for such purposes, the researcher created parallel items for both categories. For instance, one of the items in the first category (te acher engages students in scientifically oriented questions) took the form: I integrate technolog y to enable students to conduct successful empirical investigations in the teacher practices category, wh ereas in the teacher perceptions category it was expressed as A science teacher should in tegrate technology to enable students to conduct succ essful empirical investigations The main objective for this strategy was to look at potential differences between teachers self-reported perceptions and

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53 actual practices regarding the use of technology for scientific inquiry purpos es in their classrooms. The internal consistency reliability of the scale is directly related to the number of items in a scale as well as how strongly the items correlate with one another, when all else is held equal (Mueller, 1986; Devellis, 2003). For this purpose, the researcher employed as large a pool of inquiry behaviors as possible a nd selected as many items as possi ble from this pool to have high internal consistency reliability. However, when de termining the number of items in the scale, the researcher also took into consideration the suggestion that specific and tightly conceptualized objects can be measured by fewer items than the loosely defined and amorphous objects (Mueller, 1986). Therefore, the object of measurement in this study was kept specific and conceptualized according to a theoretical framework and the related literature. In addition, during the construc tion of the items, the research er attempted to avoid using items that are (a) ambiguous, (b) exceptionally lengthy, (c) difficult to read, (d) double-barreled (conveying two or more ideas), and (e) composed of multiple negatives (Devellis, 2003). In addition, the researcher also avoided the use of absolutes such as always and never. Feedback from the 6 content reviewers for the study (see pa ge 65 for their credentials ) was very useful at this process. In contrast to what Mueller (1986) suggested about using both positively and negatively worded items to prevent the problem of little variance in the scale, the researcher generated only positively worded items due to the length of the scale and to decrease the level of cognitive load for the participants. For the same reason, no additional items were included in the instrument to detect any flaws or problems infl uenced by other motivations or any measures of relevant constructs to contribu te to the validity of the scal e as suggested by Devellis (2003).

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54 Likert scaling was used in the instrument because they are easy to construct, can be highly reliable, and have been successfully adapted to measure many different kinds of attitudes (Nunnaly, 1978). The statements in the scale were presented in a 5-point strongly agree/disagree format, because it is one of the most reliable one (Gable & Wolf, 1993, Mueller, 1986); yet these statements were fairly (though not extremely) strong to reflect true differences of opinion (Devellis, 2003). In addition to 5-point strongly ag ree/disagree format, the researcher also used a 4-point not adequately prepar ed/very well prepared format and a 5-point never/almost all type frequency format for Inquiry skills and tech nology tools sections of the survey (see survey in Appendix B). After a review of the items during the content-validity procedure, the scale was administered to a representative sample of mo re than 700 science teach ers and item-analysis, alpha reliability, and factor anal ysis procedures followed to check for the validity and reliability of the scale being constructed. Content Validation Process: The next step in the instrum ent development procedure was to have the item pool reviewed by experts who are knowledgeable in inquiry-bas ed science instruction and instructional technology. This validation strategy was utilized to maximize the cont ent validity of the instrument by confirming or invalidating the de finition of the construc ts the study intended to measure as well as evaluating the items clarity and conciseness (Devellis, 2003). For this purpose, the researcher contacted one expert on educational technology to provide insight into technology related aspects of the sc ale. Five other experts in science education, as well as in integrating technology into science classrooms, comprised the conten t validation team. The researcher provided the content validators with the working definitions of the constructs including what is meant by technology in the study, and then asked them to rate each item with respect to its relevance to the construct (1 being completely irrelevant, 2 being somewhat

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55 relevant, and 3 being highly re levant) as it has been defined (Devellis, 2003) and with respect to its predetermined category of one of the five esse ntial features of scientif ic inquiry used in this study. The content validators were also asked to indicate how certain they felt about their agreement of the item to the construct (1 being completely unsure, 2 being unsure, 3 being pretty sure, and 4 being very sure). Each co ntent validator, rated the 50 items in the scale and provided feedback on the contents of the in quiry skills and technol ogy tools sections of the instrument. The experts who participat ed in the content validation of the scale were: Dr. Collen Swain, Educational Technology, School of Teaching and Learning at the University of Florida (educational technology expert who provided feedback on technology related content) Dr. Tom Dana, Science Education, School of Teaching and Learning at the University of Florida (content validator) Dr. Rose Pringle, Science Education, School of Teaching and Learning at the University of Florida (content validator) Dr. Troy Sadler, Science Education, School of Teaching and Learning at the University of Florida (content validator) Dr. Karen Irving, Science Education, School of Teaching and Learning at the Ohio State University (content validator) Dr. Dina Mayne, South Effingha m High School in Georgia, Chemistry Teacher (content validator). Before administering the instrument to the study sample, the researcher calculated the percent agreement among the content validators on whether they agreed with the researcherassigned categories to items as well as the av erage level of certainty and relevance across validators. Any item whose percent agreement wa s below 80 percent was deemed problematic and subjected to either revision or deletion. Whether it is more than 80 percent or not, an average certainty score of less than 3.0 (out of 4.0) and an average relevance score of less than 2.0 (out of

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56 3.0) were selected as additional criteria to dete rmine whether an item need ed revision or deletion from the scale. Moreover, the content valida tors also provided feedback regarding the demographic information/teacher background se gment and the inquiry skills and technology tools segments of the instrument that aimed to collect information on the extent science teachers use certain inquiry skills and t echnology tools in thei r classrooms as well as how well prepared they feel about these skills a nd technology tools. Based on the cal culations, the researcher found out that 10 items (5 perception a nd 5 practice items that were para llel to each other) had percent agreement scores of less than 80 percent. The n, based on feedback from two experts, some of these items have been revised to fit into the catego ry that is intended to re present the item and/or to make them more relevant, unde rstandable, and concis e. In addition, conten t validators written comments on some of the statements were also used to make such revisions. For instance, the item that read I integrate technology to impr ove students abilities to describe scientific theories, rules, laws, and events was revised in to I integrate technol ogy to improve students skills to check their resu lts against existing scien tific knowledge. No other item needed revision or deletion based solely on releva nce and/or certainty level scores because of above cutoff scores (above 2.0 for relevance and above 3.0 for certainty categories). After the content validation process was comp leted, the researcher designed the online version of the scale and then posted it. The 715 science teachers completed the survey, which was as large enough to eliminate subject variance (Devellis, 2003) and to successfully run the item-analysis, reliability, correla tion, and factor analysis procedur es (Gable & Wolf, 1993). This is in concert with the criteria that suggeste d having 6-10 times as many people as there are statements on the instrument (Gable & Wolf, 1993) or at least five subjects per item (Nunnaly, 1978) as the minimum number of ite ms that can be tolerated.

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57 Once the scale was administered to a large and representative sample, the researcher tested the performance of items to identif y effective functioning. The analysis of the set of items at this step was (a) factor analysis: to determine the number of latent variables underlying an item set and explain the variation among the items (D evellis, 2003; Gable & Wolf, 1993), (b) item analysis: to generate re sponse frequencies, percentages, means, and standard deviations as well as to identify items to delete from the instrume nt, and (c) reliability analysis: to indicate the scales quality. A detailed dissemination of the results of thes e analyses is presented in Chapter 4. As a summary, the factor analysis results supported a two-factor solution: te achers practices of scientific inquiry using technology and their perceptions of such use. In addition, results provided support for construct vali dity of the instrument. The Cronbachs alpha reliability value of .980 for the overall scale (.976 fo r perceptions factor and .974 fo r practices factor) indicated high internal consistency reliability. Statistical Techniques Used to Answer Study Research Questions The researcher m ade use of a variety of statistical methods to answer the overarching and the supporting questions. Before be ginning to answer the research questions, the reliability and item analysis and exploratory and confirmatory factor analyses were used to determine the reliability and validity of the SIT-TIPPS instrume nt. This process was essential in answering the research questions as all of the overarching a nd supporting questions depended on the quality of the scale. The data obtained through teachers self-reported responses to the components of the SIT-TIPPS instrument were then analyzed using a variety of statistical methods to answer the study research questions. The additional met hods used were descri ptive statis tics (e.g., frequencies), correlations, multiple regressions, t-tests, and ANOVAs. Interpretations from all of these methods contributed to the explanation of the first overarching question. For the second

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58 overarching question, the correlations between the 25 items constituting the teachers selfreported perceptions factor and the 25 items constituting the teachers self-reported practices factor were calculated. Multiple regression method wa s also used to answer this question because models indicated that these two factors were dependent on each ot her. To investigate the effect of teacher demographics and teacher background/ teacher professional development variables on teachers perceptions and practices for using technology to enact scientific inquiry, descriptive statistics, multiple regression, correlations, t-te sts, and ANOVAs were used. For example, in order to explore the relationship between ge nder and teachers classroom practices, the researcher calculated the correlation between the two variables and a ttest was conducted. In addition, gender was used as an exploratory vari able in a multiple regression model where the teacher practices factor was used a dependent variable. The last four supporting research questions were represented in the SIT-TIPPS w ith certain number of items (see Appendix B). Nine items were used to measure how well prepared teachers felt to support students to engage in certain inquiry skills and how often they use these inquiry skills in their classrooms. Twentysix items were employed to answer how well pr epared teachers felt to use certain technology tools/applications and how often they used these tools in their classrooms. In order to answer these four supporting questions, the researcher us ed frequency reports, correlations, and multiple regression models. According to the criteria set forth by C ohen (1988) for psychological research, a correlation coefficient ranging between .10 and .30 was considered small; values between .30 and .50 were considered medium; and values betw een .50 and 1.0 were considered large. In this study, this criterion was used as a guidelin e to interpret the co rrelation coefficients.

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59 Item Analysis and Reliability The Scientific Inquiry with Technology-Teacher Percepti ons and Practices (SIT-TPPS) Scale developed as part of this study dem onstrat ed high internal consistency (see Table 3-1). The overall reliability of the scale (including 50 items dealing with perceptions and practices) was .980. Internal consistency reliab ilities for its components were .976 (Teachers Perceptions), .974 (Teachers Practices), .924 (level of inquiry ski lls), .886 (frequency of us e of inquiry skills), .932 (level of use of technology tools), and .915 (frequency of use of technology tools). Table 3-2 reports item analysis results fo r each individual item including 50 items that measures teachers self-reported perceptions and pr actices of using technology to attain the goals of scientific inquiry in science classrooms. The statistics show that no item should be deleted and all of the items contributed well to the reliability of the scale. Factor Analyses Sample Size Factor analytic research requires large sam ples (Guada gnoli & Velicer, 1988) and the number of subjects needed to undertake a factor analysis of an instrument depends on the number of items that are initia lly included in the instrument. There is, however, very little agreement among the researchers regarding sample si ze in factor analysis (Pett, et al., 2003). In deciding how many subjects to be us ed in this study, the research er relied on a rule of thumb, called the subjects-to-variabl es (STV) ratio (Bryant & Yarnold, 1995, p. 100). Bryant and Yarnold (1995, p. 100) and Nunnally (1978) explained that for the results to be reliable and to replicate if the analysis is repeated using an independent sample, the minimum number of observations in ones sample should be at leas t five times the number of variables. Other researchers suggested more conservative num bers (Gable & Wolf, 1993; Pett, Lackey, & Sullivan, 2003, p.148). Some researchers suggested rules in terms of the number of subjects

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60 required. According to Comrey and Lee (1992, p. 217), 300 subjects were acc epted as good and 500 subjects as very good. Tabachnick and Fidell (2001, p. 588), on the other hand, recommended at least 300 cases for factor anal ysis. Based on these criteria, the researcher administered the instrument resulting in data from 715 science teachers. B ecause the STV ratio is 715/50, or 14.3, the sample size was sufficiently large by the reliability criterion (Bryant & Yarnold, 1995, p.100). The researcher was also careful about the natu re of the sample sel ected. Although selection of the science teachers for the study was mainly based on convenience, this practice is not deemed problematic (Fabrigar, Wegener, MacC allum, & Strahan, 1999) unless the sample is overly homogeneous and its selection is related to measured variables (Fabrigar et al., 1999). Such a practice was reported to re sult in low estimates of factor loadings and correlations among factors (Comrey & Lee, 1992). To prevent this from happening, the rese archer collected over 5,000 email addresses from various states, c ontacted over 40 nationa l science teachers associations and joined listservs in order to get a sample as large and representative as possible. Exploratory Factor Analysis This section describes the objective f or sele cting the preferred me thod of extraction and rotation for factor analysis. In this study, principal factor analysis (PFA) method was pr eferred to the commonly used principal components analysis as suggested by Costelllo and Osborne (2005). PCA is only a data reduction method (Costelllo & Osborne, 2005) and does not differentiate between common and unique variance as factor analysis does (Fabriga r et al., 1999). It was s uggested that when the purpose of the study is to identify latent variables which are contributing to the common variance in a set of measured variables, PFA is the preferred method of extraction (Fabrigar et al., 1999). Fabrigar et al., (1999) suggest ed although principal components with varimax rotation and the

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61 Kaiser criterion are the norm, they are not optimal, particularly when data do not meet assumptions, as is often the case in the social sciences. Based on an an alysis of the related literature, Fabrigar et al., (1999) favored the use of a true fa ctor analysis extraction method (they preferred maximum likelihood), obli que rotation (such as direct obl imin), and use of scree plots plus multiple test runs to determine the number of meaningful factors in a data set in order to get optimal results (i.e., results that will generalize to other samples and that reflect the nature of the population). In this study, principal axis factoring me thod was preferred to maximum likelihood (ML), as one of MLs assumptions is multivariate nor mality (Fabrigar et al., 1999). A preliminary analysis of the data in this study did not indicate normally distributed data. Therefore, principal factors methods were preferred as they did not require any distributional assumptions (Fabrigar et al., 1999). As for the method to identify how many factors to retain, the researcher used the Kaiser criterion of eigenvalues of great er than 1.0 (Fabrigar et al., 1999; Pett et al., 2003) and parallel analysis (Fabrigar et al., 1999). As for the rotation method, oblique rotation method was preferred over orthogonal solutions because the latent variables in the study demonstrated evidence of correlation with each other (see Table 3-10 & Table 3-15). Oblique methods allow the factors to correlate (Costelllo & Osborne, 2005) and produce a better estimate of th e true factors and a better simple structure than will an orthogonal rotation (Fabrigar et al., 1999). Promax rotation method was chosen to achieve this objective. In addition to using principal axis factoring with promax ro tation, the researcher made use of additional statistics provided in SPSS software to identify any severe multicollinearity (an

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62 assumption of factor analysis) and to determine whether data is appropriate for undertaking an exploratory factor analysis. Although mild multicol linearity is not a problem for factor analysis (Field, 2005), the determinant, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Barletts test of sphericity were also used to understand whether the da ta were appropriate to run exploratory factor analysis in the first place. Table 3-5 presents the KMO measure of sa mpling adequacy and Barletts test of sphericity. The value for KMO was .975 and signif icance level for Barlett s test of sphericity was .000 (<.001). It was suggested in the literature that wi th a KMO value over .5 and a significant value for Barletts test ascertain that it would be judicious to proceed with factor analysis (Pett et al., 2003, p.78). A ll three statistics suggested that the sample size was sufficient relative to the number of items in the scale and th e correlations among the individual items were strong enough to suggest that the correlation matrix was factorable (Pett et al., 2003, p.78). After this step, the researcher ran parallel analysis to get a better sense of how many factors to extract from th e factor analysis. As shown in Table 3-3, the results suggested a 6-factor model. However, a principal axis factoring method with promax rotation in SPSS extracted 5 factors. Because the scale developed in this st udy was based on a 5-category model (based on literature review and National Sc ience Education Standards, 2000), the researcher decided to go with the 5-factor model. However, a preliminary evaluation of this model based on communalities, total variance explai ned, pattern matrix, and struct ure matrix (Tables 3-6 through 3-9, respectively) did not produce interpretable fact ors considering the nature of the items in the scale. Communalities of items ranged from .604 to .766, suggesting that a high percentage of variance in a given item was explained by all five factors. Moreover, Table 3-7 illustrated that the five factors extracted were ab le to explain 69.4% of the varian ce. As shown in pattern (Table

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63 3-8) and structure matrices (Table 3-9), 19 item s loaded on the first item and 22 items loaded on the second item. Only 9 items loaded on two other factors (6 on factor 3; 3 on factor 4). None of the items loaded on the fifth factor. Because the items were originally constructed based on five essential features of scientif ic inquiry proposed by the Nationa l Science Education Standards (2000), as illustrated in Table 34, the researcher concluded that a five factor solution was not statistically supported by the data and that the five essential featur es of scientific inquiry outlined in the National Science Education Standards was i ndeed a conceptual categor ization rather than a data-driven theoretical one. Therefore the researcher ran a two-factor model using principal axis method with promax rotation considering the fact that the items in the scale were constructed in terms of teacher perceptions and practices (see Table 3-4 for the breakdown of items into these two categories). That is, statements starting with I integr ate constituted the practices items, whereas statements starting with A science teacher constitute d the perceptions items. A new factor analysis that was limited to a two-factor extraction (see Table 3-10 through Table 3-15) was able to separate items measuring perceptions fr om items measuring practices. The correlation between the two f actors was .629 (Table 3-15) and this two-factor solution was able to explain 61% of the total variation (Table 3-12). Communalities of individual items ranged between .368 and .752 (Table 3-11). Alth ough some of these communalities seemed lower than the ones produced in the five-factor m odel, they were kept due to these items high values of interpretability a nd their contributions to a welldefined factor (either teacher perceptions or teacher practices). A careful evaluation of values reported both in the pattern and the structure matrices together demonstrated a pe rfect distribution of items into categories coded as teacher perceptions and teacher practices are outlined in Table 3-4.

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64 Hence, this study and the SIT-TIPPS instru ment support 2 factors perceptions and practices. During the instrument development process, the research er found that although National Research Council (1996) reports 5 psychol ogical factors in the a ttainments of teaching scientific inquiry in the National Science Edu cation Standards, these five factors are not statistical factors. Models show ed that creating the SIT-TIPPS with two factors, perceptions and practices, created a better model for measuring teachers self-reported pe rceptions and practices regarding the use of technology to enact scientific inquiry. Multiple Regression A m ultiple regression analysis was conducted to examine the degree of association between various outcome variables and explorat ory variables. Four regression models were tested using the stepwise regression method with SPSS 13.0. Fo r the first two models, the outcome variables were the teachers perceptions and practices regarding the use of technology for scientific inquiry purposes. The exploratory variables for th ese models were the level of preparedness for using inquiry skills; the frequenc y of using inquiry skills in instruction; the level of preparedness for using technology tools; the frequenc y of using technology tools in instruction; gender; race/ethnicity; years of teaching experience; number of grades taught; the level of grades taught (high numbers meaning that a particular teache r taught higher grades ranging from 6th grade to 12th grade and higher); number of sc ience courses taught; number of computers in class; the presence of a computer lab; number of computers in computer lab; number of science labs; the pres ence of a science lab in classr oom; and previous educational technology training. The other two regression equations tested sma ller models in which two of the exploratory variables in the previous two models (the frequency of using inquiry skills in instruction and the frequency of using technology tools in instruction) served as outcome variables separately in one

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65 of the two models. These models, then, investigat ed the influence of some of the exploratory variables (included in the first two models) on both of these two outcome variables separately. Overall, no multicollinearity problem was observed among the variables because the variance inflation factor (VIF) values in all six models was le ss than 2.0, which indicated that collinearity was not a problem (Miles & Shevlin, 2001). The first regression model consisted of 16 expl oratory variables and the outcome variable: Teachers self-reported perceptions regarding the use of technology for scientific inquiry purposes. Table 3-16 indicates the unst andardized regression coefficients ( b), the standardized regression coefficients (), the observed t-values ( t ), and the p-values ( p). The second regression model consisted of 16 exploratory variables and the outcome variable: Teachers practices rega rding the use of technology for scientific inquiry purposes. Table 3-17 reports the unstandard ized regression coefficients (b), the standardized regression coefficients (), the observed t-values ( t ), and the p-values ( p) for the model. The third regression model consis ted of 5 exploratory variable s (the level of preparedness for using inquiry skills, years of experience, tota l number of grades taught, the level of grades taught, and the number of science courses taught) and the outcome variable: Frequency of using scientific inquiry skills in instruction. Ta ble 3-18 reports the unstandardized regression coefficients (b), the standardized regression coefficients (), the observed t-values ( t ), and the pvalues ( p ) for this model. The fourth regression model consisted of 8 expl oratory variables (the level of preparedness for using technology tools, years of experience, to tal number of grades taught, the level of grades taught, and the number of science courses taught, the number of computers in classroom, the number of computers in comput er lab, and previous educationa l technology training) and the

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66 outcome variable: Frequency of using technolo gy tools in instruction. Table 3-19 reports the unstandardized regression coefficients ( b), the standardized re gression coefficients (), the observed t-values ( t ), and the p-values ( p) for this model. Summary Chapter 3 described the m ethods used to develo p the SIT-TIPPS instrument as well as to investigate the research questions of the study. The results of these analyses are described in Chapter 4.

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67 Table 3-1. Descriptive statis tics and reliability index of the SIT-TIPPS instrument Components N MinimumMaximumMean Std. Dev. Reliability Perceptions 588 25125106.115.0 .976 Practices 595 2512598.517.7 .974 Inquiry skillsLevel 616 93629.45.4 .924 Inquiry skillsFrequency 606 94532.55.8 .886 Technology tools-Level 562 2610472.115.3 .932 Technology tools-Frequency 530 2913063.516.3 .915 Overall (Perceptions & Practices items) .980

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68 Table 3-2. Item analysis results Item Number Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbachs Alpha if Item Deleted 1 200.82 823.47 .627 .980 2 200.39 829.42 .603 .980 3 200.48 825.74 .637 .980 4 200.68 823.87 .592 .980 5 200.87 819.95 .639 .980 6 200.70 822.94 .684 .980 7 200.76 821.06 .684 .980 8 200.43 828.29 .583 .980 9 200.94 815.90 .718 .980 10 200.93 815.82 .735 .979 11 200.85 818.44 .744 .979 12 200.62 822.11 .747 .979 13 200.39 829.07 .637 .980 14 200.86 818.36 .745 .979 15 200.91 815.48 .710 .980 16 200.39 829.31 .618 .980 17 201.37 815.46 .648 .980 18 201.18 814.88 .665 .980 19 201.01 815.69 .733 .979 20 200.66 823.73 .675 .980 21 200.52 825.24 .719 .980 22 201.16 816.30 .689 .980 23 200.62 822.70 .727 .980 24 200.86 817.21 .710 .980 25 201.21 814.29 .699 .980 26 200.70 820.51 .680 .980 27 200.65 823.23 .642 .980 28 200.75 817.35 .743 .979 29 200.45 825.35 .688 .980 30 200.66 820.98 .731 .980 31 201.01 815.60 .757 .979 32 200.62 822.74 .739 .980 33 200.68 821.85 .645 .980 34 200.67 820.54 .753 .979 35 200.70 820.12 .756 .979 36 200.66 820.26 .746 .979 37 200.94 812.66 .798 .979 38 200.73 821.28 .651 .980 39 200.66 820.33 .753 .979 40 200.63 823.40 .708 .980 41 200.75 818.76 .659 .980

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69 Table 3-2. Continued. 42 200.88 820.53 .679 .980 43 200.75 820.96 .715 .980 44 200.71 818.37 .753 .979 45 200.99 814.85 .724 .980 46 200.85 815.38 .752 .979 47 200.76 819.81 .713 .980 48 200.91 814.19 .777 .979 49 200.64 822.03 .719 .980 50 200.78 818.75 .726 .980 Table 3-3. Parallel analysis: PAF/common factor analysis & random normal data generation (N=557, Nvariables=50) Root Raw Data Means Percentile 1.000000 25.372641 .731735 .799033 2.000000 5.327737 .662761 .702616 3.000000 1.960368 .614535 .656174 4.000000 1.419856 .576870 .618403 5.000000 .827231 .537975 .572217 6.000000 .628507 .506394 .540146 7.000000 .462105 .475422 .507302 8.000000 .428912 .444682 .480694 9.000000 .381882 .415302 .441793 Table 3-4. Breakdown of 50 items into categories and related factors Category Perception factorPractice factor Teacher engages students in scientifically oriented questions 3, 21, 26,29,301, 4, 5, 28, 46 Teacher encourages students to give priority to evidence 2, 8, 13, 16, 4419, 27, 33, 38, 41 Teacher helps students formulate explanations from evidence to address scientifically oriented questions 12, 20, 32, 35, 36, 40, 49 7, 10, 11, 15, 24, 37, 45 Teacher helps students connect explanations to scientific knowledge 23, 34, 39, 509, 14, 25, 48 Teacher encourages students to communicate and justify their proposed explanations 6, 42, 43, 4717, 18, 22, 31

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70 Table 3-5. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy .975 Approx. Chi-Square 28979.8 31 df 1225 Bartlett's Test of Sphericity Sig. .000

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71 Table 3-6. Communalities Item number Initial Extraction 1 .651 .604 2 .725 .705 3 .772 .775 4 .683 .666 5 .671 .615 6 .682 .650 7 .702 .696 8 .713 .631 9 .750 .708 10 .747 .700 11 .719 .649 12 .767 .702 13 .720 .665 14 .709 .655 15 .706 .620 16 .708 .683 17 .667 .647 18 .696 .612 19 .747 .673 20 .659 .632 21 .751 .742 22 .686 .608 23 .732 .660 24 .713 .662 25 .677 .625 26 .701 .655 27 .684 .636 28 .748 .723 29 .715 .684 30 .741 .708 31 .783 .720 32 .777 .749 33 .772 .760 34 .801 .759 35 .805 .763 36 .821 .763 37 .806 .766 38 .762 .700 39 .818 .756 40 .762 .674 41 .764 .716 42 .775 .736 43 .821 .749

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72 Table 3-6. Continued. 44 .807 .753 45 .699 .644 46 .794 .740 47 .799 .751 48 .773 .747 49 .805 .750 50 .757 .713 Extraction method: Prin cipal Axis Factoring. Table 3-7. Total variance explaine d: PAF with promax rotation Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadingsa Total % of Variance Cumulati ve % Total % of Variance Cumulati ve % Total 1 25.627 51.254 51.25425.32550.65150.651 20.559 2 5.579 11.157 62.4125.28010.55961.210 20.207 3 2.236 4.472 66.8841.9213.84265.052 13.474 4 1.688 3.377 70.2611.3862.77367.825 7.593 5 1.108 2.216 72.476.7861.57169.397 .846 6 .894 1.789 74.265 7 .727 1.453 75.718 8 .685 1.369 77.088 9 .646 1.293 78.381 10 .571 1.143 79.523 Extraction method: Prin cipal Axis Factoring. a When factors are corre lated, sums of squared loadings cannot be added to obtain a total variance.

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73 Table 3-8. Pattern matrix Factor Item number 1 2345 1 -.150 .392.568.027-.028 2 .221 -.207.713.207.085 3 .274 -.213.768.121.063 4 -.162 .322.698-.084.188 5 -.222 .505.359.279-.080 6 .428 -.059.451.127-.108 7 -.022 .380.617-.128.087 8 .123 -.074.446.483-.056 9 -.059 .593.409-.072-.125 10 -.051 .625.335-.002-.123 11 .031 .698.117.049.009 12 .673 .033.078.197-.045 13 .324 .033.091.556.078 14 .037 .694.074.107.093 15 .059 .743.034-.029.040 16 .332 .005.055.607.011 17 .112 .736-.037-.119-.286 18 .151 .714-.018-.147-.185 19 .061 .797-.053.044-.049 20 .688 -.010.028.178-.133 21 .596 .034-.037.437-.015 22 .150 .751-.086-.071-.089 23 .703 .038.067.108.019 24 .084 .723.058-.077.227 25 .180 .687.061-.198-.062 26 .780 -.049.064.045.068 27 -.037 .671.135-.024.338 28 .117 .651.132-.047.323 29 .667 -.022.126.107.292 30 .635 .079-.047.330-.066 31 .125 .828-.074-.042.002 32 .835 .003.054-.013.099 33 -.246 .824-.092.408.057 34 .820 .019.095-.040-.011 35 .844 .057-.003-.004-.004 36 .836 .021-.039.106.053 37 .145 .791-.107.114.100 38 -.190 .788-.069.352.074 39 .819 .024-.006.094.016 40 .797 .034.016-.011.059 41 -.180 .806-.098.361.032 42 .839 .084-.098-.054-.242

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74 Table 3-8. Continued. 43 .865 .029-.028-.037-.122 44 .824 .075-.028.031-.058 45 .097 .755-.087.088-.090 46 .040 .830-.131.169.077 47 .901 .034-.060-.065-.011 48 .181 .777.000-.121.146 49 .842 .015.018-.036.202 50 .820 .172-.087-.115.104 Extraction method: Princi pal Axis Factoring. Rotation Method: Promax with Kaiser Normalization. Rotation converged in 7 iterations.

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75 Table 3-9. Structure matrix Factor Item number 1 2345 1 .387 .632.714.282-.053 2 .570 .376.781.463.055 3 .618 .402.835.412.021 4 .360 .591.750.204.146 5 .361 .671.612.471-.069 6 .691 .467.699.409-.134 7 .474 .667.768.198.043 8 .510 .398.634.647-.045 9 .467 .762.688.223-.150 10 .479 .780.658.282-.137 11 .499 .796.534.328.010 12 .810 .513.530.486-.048 13 .601 .445.463.727.113 14 .500 .791.507.380.101 15 .476 .784.464.251.039 16 .596 .418.437.754.051 17 .460 .737.407.127-.292 18 .485 .739.421.123-.196 19 .491 .816.437.307-.039 20 .768 .444.462.434-.135 21 .761 .487.450.661.011 22 .495 .763.392.197-.088 23 .801 .499.504.411.009 24 .481 .778.464.230.219 25 .520 .755.477.110-.082 26 .803 .433.472.352.050 27 .391 .719.457.254.333 28 .524 .775.521.281.313 29 .758 .454.495.420.278 30 .780 .512.454.576-.050 31 .527 .842.436.252.005 32 .859 .492.500.331.076 33 .314 .771.359.559.108 34 .867 .512.539.307-.039 35 .872 .521.486.334-.023 36 .865 .497.458.422.045 37 .566 .851.440.401.117 38 .340 .760.373.521.117 39 .864 .505.483.412.006 40 .818 .480.461.313.039 41 .350 .771.366.527.077 42 .817 .475.401.240-.260

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76 Table 3-9. Continued. 43 .855 .480.454.282-.144 44 .863 .525.475.354-.073 45 .503 .788.416.335-.075 46 .491 .836.399.420.103 47 .863 .478.428.268-.033 48 .562 .838.479.214.136 49 .842 .480.461.312.179 50 .822 .541.410.233.082 Extraction method: Princi pal Axis Factoring. Rotation method: Promax with Kaiser Normalization. Table 3-10. Factor correlation matrix Factor 1 2 345 1 1.000 .554 .544.380-.023 2 .554 1.000 .552.327.006 3 .544 .552 1.000.327-.058 4 .380 .327 .3271.000.085 5 -.023 .006 -.058.0851.000 Extraction method: Princi pal Axis Factoring. Rotation method: Promax with Kaiser Normalization.

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77 Table 3-11. Communalities Item number Initial Extraction 1 .651 .456 2 .725 .412 3 .772 .464 4 .683 .412 5 .671 .504 6 .682 .541 7 .702 .511 8 .713 .368 9 .750 .608 10 .747 .638 11 .719 .652 12 .767 .694 13 .720 .456 14 .709 .651 15 .706 .612 16 .708 .441 17 .667 .494 18 .696 .508 19 .747 .651 20 .659 .609 21 .751 .639 22 .686 .551 23 .732 .663 24 .713 .607 25 .677 .550 26 .701 .651 27 .684 .537 28 .748 .628 29 .715 .600 30 .741 .653 31 .783 .684 32 .777 .730 33 .772 .621 34 .801 .741 35 .805 .746 36 .821 .752 37 .806 .726 38 .762 .600 39 .818 .752 40 .762 .659 41 .764 .611 42 .775 .620 43 .821 .701

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78 Table 3-11. Continued. 44 .807 .734 45 .699 .608 46 .794 .691 47 .799 .707 48 .773 .692 49 .805 .692 50 .757 .638 Extraction method: Prin cipal Axis Factoring. Table 3-12. Total variance explained Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadingsa Total % of Variance Cumulative % Total % of Variance Cumulative % Total 1 25.627 51.254 51.25425.25050.50050.500 21.826 2 5.579 11.157 62.4125.21310.42560.925 21.234 3 2.236 4.472 66.884 4 1.688 3.377 70.261 5 1.108 2.216 72.476 6 .894 1.789 74.265 7 .727 1.453 75.718 8 .685 1.369 77.088 9 .646 1.293 78.381 Extraction method: Prin cipal Axis Factoring. a When factors are corre lated, sums of squared loadings cannot be added to obtain a total variance.

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79 Table 3-13. Pattern matrix Factora Item number 1 2 1 .047 .644 2 .563 .116 3 .603 .115 4 .035 .619 5 -.008 .715 6 .661 .111 7 .131 .625 8 .478 .178 9 .036 .756 10 .040 .773 11 .051 .774 12 .805 .045 13 .572 .148 14 .061 .767 15 .015 .772 16 .591 .109 17 .033 .681 18 .064 .671 19 .017 .796 20 .800 -.031 21 .768 .049 22 .059 .704 23 .793 .032 24 .024 .764 25 .096 .678 26 .849 -.070 27 -.051 .764 28 .099 .727 29 .759 .024 30 .766 .064 31 .038 .803 32 .873 -.030 33 -.168 .883 34 .867 -.009 35 .868 -.007 36 .890 -.037 37 .105 .782 38 -.126 .848 39 .882 -.023 40 .821 -.015 41 -.121 .852 42 .810 -.038

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80 Table 3-13. Continued. 43 .873 -.059 44 .854 .004 45 .065 .738 46 .010 .825 47 .881 -.066 48 .088 .774 49 .849 -.027 50 .751 .072 Extraction method: Princi pal Axis Factoring. Rotation method: Promax with Kaiser Normalization. a Rotation converged in 3 iterations.

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81 Table 3-14. Structure matrix Factor Item number 1 2 1 .453 .674 2 .636 .470 3 .675 .494 4 .424 .641 5 .442 .710 6 .731 .527 7 .524 .708 8 .590 .479 9 .512 .779 10 .526 .798 11 .538 .807 12 .833 .551 13 .665 .508 14 .544 .806 15 .501 .782 16 .659 .480 17 .462 .702 18 .486 .711 19 .518 .807 20 .780 .472 21 .799 .532 22 .501 .741 23 .814 .531 24 .505 .779 25 .522 .738 26 .805 .464 27 .429 .731 28 .556 .789 29 .774 .501 30 .807 .546 31 .543 .826 32 .854 .519 33 .388 .777 34 .861 .536 35 .864 .540 36 .867 .523 37 .597 .848 38 .407 .768 39 .867 .532 40 .812 .501 41 .415 .776 42 .787 .472

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82 Table 3-14. Continued. 43 .836 .490 44 .856 .541 45 .529 .778 46 .529 .831 47 .839 .488 48 .575 .829 49 .831 .507 50 .796 .545 Extraction method: Princi pal Axis Factoring. Rotation method: Promax with Kaiser Normalization. Table 3-15. Factor correlation matrix Factor 1 2 1 1.000 .629 2 .629 1.000 Extraction method: Princi pal Axis Factoring. Rotation method: Promax with Kaiser Normalization Table 3-16. Regression analysis summary for teachers perceptions factor Variable b t-valuesp-values Constant 73.43 14.57.000* Inquiry skills-frequency .619.2324.15.000* Technology tools-level .181.1803.22.001* Note. R2 = .119 (n = 514, p = .000) *p< .05. Table 3-17. Regression analysis summary for teachers practices factor Variable b t-valuesp-values Constant 38.97 7.86.000* Inquiry skills-frequency 1.202.4017.99.000* Technology tools-level .171.1512.58.010* Technology tools-frequency .155.1502.54.012* Note. R2 = .331 (n = 462, p = .000) *p< .05.

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83 Table 3-18. Regression analysis summary for frequency of using inquiry skills Variable b t-valuesp-values Constant 16.70 14.25.000* Inquiry skills-level .532.50213.62.000* Note. R2 = .252 (n = 553, p = .000) *p< .05. Table 3-19. Regression analysis summary for frequency of using technology tools Variable b t-valuesp-values Constant 1.09 .339.735 Technology tools-level .681.63017.50.000* Years of experience .261.1534.28.000* Previous ed tech training 2.33.1514.21.000* Number of computers in classroom .232.1042.99.003* Number of computers in computer lab .117.1032.95.003* Note. R2 = .499 (n = 428, p = .000) *p< .05.

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84 CHAPTER 4 PRESENTATION AND ANALYSIS OF DATA This study developed an instrum ent, the SIT-TIPPS, that examined middle and high school science teachers self-reported percep tions and practices of using technology for scientific inquiry purposes in their classrooms. After a review of the relevant literature, specific variables were identified to construct the survey and included in the analysis to answer the research questions. These va riables included teach ers self-reported perceptions and practices of using technology to attain the goals of scientific inquiry, level and frequency of integrating certain inquiry skills and tec hnology tools in science classrooms, gender, race/ethni city, years of teaching experience, grades taught, science courses taught, access to computers, computer labs, and science labs, and time period in which educational technology courses had b een taken. This chap ter will present the analysis of data used to answer the study research questions. Study Research Questions Overarching Questions 1. How are teachers using technology to im plem ent the goals of scientific inquiry in their classrooms? 2. What are the relationships between teacher s self-reported perceptions and practices regarding the use of technology to atta in the goals of sc ientific inquiry? Supporting Questions What are the relationsh ips between teacher s self-reported perceptions and practices regarding the use of technology to attain the goals of scientific i nquiry in terms of: Teacher demographics and teacher backgr ound/professional development variables? How often do teachers support students to en gage in certain inquiry skills in their science classrooms? How often do teachers use certain technol ogy tools in their science classrooms?

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85 How prepared do teachers feel to support studen ts to engage in certain inquiry skills in their science classrooms? How prepared do teachers feel to use cer tain technology tools in their science classrooms? Demographic Reporting of the Sample In order to p rovide a thorough descripti on of the middle and high school teachers and the context in which they teach, the demographic responses for study data will be presented. Demographic Characteristics As noted in chapter 3, a total of 715 science teachers self-reported responses com prised the data for this study. Of th e 598 respondents (83.6%) who reported their gender, 207 (34.6%) were males and 391 (65.4%) were females (Table 4-1). Of the 590 participants (82.5%) who repor ted their racial/ethnic iden tity, 479 (81.2%) were White, 34 (5.8%) were Black, 27 (4.6%) were Multiracial, 26 (4.4%) were Hispanic, 20 (3.4%) were Asian, and 4 (.7%) were Am erican Indian (Table 4-2). Virginia and Florida were the two states with the highest numbe r of participants (Table 4-3). One hundred and sixty five (27.5%) participants were fr om Virginia and 156 were from Florida (26%). The third highest participation was from Kansas with 50 participants (8.3%). Teaching Experience The survey included open-ended statem ents to collect pertinent information from science teachers regarding their years of sc ience teaching experience (Table 4-4), grade levels (Table 4-5) and science courses (Table 4-6) taught, and certi fication areas. Because the range of responses science teachers provi ded on their certification areas was very wide, it is not reported by category. Therefor e, the researcher made the assumption the

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86 information provided about the science cour ses taught was an indication of areas in which they were certified. The number of years served (Table 4-4) as a science teacher ranged from 0 to 44 years with a mean of 13.5 years (n=5 83). Of the 583 teachers who responded, 244 (41.9%) fell in the 1 to 9-year range; 187 (32.1%) fell in the 10 to 19-year range; 99 (17.0%) fell in the 20 to 29-y ear range; 49 (8.4%) fell in th e 30 to 39-year range; and 4 (.7%) fell in the 40 to 44-year range. Results indicated that 171 teachers (2 3.9%) taught grade 6; 238 (33.3%) taught grade 7; 252 (35.2%) taught grade 8; 345 (48.3%) taught grade 9; 360 (50.3%) taught grade 10; 378 (52.9%) taught grade 11; 371 (51.9%) taught grade 12; and 130 (18.2%) taught grades over 12 (Table 4-5). Seventyone teachers (9.9%) ta ught only one grade level; 82 (11.5%) taught tw o grade levels; 124 (17.3%) ta ught three grade levels; 149 (20.8%) taught four grade leve ls; 79 (11.0%) taught five gr ade levels; 39 (5.5%) taught six grade levels; 35 (4.9%) ta ught seven grade levels; and 21 (2.9%) taught eight grade levels (Table 4-5). On the survey, when asked about what science courses they taught, science teachers reported a variety of course names which the researcher then categorized these responses into five categories: Life sciences, earth sc iences, physical/general sciences, physics, and chemistry. The results showed that 364 t eachers (50.9%) taught life science; 283 (39.6%) taught earth science; 369 (51.6%) taught physical/general sciences; 142 (19.9%) taught physics; and 194 (27.1%) taught chemistry (T able 4-6). One hundred and sixty seven (23.4%) teachers taught only one course; 195 (27.3%) taugh t two courses; 161 (22.5%)

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87 taught three courses; 58 (8.1%) taught four courses; and 16 (2.2%) taught 5 courses (Table 4-6). Computer Access and Knowledge Computer Access The dem ographics/teacher background sect ion of the SIT-TIPPS instrument also asked for information about whether science teachers had computers in their classrooms and the number of computers, whether they had access to computer labs and the number of computers in these labs, th e number of science labs at th eir schools, whether they had a science lab in their classrooms and whether they had anyone av ailable at their schools to provide technology support. The results indicated that 592 out of 599 teachers (98.8%) had computers in their classrooms. The number of computers in th eir classrooms ranged from 1 to 37 with a mean of 6.12 and a standard deviation of 7.63. Many teachers (36.1%) who specified the number of computers in their classrooms (n = 590) had 1 co mputer in their classrooms. The cumulative percentage calculations indica ted that 80.3% of the teachers had 1 to 9 computers in their classr ooms (see Table 4-12). When asked if teachers had access to co mputer labs, 550 out of 590 teachers (93.2%) indicated that they had access to computer labs. Responses from those who specified the number of comput ers in computer labs (n = 544) showed that the number of computers in computer labs ranged from 2 to 200 (including all com puter labs at their schools) with a mean of 28.22 and a standard deviation of 12.91. Science teachers were also asked to report the number of science labs to which they have access. According to the results, science teachers had access to an average of 2.44 science labs with a standa rd deviation of 3.72. Of the 514 teachers who responded, 62.8%

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88 reported having access to 1 science lab; 12.5% had 2 labs; 9.7% had 3 labs; and 4.9% had 4 labs. This shows that cumula tively 89.9% of science teachers had 1 to 4 science labs at their schools. The researcher also asked if the participants had a science lab in their classrooms. Of the 594 teachers who responded, 72.7% indicated that they had a science lab within their classrooms. When asked about if they had anyone availa ble at their school to provide them with technology support, 93.9% of the teachers repor ted having someone available to them for receiving technology support when needed. Th is indicates that schools are providing teachers with at least some limited technical support on a daily basis. Source of Computer Knowledge The researcher asked science teach ers to indicate whether they took educational technology classes in high school, undergraduate school, graduate school, or in-service or continuing education courses. As shown in Table 4-13, the majority of the science teachers (92.3%) reported receiving educational technology classes during in-service trai ning or continuing education courses. More teachers reported taking educational technology classes in graduate school (54.4%) than receiving it in undergraduate school (47.3%). Only 28.3% of the science teachers took educational technology classes in high school. Because teachers were able to select more than one op tion to report at what stage in their career they took educational technology courses, the researcher attempted a new calculation to see in how many of these levels (high school, unde rgraduate school, graduate school, and in-service or continuing education) a pa rticular science teac her reported taking educational technology classes. Results indicated that 187 teachers (26.2%) took

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89 educational technology classes at only one of these levels; 204 (28.5%) at two levels; 122 (17.1%) at three levels; and 66 (9.2 %) at all of the four levels. Answering Research Question 1 This research question w as answered by exploring supporting questions 2 and 3. Regarding the statements dealing with teachers frequency of use of the inquiry skills and technology tools in instruction, teachers rated their use based on a 5-point Likert scale in this order: Never; rarely (e.g., a few times a year); sometimes (e.g., once or twice a month); often (e.g., once or tw ice a week); and almost or all science lessons. The results on the frequency of use of these inquiry skills (Table 4-7) indicated that supporting students to explain cause-effect relationships (M = 4.0, 28.2%) and supporting students to discuss scientific explanati ons/ideas/models with ot hers (M = 3.88, 28.2%) were the two highest rated skills by the teach ers. Approximately twenty-eight percent of the science teachers report ed that they integrate these skills in most or all science lessons. Supporting students to conduct experiments (M = 3.70, 11.5%) and to collect, organize, and analyze data (M = 3.78, 12.6%) were also among the highest rated skills. Over 12.5% of the teachers reported integrating them in almost or all science lessons. The results showed the inquiry skills with the lowest mean scores were: to support students to identify their own misconceptions of science content (M = 3.10), to find biases and flaws in their scien tific explanations (M = 3.10), to test scientific explanations against current scientific knowledge (M = 3.12), and to critiq ue experiments (M = 3.17). About 21% of teachers reported th at they felt either not adequately prepared or somewhat prepared to implement these skills in thei r instruction. Regarding the frequency with which they integrated these ski lls in their lessons, the result s were almost parallel. Having the options never, rarely (e.g., a few tim es a year), and sometimes (e.g., once or

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90 twice a month) treated as low integration of inquiry skills in instruction, critiquing experiments was the least integrated skill (M = 3.20). Of the 624 teachers who rated this skill, 59.2% selected the never, rarely, and so metimes options. The other two lowest rated skills in this category were testing scientif ic explanations against current scientific knowledge (M = 3.31) and finding biases or flaw s in scientific explanations (M = 3.39) with 56.5% selecting never, rarely, and sometimes options for the former and 53.5% selecting the same options for the latter. When asked about how often they used technology tools (Table 4-8). Teachers responses showed that word processing (M = 4.16), which was the tool they felt more prepared to use, was again the most used technology tool in less ons. About 47% of the teachers reported they used word processing in most or all science lessons. The other highly rated technologies from high to low were presentation devices (M = 4.12, 45.6%); presentation software (M = 3.86, 38.1%); Inte rnet searches (M = 3.67, 25.4%); and email (M = 3.52, 34.7%) (Table 4-8). The least frequently used technologies by science teachers were from lowest to highest were videoconferencing, telec onferencing (M = 1.29); portable Global Positioning Systems (M = 1.44); blogs (M = 1.45); data collection, telecollaborative activities (M = 1.50); video editing (M = 1.54); wikis (M = 1.56); podcasts, videocasts (M = 1.63); wireless communi cation devices (M = 1.69); im age/picture editing (M = 2.00); and virtual experiences (M = 2.16). When never and rarely (e.g, a few times a year) options were considered a non-use, 93.1% of teachers reported almost never using videoconferencing, teleconferencing in their instruction. Th e percentages were 89.7% for portable Global Positioning Sy stems; 88.1% for blogs; 87.5% for data

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91 collection, telecollabora tive activities; 86.7% for video editing; 84.7% for wikis; 82.6% for podcasts, videocasts; 80.4% for wire less communication devices; 69.7% for image/picture editing; and 65.9% for virtual experiences. Answering Research Question 2 As m entioned in the methodology secti on, frequencies, correlations, multiple regression models, t-tests, and ANOVAs were us ed to determine the relationship between teachers perceptions and practices of using technology tools/applications for scientific inquiry purposes. This research question us ed teachers self-reported responses from supporting questions 4 and 5 along with data from research questions 1. Level of Use of Inquiry Sk ills and Technology Tools Regarding the statem ents dealing with t eachers level of preparedness, science teachers rated their level of inquiry skills and technology tool s based on a 4-point Likert scale ranging from not adequately prepared to very well prepar ed (i.e., not adequately prepared; somewhat prepared; fairly well prepared; and very well prepared). When asked about how well prepared they felt about supporting students to achieve certain inquiry tasks, science teachers felt v ery well prepared in supporting students to collect, organize, and analyze data (M = 3.51, 58.7%), explain cause-effect relationships (M = 3.42, 52.1%), and conduct experiments (M = 3.41, 53.4%). The rest of the inquiry skills produced lower mean scores and percentages (see Table 4-9). Regarding science teachers self-reported level of preparedness in using certain technology tools, the results of the study indica ted that (Table 4-10) science teachers felt more prepared to use (from highest to lowe st) word processing (M = 3.85); e-mail (M = 3.80); Internet searches (M = 3.70); presenta tion software (M = 3.69) ; spreadsheets (M =

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92 3.56); and presentation devices (M = 3.55). This equated to 87% of the teachers feeling very well prepared to use word processi ng in their classrooms; 84.4% to use email; 75.6% to use Internet search es; 68.1% to use spr eadsheet; and 67.5% to use presentation devices. On the other hand, teachers felt less prepared for using certain technologies in their classrooms. Using the options not adequa tely prepared and somewhat prepared considered together, teachers rated (from lowest to hi ghest) low on data collection telecollaborative activities (M = 1.85, 75.6%); videoconferen cing, teleconferencing (M = 1.86, 74.5%); portable Global Positioning System s (M = 2.00, 69.7); video editing (M = 2.00, 69.3%); podcasts, videocasts (M = 2.10, 65.6%); blogs (M = 2.13, 64.8%); wikis (M = 2.13, 63.3%); wireless communicat ion devices (M = 2.16, 63.3%); Smart Board/Promethean interac tive boards (M = 2.34, 56.2%); and webpage design (M = 2.37, 56.4%). Correlational Analysis Correlations were com puted among the te achers self-reported perceptions and practices, teachers level of preparedness in using certain scientific inquiry skills and technology in their classrooms, the frequenc y of use of these inquiry skills and technology tools during instruction, number of years of experi ence teaching science, total number of grades taught, the level of gr ades taught (high numbers meaning that a particular teacher taught higher grades ranging from 6th grade to 12th grade and higher), the number of computers in classroom and computer labs, and the total number of educational opportunities (high school, undergraduate school, graduate school, and/or inservice training or continuing education) in which they received e ducational technology training. Table 4-11 reports the Pearson Product-Moment Correlation coefficients and

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93 level of significance among the aforementioned variables. Some of the correlations will not be discussed in the chapter because they do not offer a practical interpretation. Only the correlations that appear in bold will be reported and inte rpreted throughout this text. Teachers self-reported perceptions regardi ng the use of technology for scientific inquiry purposes in science classrooms were positively correlated with: Teachers practices of using technology for scientific inquiry purposes in their own classrooms ( r = .649, p<.001); the level of preparedness fo r using certain inquiry skills ( r = .225, p<.001); the frequency of using certain inquiry skills ( r = .267, p<.001); the level of preparedness for using certain technology tools in instruction (r = .233, p<.001); the frequency of using certain technology tool s in instruction ( r = .189, p <.001); and the total number of educational levels in which they r eceived educational technology training ( r = .136, p= .001). Teachers practices of using technology for scientific inquiry purposes in their own classrooms were positively correlated with: Teachers self-reported perceptions regarding the use of technology for scie ntific inquiry purposes ( r = .649, p<.001); the level of preparedness for using certain inquiry skills ( r = .283, p<.001); the frequency of using certain inquiry skills ( r = .456, p<.001); the level of prepar edness for using certain technology tools in instruction ( r = .384, p<.001); the frequency of using certain technology tools in instruction ( r = .443, p<.001); the number of computers in class ( r = .109, p= .010); the number of computers in computer lab ( r = .136, p= .002); and the total number of educational levels in which th ey received educational technology training ( r = .157, p= .001).

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94 The level of preparedness for using certain inquiry skill s was significantly correlated with: Teachers self-reported per ceptions regarding the use of technology for scientific inquiry purposes ( r = .225, p<.001); teachers practices regarding the use of technology for scientific inquiry purposes ( r = .283, p<.001); the frequency of using certain inquiry skills ( r = .522, p<.001); the level of prepar edness for using certain technology tools in instruction ( r = .448, p<.001); the frequency of using certain technology tools in instruction ( r = .275, p<.001); total number of grades taught ( r = .151, p<.001); and the level of grades taught ( r = .144, p<.001). The frequency of using certain inquiry skills variable was positively correlated with: Teachers self-reported perceptions regarding the use of technology for scientific inquiry purposes ( r = .267, p<.001); teachers practices rega rding the use of technology for scientific inquiry purposes ( r = .456, p<.001); the level of preparedness for using certain inquiry skills ( r = .522, p<.001); the level of prepar edness for using certain technology tools in instruction ( r = .423, p<.001); the frequency of using certain technology tools in instruction ( r = .450, p<.001); and the number of computers in computer lab ( r = .088, p = .043). The level of preparedness for using certai n technology tools in instruction variable was positively correlated with: Teachers self-reported perceptions regarding the use of technology for scientific inquiry purposes ( r = .233, p<.001); teachers practices regarding the use of technology for scie ntific inquiry purposes ( r = .384, p<.05); the level of preparedness for using certain inquiry skills ( r = .448, p<.001); the frequency of using certain inquiry skills ( r = .423, p<.001); the frequency of usi ng certain technology tools in instruction ( r = .671, p<.001); total num ber of grades taught ( r = .118, p= .005); the level

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95 of grades taught ( r = .121, p= .004); and the total number of educational levels in which they received educationa l technology training ( r = .249, p= .001). It was, however, negatively correlated with the number of years of teaching experience ( r = -.177, p<.001). The frequency of using certain technol ogy tools in instruction variable was positively correlated with: Teachers self-reported perceptions regarding the use of technology for scientific inquiry purposes ( r = .189, p<.001); teachers practices regarding the use of technology for scie ntific inquiry purposes ( r = .443, p<.05); the level of preparedness for using certain inquiry skills ( r = .275, p<.001); the frequency of using certain inquiry skills ( r = .450, p<.001); the level of prepar edness for using certain technology tools in instruction ( r = .671, p<.001); the total number of grades taught ( r = .112, p= .010); the level of grades taught ( r = .120, p= .006); the number of computers in class ( r = .136, p= .002); the number of computers in computer lab ( r = .180, p<.001); and the total number of educational levels in which they received educational technology training ( r = .262, p<.001). The number of years of teaching experience was positively correlated with the total number of grades taught ( r = .226, p<.001), but negatively correlated with the level of preparedness for using certain te chnology tools in instruction ( r = -.177, p <.001) and the total number of educational levels in whic h they received educational technology training ( r = -.193, p <.001). Finally, the level of grades taught was signi ficantly correlated with: The level of preparedness for using certain inquiry skills ( r = .144, p<.001); the level of preparedness for using certain technology tools in instruction (r = .121, p=.004); the frequency of using

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96 certain technology tool s in instruction ( r = .120, p =.006); the total number of educational levels in which they received educational technology training ( r = .321, p<.001). Multiple Regression Four regression m odels were tested usi ng the stepwise regression method in order to examine the degree of association between various outcome vari ables and exploratory variables. Results provided additional in sight into understanding the relationships between demographic variables, teachers comfort levels in using scientific inquiry and technologies, and their level of integra tion of these skills and technologies. Results from the first model showed that R2 of .119 was statistically significant, F (2,333) = 22.442, p<.001. This model indicated that two exploratory variables (frequency of using inquiry skills and level of prepar edness for using technology tools) were jointly associated with about 12% of the varian ce in teachers self-reported perceptions regarding the use of technology fo r inquiry purposes. No other variable was si gnificant at the p<.05 level. Although the influence of thes e two exploratory variables on the outcome variable was small (12%), this observation indicated that as scienc e teachers frequency of using inquiry skills in instruction and the level of their preparedness for using technology tools in instruction ge t higher, it is likely that their perceptions regarding the use of technology for scientific inquiry purposes get higher as well. The second model showed that R2 of .331 was statistically significant, F (3,330) = 54.437, p<.001. This model indicated that three e xploratory variables (frequency of using inquiry skills, level of preparedness for us ing technology tools, and frequency of using technology tools in instruction) were jointly a ssociated with about 33% of the variance in teachers practices regarding the use of t echnology for inquiry purposes in their own classrooms. No other variab le was significant at the p<.05 level. This result showed that

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97 as science teachers frequency of using inqui ry skills in instruction, the level of their preparedness for using technology tools in instruction, and the frequency of using technology tools in their classr ooms get higher, it is likely th at their practices regarding the use of technology for scientific i nquiry purposes get higher as well. The third model showed that R2 of .252 was statistically significant, F (1,551) = 185.63, p<.001. This model indicated that only one exploratory variable (level of preparedness for using inquiry skills in instruction) was associated with about 25% of the variance in teachers frequency of using scientific inquiry skills in their classrooms. No other variable was significant at the p <.05 level. This result showed that as science teachers level of preparedness for using i nquiry get higher, it is likely that their frequency of using certain scie ntific inquiry skills in in struction get hi gher as well. The fourth model showed that R2 of .499 was statistically significant, F (5,422) = 84.07, p<.001. This model indicated that 5 explorat ory variables (level of preparedness for using inquiry skills in instruction, years of experi ence, previous educational technology training, the number of computers in classroom, and the number of computers in computer lab) were associated with about 50% of the variance in teachers frequency of using technology tools in their classrooms. No other variable wa s significant at the p<.05 level. This result showed that as science teachers le vel of preparedness for using technology tools in classroom, their teaching experiences, the level of educational technology training they receiv ed at various stages duri ng their career (high school, undergraduate school, graduate school, and in -service training or continuing education), the number of computers in their classrooms, and the number of computers in computer

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98 labs they have access to get higher, it is lik ely that their frequency of using technology tools in instruction get higher as well. It can be concluded that th e results from the multiple regression analysis models indicate a certain degree of association between some of the demographic variables, teachers self-reported perceptions and practices regarding the use of scientific inquiry using technology, and their comfort levels with and uses of inquiry skills and technologies. The results obtained from the four models contribute to answering the second research question that addresses the relationship between teachers perceptions and practices as well as the s upporting questions of the study. Further Analyses Som e of the variables used in the study enabled the researcher to test for significant differences between group means. The re searcher conducted Ttests and ANOVAs to study the differences between the subsets of certain variables. Using the subsets of gender, presence of science la b in classroom, and presence of computer lab in school, significant differences were test ed using a T-test with respect to teachers self-reported perceptions and practices regarding the use of technology for scien tific inquiry purposes; teachers level of preparedness for using inquiry skills and technology tools in instruction; and teachers fre quency of using inquiry skills and technology tools in their classrooms. An alpha level of .05 was used fo r all statistical tests. For the ANOVA tests, a different set of variables (teachers year s of teaching experien ces in a categorized format, the number of different types of scie nce lessons taught, race /ethnicity, and state) were tested for significant group differences wi th respect to the six variables previously mentioned.

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99 Due to large volume of subsets of the da ta used in T-test and ANOVA statistics, only significant results were reported in Tables 4-14, 4-15, 4-16, and 4-17. Cohens d for significant t-test results and partial eta-squared ( 2) for significant ANOVA results were also computed to report effect sizes. The independent-samples t-test analysis in dicated that there was not any differences between males (n=207) and females (n=391) in terms of teachers self-reported perceptions ( t =-.363, df=558, p=.717, two-tailed) and practices ( t =-.083, df=560, p=.934, two-tailed) regarding the use of technology for scientific i nquiry purposes; teachers level of preparedness for using inquiry skills ( t =-.089, df=584, p=.373, two-tailed) and technology tools in instruction ( t =.146, df=577, p=.884, two-tailed); and teachers frequency of using inquiry skills (t =-.200, df=543, p=.845, two-tailed) and technology tools in their classrooms ( t =.161, df=518, p=.872, two-tailed). To test whether there were significant differences in terms of having (or not) a science lab in the classroom, four variab les were studied: Teachers self-reported perceptions and practices regarding the use of technology for scien tific inquiry purposes; teachers level of preparedne ss for using technology tools in instruction; and teachers frequency of using technology tools in their classrooms. Results showed that teachers self-re ported perceptions re garding the use of technology for scientific inqui ry purposes did not differ significantly based on whether there was a science lab in their classrooms ( t =-1.520, df=554, p=.129, two-tailed). This means that teachers who have a science lab in their classrooms did not differ significantly from those who do not on their perceptions re garding the use of t echnology for scientific inquiry purposes in instruction. They did, however, have significantly higher means with

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100 regard to teachers practices factor ( t =2.250, df=556, p=.025, two-tailed, Cohens d=.22); teachers level of prepar edness for using technology tools in instruction (t =2.003, df=541, p=.046, two-tailed, Cohens d=.19); and teachers frequency of using technology tools in their classrooms (t =2.744, df=517, p=.006, two-tailed, Cohens d=.27). Teachers who had a computer lab in their schools scored significantly higher than those who did not on teachers level of preparedness for using technology tools in instruction ( t =2.749, df=537, p=.006, two-tailed, Cohens d=.39) and teachers frequency of using technology tools in their classrooms ( t =2.103, df=513, p=.036, two-tailed, Cohens d=.47). Both groups of science teachers did not differ significantly on teachers perceptions ( t =.140, df=552, p=.888, two-tailed) and practices ( t =1.342, df=554, p=.180, two-tailed) factors. All of the Cohens d effect sizes reported were less than .50, which indicated small effect sizes for significant ttest results, based on criteria suggested by Cohen (1988, p.25). The effect of race/ethnicity was not stat istically significant for teachers selfreported perceptions regarding the use of technology for scientific inquiry purposes, F (5, 547)=1.468, p=.198; teachers level of preparedness for using technology tools in instruction, F (5, 533)=1.945, p=.085; teachers frequency of using inquiry skills, F (5, 565)=1.264, p=.278; and teachers frequency of using technology tools in their classrooms, F (5, 508)=1.745, p=.123. However, with an alpha level .05, the effect of race/ethnicity was statistically significant for teachers practices regarding the use of technology for scientific inquiry purposes, F (5, 548)=2.653, p=.022, 2=.024, and teachers level of prep aredness for using inquiry skills in instruction, F (5, 572)=3.016, p=.011, 2=.026.

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101 A post hoc Tukey test was conducted to de termine which race/ethnicity categories indicated significant differences. Results indicated that although the overall F value for teachers practices factor was significant at the .05 level, none of the race/ethnicity categories showed statistical difference among each other. For teachers level of preparedness for using inquiry skills, howev er, results demonstrated that Hispanic (Mean=32.31, SD=3.93) teachers had significan tly higher scores than their Asian counterparts (Mean=26.95, SD=6.96), p=.009, SE=1.57. The effect of the number of different types of science lessons taught was not statistically significant for teachers self-reported perceptions, F (5, 582)=1.600, p=.158, and practices, F (5, 589)=2.145, p=.059, regarding the use of technology for scientific inquiry purposes; teachers level of prepare dness for using inquiry skills in instruction, F (5, 610)=1.125, p=.346; teachers frequency of using inquiry skills, F (5, 600)=1.244, p=.287; teachers level of preparedness for using technology tools, F (5, 556)=1.745, p=.123; and teachers frequency of using technology tools in their classrooms, F (5, 524)=.818, p=.537. In order to investigate if teachers from di fferent states had significant differences with each other in terms of variables discussed above, the researcher selected only six states (Connecticut, Florida, Kansas, Michig an, Virginia, Wisconsin) from which more than 29 science teachers participated in th e study. The effect of state where science teachers were teaching at the time of th e study was not statistically significant for teachers perceptions, F (5, 438)=1.628, p=.151, and practices, F (5, 433)=1.203, p=.307, regarding the use of technol ogy for scientific inquiry pur poses; teachers level of preparedness for using inqui ry skills in instruction, F (5, 449)=2.047, p=.071; teachers

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102 level of preparedness for using technology tools, F (5, 415)=1.065, p=.379; and teachers frequency of using technology tools in their classrooms, F (5, 396)=1.550, p=.173. Only teachers frequency of using inquiry skills was significant at the .05 level, F (5, 445)=3.168, p=.008, 2=.034. A post hoc Tukey test was conducted to determine if teachers teaching in various states had signifi cant differences among them in terms of six variables listed above. Results indicated significant di fference between only those teachers who taught in Florida and Kansas. Science teachers from Florida (Mean=33.66, SD=5.32) had significantly higher scores th an those who teach in Kansas (Mean=30.29, SD=5.04), p=.004, SE=.93. The effect of years of science teaching experience was not statistically significant with teachers perceptions, F (4, 543)=.693, p=.597, and practices, F (4, 545)=.615, p=.652, regarding the use of technology for scien tific inquiry purposes; teachers level of preparedness for using inqui ry skills in instruction, F (4, 566)=1.777, p=.132; teachers frequency of using inquiry skills, F (4, 559)=2.129, p=.076; and teachers frequency of using technology tools in their classrooms, F (4, 504)=.251, p=.909. With an alpha level .05, the effect of years of science teaching experience was statisti cally significant for teachers level of preparedne ss for using technology tools, F (4, 528)=4.167, p=.002, 2=.031. A post hoc Tukey test was used to determine which experience categories indicated statistically significant mean scores According to the resu lts, science teachers who have 1 to 9 years of experience (M ean=74.88, SD=14.89) had significantly higher mean scores than those who taught science for 20 to 29 years, (Mean=69.47, SD=14.97), p=.034, SE=1.88, as well as those who taught science for 30 to 39 years, (Mean=67.67, SD=15.33), p=.034, SE=2.26. These results demonstrated that those science teachers who

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103 are new in the field and less than 10 years of experience with respec t to those who have 20 to 39 years of experience reported higher level of preparedness for using technology tools in instruction. All of the partial eta-squared values to report effect sizes were less than .06, which indicated small effect sizes for significant ANOVAs, based on criteria suggested by Cohen (1988, p.285). A Summary of Results in terms of Study Research Questions The researcher m ade use of a variety of methods to answer the overarching and the supporting questions. First of all, reliability and item analysis and exploratory and confirmatory factor analyses were used to determine the reliability and validity of the SIT-TIPPS instrument. This process was essentia l in answering the re search questions as all of the overarching and supporting questions depended on the quality of the scale. The data obtained through teachers responses to the components of the SIT-TIPPS instrument were analyzed usi ng a variety of statistical met hods to answer the research questions of the study. The SIT-TIPPS indicate d high reliability (s ee Table 3-1 & 3-2) and good content and construct va lidities based on expert ju dgment and factor analysis results. The additional methods used to answer the studys questions were descriptive statistics (e.g., frequencies), correlations, multiple regressions, t-tests, and ANOVAs. Interpretations from all of these methods c ontributed to the expl anation of the first overarching question, which dealt with how science teachers are using technology to implement the goals of scientific inquiry in their classrooms. It can be concluded that science teachers who scored high in th e SIT-TIPPS instrument used technology tools/applications to engage students in scientifically orie nted questions; to encourage students to give priority to evidence in re sponding to questions; to enable students to

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104 formulate explanations from evidence; to en able students to conn ect explanations to scientific knowledge; and to encourage students to communicate and justify their explanations. The data provided by the SI T-TIPPS instrument provides an in-depth snapshot to describe how the five essential features of scientific i nquiry as presented in the National Science Education Standards (1996) are being used by middle and high school science teachers. For the second overarching question, the correlations between the 25 items constituting the teachers per ceptions factor and the 25 items constituting the teachers practices factor were calculated. Multiple re gression method was used as well to answer this question because models indicated that these two factors were dependent on each other. Correlation analysis indicated a positive significant correlation between teachers perceptions and practices regarding the use of technology for scien tific inquiry purposes. The first supporting question investigated the effect of teacher demographics and teacher background/teacher professional develo pment variables on teachers self-reported perceptions and practices for using technol ogy to enact scientific inquiry. For this purpose, descriptive statistics, multiple regressions, correlations, t-tests, and ANOVAs were used. In general, the results in this category indicated that as science teachers get more years of teaching experience, get expos ed to educational technology training at different stages during their career, and get access to more co mputers in their classrooms and computer labs, they are more likely to use technology tools in their classrooms. Teachers gender, race/ethnicity, state in wh ich science teachers are teaching, and the number of different types of science course s a science teacher taught did not have any

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105 significant effect on their perceptions and pr actices regarding the use of technology for scientific inquiry purposes. The last four supporting questions enable d the researcher to explore the second overarching research question. Nine items from the SIT-TIPPS were used to measure how well prepared teachers feel to support stud ents to engage in certain inquiry skills and how often they use these inquiry skills in their classrooms. Twenty-six items were employed to answer how well prepared t eachers feel to use certain technology tools/applications and how often they use thes e tools/applications in their classrooms. In order to answer these four supporting questi ons, the researcher used frequency reports, correlations, and multiple regression models. In general, results in this category showed science teachers felt very well prepared in s upporting students to co llect, organize, and analyze data; explain cause-effect relationshi ps; and conduct experiments. However, they felt less comfortable in supporting students to identify their own misconceptions of science content; find biases and flaws in their scientific explanations; test scientific explanations against current sc ientific knowledge; and critique experiments. In terms of how frequently these skills are integrated into science classrooms, teachers seemed to more frequently support students to explai n cause-effect relationships; to discuss scientific explanations/ideas/models with others, conduct experiments; and to collect, organize, and analyze data. However, they less frequently support students to find biases and flaws in scientific explanations; to te st scientific explanat ions against current scientific knowledge; and to critique experiments. In addition, teachers frequency of us ing inquiry skills and their level of preparedness for using technology tools are jo intly associated with about 12% of the

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106 variance in teachers self-repor ted perceptions regarding the use of technology for inquiry purposes. About 33% of the variance in teachers practices regarding the use of technology for scientific inquiry purposes wa s explained by how frequently teachers used inquiry skills and technology t ools in instruction and how well prepared they feel to use technology tools. The extent to which scienc e teachers felt better prepared to use scientific inquiry skills was associated with about 25% of the variance in how frequently they use these skills in instruction. Moreover, results also indicated that as science teachers feel more prepared to use technology tools in thei r lessons, get more years of teaching experience, get exposed to educationa l training at different stages during their career, get access to more computers in thei r classrooms and computer labs, they will more likely to use technology tools in their science courses.

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107 Table 4-1. Participant characte ristics based on gender (n=598) Gender n % Male 207 34.6 Female 391 65.4 Table 4-2. Participant ch aracteristics based on race/ethnicity (n=590) Race/Ethnicity n % American Indian 4 .7 Asian 20 3.4 Black 34 5.8 Hispanic 26 4.4 White 479 81.2 Multiracial 27 4.6

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108 Table 4-3. Distribution of partic ipants based on states (n=601) States n % AL 16 2.7 AZ 3 .5 CA 15 2.5 CT 30 5.0 FL 156 26.0 GA 6 1.0 HI 9 1.5 IL 6 1.0 IN 1 .2 IA 13 2.2 KS 50 8.3 KY 1 .2 LA 1 .2 MD 2 .3 MA 2 .3 MI 36 6.0 MN 1 .2 MO 6 1.0 MT 1 .2 NE 1 .2 NH 6 1.0 NJ 3 .5 NM 7 1.2 NY 5 .8 NC 6 1.0 OH 5 .8 OK 1 .2 PA 1 .2 SC 6 1.0 TN 1 .2 TX 5 .8 VA 165 27.5 WA 2 .3 WV 1 .2 WI 29 4.8 WY 1 .2 DC 1 .2

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109 Table 4-4. Number of y ears of teaching experien ce: Categorized (n=583) Categories n % 1-9 244 41.9 10-19 187 32.1 20-29 99 17.0 30-39 49 8.4 40-44 4 .7 Table 4-5. Grade levels taught by science teachers (n=715) Characteristics n% Grade levels taught 6 17123.9 7 23833.3 8 25235.2 9 34548.3 10 36050.3 11 37852.9 12 37151.9 Over 12 13018.2 Total number of grades taught 1 719.9 2 8211.5 3 12417.3 4 14920.8 5 7911.0 6 395.5 7 354.9 8 212.9 Table 4-6. Courses taught by science teachers (n=715) Characteristics n% Course name Life Sciences 36450.9 Earth Sciences 28339.6 Physical/General Sciences 36951.6 Physics 14219.9 Chemistry 19427.1 Total number of courses taught 1 16723.4 2 19527.3 3 16122.5 4 588.1 5 162.2

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110 Table 4-7. Science teachers frequency of use of scientific inquiry skills Never Rarely Sometimes Often Almost or All Lessons To support students to: 12345n M S.D. 1. Ask researchable questions 1.09.633.241.414.8623 3.59 .89 2. Conduct experiments .54.431.552.311.5620 3.70 .75 3. Collect, organize, analyze data .53.725.657.612.6620 3.78 .73 4. Identify their own misconceptions of science content 1.110.132.837.418.6625 3.62 .94 5. Explain cause-effect relationships .32.921.247.428.2624 4.00 .80 6. Test scientific explanations against current scientific knowledge 3.214.938.434.78.8623 3.31. .94 7. Find biases or flaws in their scientific explanations 2.615.235.733.912.6625 3.39 .97 8. Discuss scientific explanations/ideas/models with others 1.36.623.240.728.2624 3.88 .94 9. Critique experiments 3.421.234.633.87.1622 3.20 .96

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111 Table 4-8. Science teachers fre quency of use of technology tools Never Rarely Sometimes Often Almost or all lessons 1 2 3 4 5 n M S.D. 1. Presentation devices (such as video projectors, LCD panels) 2.5 5.4 15.1 31.4 45.6 609 4.12 1.1 2. Smart Board/Promethean interactive boards 53.0 13.4 7.0 11.1 15.4 610 2.23 1.5 3. Wireless communication devices (e.g., PDAs, student digital response systems) 62.3 18.1 10.7 5.8 3.1 608 1.69 1.1 4. Graphing/scientific calculators 30.6 16.8 20.1 19.1 13.3 607 2.68 1.4 5. Portable Global Positioning Systems (GPS) 69.9 19.8 7.6 1.8 1.0 607 1.44 .79 6. Digital data collection devices (e.g., pH, pressure and temperature probes, digital microscopes, Navigator systems) 22.8 26.1 30.2 15.8 5.1 609 2.54 1.2 7. Videoconferencing, teleconferencing 81.3 11.8 3.9 2.0 1.0 611 1.29 .72 8. Word processing (e.g., Word) 1.6 4.9 16.6 29.7 47.1 609 4.16 .98 9. Spreadsheets (e.g., Excel) 7.9 14.5 28.9 30.7 17.9 605 3.36 1.2 10. Presentation software (e.g., Power Point) 3.5 11.4 18.6 28.5 38.1 607 3.86 1.1 11. Database software 31.0 23.7 23.5 13.7 8.1 604 2.44 1.3 12. Educational games 20.5 27.1 32.8 14.0 5.6 609 2.57 1.1 13. Virtual experiences (e.g., Google Earth and Starry Night, a virtual planetarium) 33.2 32.7 21.9 9.4 2.8 608 2.16 1.1 14. Graphing and data analysis software 26.0 22.7 30.3 15.1 5.8 603 2.52 1.2 15. Video editing 64.1 22.6 9.6 2.6 1.0 605 1.54 .85 16. Image/picture editing 45.9 23.8 18.6 7.5 4.2 601 2.00 1.2 17. E-mail 15.2 10.4 16.4 23.4 34.7 599 3.52 1.4 18. Webpage design 51.5 21.9 12.1 8.6 5.8 602 1.95 1.2 19. Accessing online databases 23.7 28.0 26.8 14.9 6.6 604 2.53 1.2 20. Internet searches 2.7 10.6 29.4 32.0 25.4 603 3.67 1.1 21. Online simulations 14.0 22.3 36.1 18.8 8.8 601 2.86 1.1 22. Online science games 28.2 27.0 29.7 10.1 5.0 603 2.37 1.1 23. Wikis 68.1 16.6 9.0 4.3 2.0 598 1.56 .97 24. Blogs 74.1 14.0 6.5 4.0 1.5 602 1.45 .89 25. Podcasts, videocasts 62.8 19.8 11.5 3.3 2.5 600 1.63 .98 26. Data collection telecollaborative activities (e.g., Journey North, SCOPE, Amazing Space) 691. 18.4 7.4 3.8 1.3 598 1.50 .89

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112 Table 4-9. Science teachers level of prep aredness of scientific inquiry skills Not adequately prepared Somewhat prepared Fairly well prepared Very well prepared To support students to: 1 2 3 4 n M S.D. 1. Ask researchable questions 2.4 11.5 44.0 42.1 627 3.26 .75 2. Conduct experiments 1.4 9.1 36.0 53.4 625 3.41 .72 3. Collect, organize, analyze data 1.3 5.4 34.6 58.7 625 3.51 .66 4. Identify their own misconceptions of science content 2.6 17.9 46.3 33.2 626 3.10 .78 5. Explain cause-effect relationships 1.3 7.5 39.1 52.1 626 3.42 .69 6. Test scientific explanations against current scientific knowledge 2.2 18.7 43.9 35.1 626 3.12 .78 7. Find biases or flaws in their scientific explanations 4.0 18.4 41.6 36.0 625 3.10 .83 8. Discuss scientific explanations/ideas/models with others 2.4 9.1 40.7 47.8 627 3.34 .74 9. Critique experiments 4.3 15.7 38.2 41.8 624 3.17 .85

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113 Table 4-10. Science teachers level of preparedness of using technology tools Not adequately prepared Somewhat prepared Fairly well prepared Very well prepared 1 2 3 4 n M S.D. 1. Presentation devices (such as video projectors, LCD panels) 3.1 6.7 22.7 67.5 616 3.55 .75 2. Smart Board/Promethean interactive boards 33.3 22.9 20.3 23.4 615 2.34 1.2 3. Wireless communication devices (such as PDAs, student digital response systems) 37.9 25.4 19.7 16.9 614 2.16 1.1 4. Graphing/scientific calculators 18.7 25.3 26.3 29.7 616 2.67 1.1 5. Portable Global Positioning Systems (GPS) 42.9 26.8 17.5 12.9 613 2.00 1.1 6. Digital data collection devices (such as pH, pressure and temperature probes, digital microscopes, Navigator systems) 12.2 22.2 29.3 36.3 617 2.90 1.0 7. Videoconferencing, teleconferencing 48.5 26.0 17.1 8.5 615 1.86 .99 8. Word processing (e.g., Word) .7 1.6 10.1 87.6 614 3.85 .45 9. Spreadsheets (e.g., Excel) 2.1 7.5 22.3 68.1 614 3.56 .72 10. Presentation software (e.g., Power Point) 1.6 4.9 16.2 77.3 617 3.69 .64 11. Database software 11.4 18.8 28.6 41.2 616 3.00 1.0 12. Educational games 7.9 19.1 31.8 41.2 611 3.06 .96 13. Virtual experiences (such as Google Earth and Starry Night, a virtual planetarium) 14.1 27.8 28.3 29.8 615 2.74 1.0 14. Graphing and data analysis software 12.7 27.6 28.1 31.6 613 2.79 1.0 15. Video editing 42.1 27.2 18.9 11.7 613 2.00 1.0 16. Image/picture editing 20.6 28.1 25.8 25.5 612 2.56 1.1 17. E-mail 1.1 2.4 12.1 84.4 614 3.80 .53 18. Webpage design 27.2 29.2 23.5 20.2 614 2.37 1.1 19. Accessing online databases 9.8 21.2 30.1 38.9 614 2.98 1.0 20. Internet searches 1.0 3.6 19.9 75.6 614 3.70 .59 21. Online simulations 6.4 15.5 29.8 48.4 614 3.20 .92 22. Online science games 10.0 17.6 30.3 42.1 608 3.04 1.0 23. Wikis 40.3 23.0 19.6 17.1 608 2.13 1.1 24. Blogs 38.0 26.8 19.3 15.9 611 2.13 1.1 25. Podcasts, videocasts 37.3 28.3 21.2 13.2 612 2.10 1.1 26. Data collection telecollaborative activities (e.g., Journey North, SCOPE, Amazing Space) 48.9 26.7 15.5 9.0 614 1.85 .99

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114 Table 4-11. Pearson Product-Moment Correlation between variables Variables 1 2 3 4 5 6 7 8 9 10 11 12 Perceptions 1 .649** .225** .267** .233** .189** -.019 .087** .077 -.006 .084 .136** Practices 1 .283** .456** .384* .443* .359 .024 .011 .109* .136** .157** Inquiry skills-level 1 .522** .448** .275** .078 .151** .144** .007 .062 .042 Inquiry skillsfrequency 1 .423** .450** .064 .065 .050 .070 .088* .069 Technology toolslevel 1 .671** .177** .118** .121** .061 .117** .249** Technology toolsfrequency 1 .014 .112** .120** .136**.180** .262** Years of experience 1 .226** .202** .110**.035 -.193** Number of grades taught 1 .982** -.041 .107* .343** Grade level 1 -.022 .087* .321** Number of computers in class 1 .045 -.013 Number of computers in computer lab 1 -.120** Previous ed tech training 1 Note. *p<.05, **p<.001 (2-tailed)

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115 Table 4-12. The number of computers in classrooms Number of Computers n %Cumulative % 1 213 36.1 36.1 2 86 14.6 50.7 3 38 6.4 57.1 4 29 4.9 62 5 32 5.4 67.5 6 14 2.4 69.8 7 27 4.6 74.4 8 20 3.4 77.8 9 15 2.5 80.3 Table 4-13. Percent of teachers reporting taking educational technology classes n%Total Level of educational technology training High school 15228.3538 Undergraduate school 26147.3552 Graduate school 28854.4529 In-service training or continuing education courses 56892.3568 Frequency of educationa l technology training 1 18726.2 2 20428.5 3 12217.1 4 669.2 Table 4-14. T-test results for subscales based on presence of science lab in classroom Subscale vs. lab presence NMeanSD t df P Cohens d Practices Lab present 40999.8316.792.25*556.025 .22 Lab not-present 14996.1318.17 Technology tools-level Lab present 39572.9014.762.00*541.046 .19 Lab not-present 14870.0015.86 Technology tools-frequency Lab present 37764.6516.392.74*517.006 .27 Lab not-present 14260.2515.95 p< .05, two-tailed

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116 Table 4-15. T-test results for subscales base d on presence of computer lab in school Subscale vs. lab presence NMeanSD t df P Cohens d Technology tools-level Lab present 50272.7314.912.75*537.006 .47 Lab not-present 3765.7315.57 Technology tools-frequency Lab present 47963.7916.472.10*513.036 .39 Lab not-present 3657.8613.90 p< .05, two-tailed Table 4-16. ANOVA results between subscales and selected variables Source df (between) df (within) F 2 P Ethnicity x Inquiry skills-level 55723.016 .026 .011* Experience-categorized x Technology tools-level 45284.167 .031 .002* State x Inquiry skills-frequency 54453.168 .034 .008* p< .05 Table 4-17. Summary of Post Hoc (Tukey) ANOVA results for significant differences Source NMeanSD SE P Ethnicity x Inquiry skills-level Asian 1926.956.96 1.60 Hispanic 2632.313.93 .77 Asian x Hispanic 1.57 .009* Experience-categorized x Technology toolslevel 1-9 22474.8814.89 .99 20-29 8569.4714.97 1.62 30-39 4667.6715.33 2.26 1-9 x 20-29 1.88 .034* 1-9 x 30-39 2.39 .023* State x Inquiry skills-frequency FL 15133.665.32 .43 KS 4830.295.04 .72 FL x KS .93 .004* p< .05

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117 CHAPTER 5 FINDINGS, CONCLUSIONS, IMPLICATIONS AND SUGGESTIONS FOR FUTURE RESEARCH There have been m any efforts throughout the history of science education to improve teaching and learning in elementary and s econdary schools (Abrams, 1998). The National Research Council and American Association for the Advancement of Scie nce have contributed to efforts by publishing reports such as the National Science Education Standards (1996), Science for All Americans (1990), and Benchmarks for Science Literacy (1993) that emphasized student learning, the nature of sc ience, science literacy, and sc ientific inquiry. Despite these efforts, the current science curriculum in the Un ited States and many other countries has failed to prepare students for the kinds of experiences they will need to become successful science learners (Linn et al., 2004). Scientific inquiry has been an overarching goal of science education (AAAS, 1993; NRC, 1996; Flick, 1997; Crawford, 1997; Edelson, et al., 1999) and a central strategy for teaching science (NRC, 1996) for decades. Although there are certain instructional me thods and strategies that help teachers implement scientific inquiry in their classrooms, the use of technology can also play a significant role in meeting the goals of scientific inquiry. According to the National Research Council (1996), a goal for using educationa l technology in the classroom is to identify effective ways to use technology tools for higherorder thinking that mesh with the assumptions of scientific inquiry. When it comes to technology integration, teachers play a key role (Scheffler & Logan, 1999). Yet, research indicates teachers lack good instructional frameworks for effective implementation of technology into the curriculum (Bitner & Bitner, 2002). In addition to that, there is little literature available on answer ing the question of how science teachers use technology to succeed in enacting the goals of scie ntific inquiry. There is clearly a need to

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118 understand how science teachers use technology as they strive to at tain the goals of scientific inquiry in their classrooms and to provide te achers with a framework for using technology in scientific inquiry-b ased instruction. This study, which is based on the premise technology could enhance the quality of scientific inquiry-based instruction, attempted to answer the question of how science teachers use technology to attain the goals of scientific inquiry-ba sed instruction through the development of an instrument about scientific inquiry and the use of technol ogy for that purpose. It was an attempt to address an essential topic both in science education and educational technology. Through the creation of an instrument, the SIT-TIPPS, the study addressed the following research questions: Overarching Questions 1. How are teachers using technology to im plement the goals of scientif ic inquiry in their classrooms? 2. What are the relationships between teacher s self-reported perceptions and practices regarding the use of technology to atta in the goals of sc ientific inquiry? Supporting Questions What are the relationsh ips between teachers se lf-reported perceptions and practices regarding the use of technology to atta in the goals of scientific inquiry in terms of: Teacher demographics and teacher backgr ound/professional development variables? How often do teachers support students to engage in certain inquiry skills in their science classrooms? How often do teachers use certain technol ogy tools in their science classrooms? How prepared do teachers feel to support students to engage in certain inquiry skills in their science classrooms? How prepared do teachers feel to use certain technology tools in their science classrooms?

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119 The SIT-TIPPS Instrument One of the m ain purposes of this study was to validate and develop an instrument called the Scientific Inquiry with Technology-Teachers Perceptions and Practic es Scale (SIT-TIPPS). The theoretical framework and ex tensive literature base of the items as well as experts agreement on the contents of the items contributed to the content validity of the instrument. In addition, a successful two-factor solution using e xploratory factor analys is provided support for the construct validity of the SI T-TIPPS. Therefore, the SIT-TIPPS instrument could be used to identify middle and high school science teache rs self-reported per ceptions and practices regarding the use of technology to enact scientific inquiry in their classrooms and to explore to what extent science teachers feel comfortable to use certain scientific inquiry skills and technology tools in their lessons. This is important in assessing the needs of science teachers in terms of professional development and designing effective preservice and inservice training programs. One of the initial expectations of the resear cher when generating the item pool for the SITTIPPS was to observe a five-factor solution after factor analysis This was because the first attempt by the researcher to generate the SIT-TIP PS items based on the five essential features of scientific inquiry as described in the National Science Educa tion Standards (NRC, 1996). The factors are: Teachers engage students in scientifi cally oriented questions; enable students to give priority to evidence in responding to questions; encourage students to formulate explanations from evidence; enable learners to connect expl anations to scientific knowledge; and encourage learners to communicate and just ify explanations. The factor analysis procedure did not produce a five-factor solution as expect ed. Instead, it produced a two-f actor solution in the form of perceptions and practices. Although a ll of the items in the scale, measuring either perceptions or practices of science teachers rega rding the use of technology to at tain the goals of scientific

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120 inquiry, were generated based on thes e five features of scientific inquiry and their contents were verified by five content experts, the inability of f actor analysis to yield fi ve factors might indicate that science teachers opinions and perspectives regarding these five components are not distinctive. Although the Nati onal Science Education Standa rds (NRC, 1996) reports five psychological factors in the at tainments of teaching scientif ic inquiry, this study provided evidence that these five factors are not statis tical factors. Creating the SIT-TIPPS with two factors, perception and practice, created a be tter model for understand ing science teachers perceptions and practices regarding the use technology to attain the goals of scientific inquiry in instruction. Summary of Findings The results of the study dem onstrated that the SIT-TIPPS is a useful tool in analyzing selfreported current practice of middle and high sc hool science teachers regarding the use of technology for scientific inquiry purposes and in furthering the discussion on how science teachers can use technology to attain the goals of scientific inquiry. It measured middle and high school science teachers self-reported perceptions and practices regardi ng the use of technology in attaining the goals of scientific inquiry. In do ing so, it connected theory and research from two fields, science education and educational tec hnology, which can result in a change of daily practice in science classroom. Even though there are some instruments targeting scientific inquiry in science education literature (Bodzin & Beerer, 2003; Brandon & Taum, 2005), an instrument specifically targeti ng scientific inquiry in scien ce classrooms where technology is used is an area of need in both fields. The inst rument developed in this study and the research questions it attempted to answer is a contribution to this body of literature and the fields of science education and educational technology.

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121 This study had two overarching research que stions and five supporting questions. The main questions focused on how teachers use te chnology to implement the goals of scientific inquiry and the relationships betw een their self-reported perceptions and practices regarding this implementation. The supporting questions articula ted these relationships by addressing some teacher demographics and background/professional development variables as well as the level and frequency of their preparedness and use of certain inquiry tasks a nd technology tools. While the total scores obtained from the SIT-TIPPS and the relationships between perception and practice items were utilized to help answer the overarching ques tions of the study, relationships obtained from supporting questions also contributed to answeri ng these overarching questions. Conclusions were drawn from the results of the study in relation to these questions. Characteristics of Science Teachers A national sample of middle and high school science teachers was targ eted to participate in the study. About 65% of the science teacher s were females and the majority (81.2%) were white. This profile of science teachers parallels that of another large-sc ale national study called the National Survey of Science and Mathematic s Education (Weiss et al., 2001; Smith et al., 2002) in which about 2500 middle and high school science teachers were surveyed from 1977 to 2000. In this survey, about 64% of the science t eachers (grades 5-12) were females and about 81 percent were white. This similarity in profiles in terms of gender and race/ethnicity might indicate a continuing trend among middle and hi gh school science teachers in the U.S. since 1977. In that respect, this study might be a good contribution to the data from 1977 to 2000 by demonstrating a similar trend in 2008. Demographic findings also indi cate that the more experienced science teachers get the more grades (6 through 12 and above) they tend to teach. However, teachers with less teaching experience (or younger for that matter) seem to receive more educational technology training at

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122 more levels of various educational settings and in-service/continuing educa tion and seem to feel more prepared to use technology tools in instruction. Data also showed that the level of grades taught significantly correlates with the total number of educational levels in which teacher s received educational technology training. Hence, it appears that teachers who teach more grade levels recei ve more educational technology training. The level of grade taught positively correl ates with the level of preparedness for using inquiry skills, the level of using technology tool s, and frequency of using technology tools in instruction. This means that te achers who teach higher-level grad es indicate a higher level of preparedness and frequency for using inquiry ski lls and technology tools in their lessons. This could be reflective of the potential differences in preservice and inservice experiences at the middle school and high school level. This finding is in line with the results of the 2000 National Survey of Mathematics and Science Educati on study (Weiss, et al., 2001), which indicated higher percentages of high school science teachers feeling very well qualified to teach science processing skills. A Snapshot of Science Teachers Scient ific In quiry and Technology Skills Study results gave a clear picture of scien ce teachers strengths and areas in which improvements are needed in the areas of scientif ic inquiry and technology integration. Table 5-1 and Table 5-2 list teachers weaknesses and stre ngths in terms of their self-reported comfort levels and actual practices in us ing inquiry skills and technology t ools/applications in instruction. Middle and high school science teach ers that participated in th is study do feel prepared to support students in traditional aspects of scie ntific inquiry. However, in areas which might challenge content knowledge, such as identifying misconceptions of science content, there is great discomfort. When looking at the tasks listed in the weakness column of Table 5-1, a

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123 concern of science content is clear. This results in students not engaging in scientific inquiry tasks that challenge and expa nd their content knowledge. Regarding middle and high school science te achers self-reported comfort levels and integration of technology tools/ applications in instruction, it is obvious to see that science teachers feel very comfortable using common fo rms of technologies such as word processing, Internet searches, and spreadsheets. However, they exhibit low comfor t level and integration when it comes to uncommon and new form s of technologies such as data collection telecollaborative activities, por table Global Positioning Systems, and Internet 2.0 applications. The technologies listed in the weakness column of Table 5-2 designate essential tools and applications that lend themselves to higher-order thinking in classrooms. Hence, the inadequacy of science teachers abilities to utilize tools such as these point out an area of concern because it results in students not benefiting from the transf ormative and challenging power of these tools to investigate scientific pr inciples and concepts. Source of Computer Knowledge Science teachers responses to whether th ey received educatio nal technology training during high school, undergraduate sc hool, graduate school, or in -service/continuing education yielded interesting results. Th e majority of the science te achers (92.3%) reported receiving educational technology classes dur ing in-service training and continuing education. This indicates the importance of in-ser vice training provided to scien ce teachers regarding the use of instructional technologies in classrooms. More educational technology training was received by science teachers in graduate school than during undergraduate education. Because of teachers multiple responses to select a level where th ey received educational technology training, it was interesting to see the percentage of teachers w ho reported having training at certain number of levels. Although nearly 92% reported getting ed ucational technology traini ng at a various level

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124 during their careers, th ese findings indicate a low infusion of educational technology training throughout the career path of a science teacher. Additional Dem ographic Findings Although previous research indicated more ma le teacher use of computers in classrooms (Becker, 1994; Chiero, 1997), this study found no di fferences between male and female science teachers. According to the results of this study, there is no difference between male and female science teachers in terms of their perceptions and practices rega rding the use of technology for scientific inquiry purposes ; how prepared they feel to use inquiry skills and technology tools in instruction; and how often they used inquiry skills and t echnology tools during lessons. Having a computer lab at sc hool also plays an important role for science teachers. Teachers who have a computer lab in their scho ols scored significantly higher than those who did not on how well prepared they feel to use te chnology tools and how frequently they use these tools in instruction. Both groups on the other hand, do not differ in terms of th eir perceptions and practices regarding the use of technology for scientific inquiry purposes. Teachers race/ethnicity does not produce sign ificant results for teachers self-reported perceptions and practices in using technology for inquiry purposes their level of preparedness for using technology tools, and how often they use inquiry skills a nd technology tools in instruction. It is, however, significant for how th ey practice the use of technology for scientific inquiry skills in classroom as well as for how we ll prepared they feel to use inquiry skills in instruction. Though significant, post hoc ANOVA test did not produce any group differences between race/ethnicity categories. The only sign ificant mean difference was between Hispanic and Asian teachers in terms of how well prepared they feel to use inquiry skills. Results indicate Hispanic teachers feel more prepared to use sc ientific inquiry skills in instruction than their Asian counterparts.

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125 The results were not very different for the state in which science teachers are teaching based on the variables listed above. Only teachers from Florida and Kans as (among Connecticut, Florida, Kansas, Michigan, Virginia, Wisconsin) differ significantly in te rms of how frequently they use scientific inquiry skills in instruction. Florida teachers us e scientific inquiry skills more in their lessons than Kansas teachers. An overv iew of the science education standards of both states reveals Florida science education standards seem to provide more detailed benchmarks to assist teachers in instruction. However, a more thorough analysis of the st andards in two states are needed to make comparisons and to identify th e impact of content stan dards in both states on teachers implementation of inquiry skills in their classrooms. Science teachers years of teaching experien ces are significant for how confident they feel to use technology tools in th eir classrooms. According to th e results, science teachers who have 1 to 9 years of teaching experience report s higher level of confid ence in using technology tools in their lessons than those who have 20 to 29 years of experience and 30 to 39 years of experience. This demonstrates science teachers who are relatively new and have less than 10 years of experience in the field feel more prepared to use tec hnology tools in their lessons than their more experienced counter parts (between 20-39 years of experience). Interestingly, the group of science teachers who have less than 10 years of experience does not demonstrate a significant difference with those who have 10 to 19 years of teaching experience. Study Implications The SIT-TIPPS instrument developed in this st udy could be used to identify m iddle and high school science teachers self-reported perceptions and practices regarding the use of technology to enact scientific inquiry in their classrooms and to explore to what extent science teachers feel comfortable to use certain scientif ic inquiry skills and t echnology tools in their

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126 lessons. It also enables one to look at the existe nce and the degree of relationship among this set of variables. The findings of the study have implications that could help researcher s, educators, science teachers and administrators identify how tec hnology is being used by middle and high school science teachers as well as how well prepared th ey feel to use scientif ic inquiry skills and technology tools during instruction. It also provides information about science teachers selfreported perceptions and practices of using technol ogy to attain the goals of scientific inquiry as set forth by the National Science Education Stan dards (NRC, 1996) and discussed extensively by researchers in the field. Moreover, the SIT-TIPPS instrument could effect ively be used as an evaluation tool for curriculum. For instance, the integration of an exemplary science curriculum such as the Foundational Approaches in Science Teaching (FAS T) that potentially lends itself to students using the scientific inquiry process could be evaluated using the components of SIT-TIPPS instrument. The experiments and activities empl oyed by a FAST teacher could be evaluated in terms of the use of inquiry skills and technology tools/applications addressed in the SIT-TIPPS. For example, using the SIT-TIPPS one can easily determine the degree to which a FAST teacher enables students to critique e xperiments found in FAST or to te st their explanations from the FAST activities against current scientific knowledge. The SIT-TIPPS could enable researchers to analyze the state of inte gration of scientific inquiry and technology not only at the classroom level, but also at the school and district level. The way the SIT-TIPPS instrument was developed dovetails with the essential features of scientific inquiry that ha s been an overarching goal in the science education field (NRC, 1996; Flick, 1997; Crawford, 1997; Edelso n et al., 1999) along with the manner in which technology plays a signif icant role in meeting the goals of scientific

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127 inquiry. This approach can he lp researchers from both scie nce education and educational technology fields analyze how scien ce teachers perceive such a complex form of instruction and how often they try to implement such practices in their lessons. The findings from such studies could shed light on school level a nd district level analysis of th e state of scientific inquiry implementation from teachers perspective and then could lead to district level policy and professional development for science teachers. Fo r example, the finding that illustrates science teachers inadequacy to use interactive whiteboa rds, which many districts purchase these days, highlights the importance of knowi ng this fact in order to prov ide professional development to enable teachers to support students learning th rough presentations, demonstrations, and more. In this respect, another important aspect of the curricular evaluative ability of the SITTIPPS instrument is its emphasis on teachers role. When it come s to technology teachers play a key role (Scheffer & Logan, 1999). Yet, research indicates teachers lack solid instructional frameworks for effective implementation of tech nology into the curriculum (Bitner & Bitner, 2002). The SIT-TIPPS with its emphasis on teache rs perceptions and practices, and the way it meshes the essential features of scientific inquiry sk ills and use of technology tools can be used by researchers, teacher educators, and administrators to analyze teacher behaviors and practices with respect to technology use to achieve scientific inquiry. The SIT-TIPPS can also be a useful tool in teaching preservice science teachers and helping them understand the complex nature of scientific inquiry and how technology can be used to facilitate its successf ul implementation. According to some studies, preservice science teachers graduated without conducting a single inqui ry in their programs (Windschitl, 2003) and lacked understanding of inquiry, skills, and experi ences (Newman et al., 2004) as well as training and support (White & Frederiksen, 1998) to im plement inquiry-oriented teaching. They also

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128 have very few operational models (Crawfor d, 1997) that they could use during inquiry instruction. The instrument devel oped in this study has the potent ial to support preservice science teachers to better understand what inquiry skills are expected of st udents and how technology can contribute to the fulfillment of these expectat ions. For instance, one of the scientific inquiry skills to which students are to be exposed and acquire knowledge about is addressing misconceptions of science content. Presentation devices and reliable Internet-based applications could help students identify and overcome th eir own misconceptions. As noted in the study findings, when preservice teachers are exposed to such a detailed approach, they can develop a better understanding of the inquiry skills e xpected from students and understand ways technology contribute to its achiev ement. The findings of the study point out that even inservice science teachers perceptions a nd actual classroom practices fo r using technology in a science inquiry oriented classroom differ. A more structured scientific inquiry education/training using technology in colleges and schools of educati on designed according to the National Science Education Standards for scientific inquiry might help close this gap for preservice and inservice science teachers. The study highlights areas where professional development activities for science teachers could focus. For instance, the findings that indica te science teachers low comfort levels and uses of inquiry skills such as supporting students to cr itique experiments and finding biases/flaws in their reasoning are salient for professional de velopment designers. In terms of technology, teachers low comfort levels in and uses of new and/or uncommon forms of technology tools/applications are also worth paying atten tion. Teachers are comfor table with technology tools that have been around for a long time such as email. The same teachers, on the other hand, report low a comfort level and integration when it comes to a relatively newer or unfamiliar

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129 forms of technology tools such as using Internet 2.0 tools. These technology tools exhibit high potential to facilitate scientific inquiry in cl assrooms with the help of technology. These results indicate the need to train science teachers to be able to use these techno logy tools and to show them how these technologies could bring about cha nge in their teaching pr actices toward better integration of technology to attain the goals of scientific inquiry. Professional development activities and pres ervice teacher education programs should concentrate more on increasing science teachers familiarities with these new and unfamiliar forms of technologies while continuing to encourage the use of most commonly used technologies. As study findings indicate, when science teachers get more prepared to use technology for scientific inquiry pu rposes they tend to use it more often in their classrooms for the same purposes. If science teachers familiarity with these technology tools increase, they will be more likely to appreciate the potential these t echnology tools carry for their instruction. Another finding of the study indicates almost all of the classrooms have at least one computer in them. This tells us that the limited use of thes e computer tools could be affected by teachers inability to use them effectively. Yet, if the computers that are available in classrooms are lacking the technical requirements to run these programs/applications effi ciently, it might also contribute to teaches inability to use thes e tools/applications in their classrooms. The findings also have implicati ons for school districts, comm unity colleges, colleges and schools of education, researchers, practitioners, administrators or even politicians who have the responsibility to provide training to science teachers or make decisions on their behalf. The components of the SIT-TIPPS instrument could be used by professors and trainers to help preservice and inservice science teachers iden tify what they are missing in their recent knowledge base and skills relate d to scientific inquiry and technology, and then focus their

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130 training/education on these missi ng areas. One tangible product of such an understanding, for example, would be to teach science teachers how to use data collection telecollaborative activities and how to integrate them to support students to develop hypotheses, collect/organize/analyze data, make judgments, and critique findings with peers. As this study indicates such uses are missing in our classrooms. As the findings of this study also indicates, science teachers comf ort levels with and exposure to inquiry skills and t echnology tools/applications matte r. Improving scientific inquiry and technology skills could be achieved by exposi ng teachers to these skills, technology tools, and strategies on a regular basis. They need to observe successful impl ementation examples and practice these skills before they complete their initial teacher preparation program. For inservice teachers, this translates to new policies in the form of mandatory scientific inquiry and technology professional developmen t programs/trainings. As this study illustrates, the higher the comfort level science teachers have with inquiry skills and technology tools, the more they use these skills and technologies in their classrooms. This could be done by providing teachers the opportunity to shadow teachers effectively enacting the goals of scientific inquiry with technology. Obviously, resources ar e also important to achieve these objectives. The studys findings show the relationship between computer access and integration. Therefore, schools and districts should invest more money, time, a nd energy on technology along with the professional development that is required to familiarize teach ers with their effective use in the classroom. This implication is also supported by rese arch. Recent studies on science teachers and inquiry suggest that professional development programs help science teachers develop inquirybased skills and applications in the cla ssroom (Wee, Shepardson, Fast, & Harbor, 2007). According to new evidence in the literature on th e nature of professional development programs,

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131 it is suggested that such programs should enable t eachers to experience the benefits of scientific inquiry-based practices as learne rs through inquiry-based activiti es (Spector, Burkett, & Leard, 2007) and should focus on student learning and learning difficulties encountered by students (Lotter, Harwood, & Bonner, 2006) in an inquiry at mosphere. In this respect, the content of the SIT-TIPPS instrument addresses all of these as pects of professional development. This is because it interprets essential f eatures of scientific inquiry fr om a students perspective and encourages teachers to look from the same pers pective. The way it details the components of scientific inquiry is a very us eful tool for assessing areas wh ere students are having (or could have) difficulties or inquiry activiti es should target in the first pla ce. As suggested in the recent empirical studies mentioned above, the use of th is approach could open new doors to alternative and effective professional de velopment programs for inservi ce science teachers and methods courses for preservice science teachers. Possibilities for Future Research Although the researcher was able to attract 715 science teache rs nationwide to participate in the survey and collected enough data to run f actor analyses reliably, a larger sample size would have produced more generalizable informa tion. In addition, the researcher did not make any attempt to differentiate between middle and high school science teachers when interpreting the results or developing and validating the SIT-TIPPS instrume nt. A more homogeneous sample could have been produced different results. Participation rate from various states were not equal. Some of the states were represented in the study with more participants. Because so cio-economic structures, beliefs and culture are engraved in the educational life of different states (or even with in a state) findings might exhibit differences in teachers perceptions and practices regarding the use of technology and scientific inquiry in science lessons, a different sample st ructure and state-wide re presentation might have

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132 resulted in a different respons e pattern. This might pinpoint an area of further investigation regarding the use of the SIT-TIPPS instrument. As mentioned above, although teachers report high percentage of computer presence within their classrooms and/or in computer labs, no attempt was ma de to determine the quality of this technology infrastructure. As is true with all new instruments, additiona l research is needed to support the validity and reliability of the instrument developed in this dissertation study. A ra ndomly chosen, larger and more representative sample might produce different results. Conclusion Literature indicates scientific inquiry is one of the major goals of science curriculum and science teachers play a key role in achieving this goal as well as in implementing technology to support the objectives of scientific inquiry-based instruction. The instrument developed in this study, the SIT-TIPPS, contributes to this body of literature by indicating how well prepared teachers self-report their ab ility to use scientific inquiry sk ills and technology tools in their classrooms and how often they use them. Resu lts indicate science teachers are below the satisfactory level in terms of im plementing some of the essential features of scientific inquiry skills and integrating some new generation of technology tools during science lessons. The results of the study highlight area s in which preservice science teachers and inservice science teachers need training and professional development. The researcher contends that if science teachers lack certain inquiry skills this woul d cause their students to lack the same skills. Therefore, these results should be alarming to t hose who are in charge of shaping the policies, initial teacher preparation progr ams, and professional developmen t activities for future science teachers.

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133 Results also indicate the im portance of providing more educ ational technology training to teachers continuously throughout their career. The more training they receive the more likely they tend to use these technologi es in their classrooms and, t hus, more often they use these technologies to facilitate scientific inquiry. In summary, the SIT-TIPPS instrument can be a highly effective tool for science educators, teachers, researchers, administrators, and policy makers to diagnose problems associated with science teachers perceptions and practices regard ing the use of technology tools/applications to attain the goals of scientific i nquiry and to elucidate the factors contributing to their low comfort levels with the uses of inquiry skills and techno logy tools in science clas srooms. Instruments that specifically targeting technology us e for scientific inquiry pur poses are limited in number. Therefore, this study makes a very valuable contribution to this body of literature by developing and validating the SIT-TIPPS instrument, which is capable of answering a wide range of questions dealing with science teachers perceptio ns and actual classroom practices regarding the use of technology for scientific inquiry purposes.

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134 Table 5-1. Teachers self-reported strengths and weaknesses in scientific inquiry. Strengths (from highest to lowest) Weaknesses (from lowest to highest) Level of preparedness to support students to: Collect, organize, and analyze data Id entify their own misconceptions of science content Explain cause-effect relationships Find biases and flaws in their scientific explanations Conduct experiments Test scie ntific explanations against current scientific knowledge Critique experiments Frequency of integration of skills to support students to: Explain cause-effect relationships Find biases and flaws in their scientific explanations Discuss scientific explanations/ideas/models with others Test scientific e xplanations against current scientific knowledge Conduct experiments Critique experiments Collect, organize, and analyze data Table 5-2. Teachers strengths and wea knesses in technology tools/applications. Strengths (from highest to lowest) Weaknesses (from lowest to highest) Level of preparedness to support students to: Word processing, Email, Internet searches, presentation software, spreadsheets, presentation devices Data collection telecollaborative activities, videoconferencing, teleconferencing, portable Global Positioning Systems, video editing, podcasts/videocasts, blogs, wikis, wireless communication devices, Smart Board/Promethean interactive boards, webpage design Frequency of integration of skills to support students to: Word processing, presentation devices, presentation software, Internet searches, Email Videoconferencing, teleconferencing, portable Global Positioning Systems, blogs, data collection telecollaborative activities, videoediting, wikis, podcasts/videocasts, wireless communication devices, image/picture editing, virtual experiences

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135 APPENDIX A RESEARCH STUDY INFORMED CONSENT FORM

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136 UFIRB 02 Social & Behavioral Research Protocol Submission Title of Protocol: Development and Validation of Scientific Inquiry with Technology Teacher Practices and Percep tions Scale (SIT-TPPS) 1. Principal Investigator: Ugur Baslanti UFID #: 8735-6150 Degree / Title: Doctoral Candidate in Educational Technology Department: School of Teaching and Learning Mailing Address: 323 University Vllg S. #1 Gainesville, FL 32603 Email Address & Telephone Number: baslanti@ufl.edu & (352) 846-5282 Co-Investigator(s): UFID#: Supervisor: Dr. Colleen Swain UFID#: N/A Degree / Title: Associate Professor & Graduate Coordinator Department: School of Teaching and Learning Mailing Address: PO Box 117048 School of Teaching & Learning University of Florida, Gainesville, FL 32611 Email Address & Telephone Number: (352) 392-9191, ext. 264 Date of Proposed Research: From Oct 15, 2007 to August 1, 2008. Source of Funding (A copy of the grant proposal must be s ubmitted with this protocol if funding is involved): None. Scientific Purpose of the Study: The purpose of this study is to develop and validat e a survey that focuses on scientific inquiry in science classrooms where technology is used. It is an attempt to address an essential topic both in science education and educational technology. Ev en though there are some instruments targeting scientific inquiry in science education literature, an in strument specifically ta rgeting scientific inquiry using technology is an area of need in both fields. Such an instrument will be helpful in analyzing the current practice in schools and colleges of education and in furthering the discussion on how science teachers can use technology to attain the goals of sci entific inquiry. For this purpose, this study intends to develop a quantitative instrument that measures teachers perceptions and practices regarding the use of technology in attaining the goals of scientifi c inquiry. It connects theory and research from two fields; science education and educational technology, wh ich can result in a change of daily practice in science classroom. Describe the Research Methodology in Non-Technical Language: ( Explain what will be done with or to the research participant. ) The researcher will contact middle and high school level science teachers nationwide via email or through national and statewide professional teacher or ganizations to participate in the study by filling

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137 out the online version of the survey, which will be situated in a fire-walled se rver at the College of Education. Some of the participants who live with in a reasonable distance to the researcher may be contacted in their schools and invited to fill out a paper based version of the survey. In either case, the participants will be given a consent form before they take the survey. In the online version, the participants will be directed to the online survey from an independent URL, which holds the consent form. Participants will be directed to the survey after they read the consent form and click on Press here to start the survey button. Participants will not be able to access the content s of the survey without submitting their consent. All data will be stored in a fire-walled server at the College of Education. Only the researcher and the supervisors will have access to data. The survey will also include a section that presents the operational definit ion of technology as it relates to the study; and a section that asks participants to complete demographics as well as a professional background (teaching experience etc.) information. Please see attached form. Describe Potential Benefits and Anticipated Risks: ( If risk of physical, psychological or economic harm may be involved, describe the steps taken to protect participant.) There are no known benefits or ri sks involved on the participants behalf. Describe How Participant(s) Will Be Recruited, the Number and AGE of the Participants, and Proposed Compensation: Approximately 500 science teachers at the middle and high school level nationwide will be recruited through the use of electronic mailing as well as personal contacts. The researcher w ill also contact some professional teacher organizations to email an invitation note out to their members. All participants will be at or over eighteen years old. The partici pants will not be compensated for participation in the study. Describe the Informed Consent Process. Include a Copy of the Informed Consent Document: The informed consent will be presented to the participants on their computer screen prior to participating in the study and they can print or save the page for their information if they would like to do so. These consent forms will be secured in a fire-w alled server at the College of Education. Please see attached form. If the participants wish to complete the paper vers ion of the survey, the paper version of the informed consent form will be presented to the participants bef ore they agree to participate in the study. The signed consent forms will be collected by the resear cher and secured in a safe place. Please see attached form. Principal Investigator(s) Signature: Supervisor Signature: Department Chair/Center Director Signature: Date:

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138 INFORMED CONSENT FORM (Online Version) Dear Science Teacher, My name is Ugur Baslanti, and I am a graduate student from the School of Teaching and Learning at the University of Florida. I would like to invite you to participate in an online study that focuses on scientific inquiry in science classrooms where technology is used. For this purpose, this study intends to develop a quantitative instrument that measures teachers perceptions and practices regarding the use of technology in attaining the goals of scientific inquiry Such an instrument will be helpful in analyzing the current practice in schools and colleges of education and in furthering the discussion on how science teachers can use technology to attain the goals of scientific inquiry. The procedure will entail the completion of a shor t demographic page and a short survey. It will take approximately 25 minutes to complete the survey. Participation in this project is completely volunt ary. You do not have to answer any questions you do not wish to answer, and you are free to withdraw your consent and to discontinue your participation at any time without any consequences. Your identity will be kept confidential to the extent provided by law There are no risks or direct benefits from your partici pation in this study apart from reflecting on your experience. You will not be co mpensated in any form for your partic ipation in this study. The measure will be kept secure and only accessible to Ugur Baslanti and his advisors, Dr. Colleen Swain and Dr. Tom Dana. You will not be associated with your response s, which will be kept secure. Data will be removed from the server as soon as practicable. Data w ill not be shared and it will be stored on a highly secure and firewalled server, which can only be accessed by the research investigator via password-protected file transfer protocols. This study has been approved by the University of Florida Inst itutional Review Board (IRB). For questions or concerns about your rights as a resear ch participant, contact the UFIRB office, P.O. Box 112250, University of Florida, Gainesvill e, FL 32611-2250. Phone: (352) 392-0433. If you have any questions about this research project, please contact Ugur Baslanti, baslanti@ufl.edu or Dr. Coll een Swain, Room (352) 392-9191 x 264. I have read the procedure described above and by clicking the below link, I am voluntarily agreeing to participate in th e survey study, and that I have received a copy of this description electronically. Press Here To Start The Survey

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139 INFORMED CONSENT FORM (Paper Version) Dear Science Teacher, My name is Ugur Baslanti, and I am a graduate student from the School of Teaching and Learning at the University of Florida. I would like to invite you to par ticipate in a study that focu ses on scientific inquiry in science classrooms where technology is used. Fo r this purpose, this study intends to develop a quantitative instrument that measures teachers perceptions and practices regarding the use of technology in attaining the goals of scientific inquiry Such an instrument will be helpful in analyzing the current practice in schools and colleges of education and in furthering the discussion on how science teachers can use technology to attain the goals of scientific inquiry. The procedure will entail the completion of a shor t demographic page and a short survey. It will take approximately 25 minutes to complete the survey. Participation in this project is completely volunt ary. You do not have to answer any questions you do not wish to answer, and you are free to withdraw your consent and to discontinue your participation at any time without any consequences. Your identity will be kept confidential to the extent provided by law There are no risks or direct benefits from your partici pation in this study apart from reflecting on your experience. You will not be compensated in any form for your part icipation in this study. Data will be accessible only to Ugur Baslanti and his advisors, Dr. Colleen Swain and Dr. Tom Dana and stored in a safe place at the College of Education. This study has been approved by the University of Florida Inst itutional Review Board (IRB). For questions or concerns about your rights as a resear ch participant, contact the UFIRB office, P.O. Box 112250, University of Florida, Gainesvill e, FL 32611-2250. Phone: (352) 392-0433. If you have any questions about this research project, please contact Ugur Baslanti, baslanti@ufl.edu or Dr. Coll een Swain, Room (352) 392-9191 x 264.

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140 APPENDIX B SURVEY INSTRUMENT

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141 Scientific Inquiry with Technology Te acher Perceptions and Practices Survey (SIT-TPPS) Ugur Baslanti School of Teaching & Learning University of Florida baslanti@ufl.edu Definition of Technology In this study, technology refers to a range of devices and tech nological processe s specifically used for teaching and learning purposes in K-12 settings. For the purposes of this study, the following devices with exam ples, comprise the definition of technologies: Computers (desktop, laptop) Presentation devices (such as video projectors, LCD panels) Smart Board/Promethean interactive boards Wireless communication devices (such as PDAs, phones, di gital response systems) Computer software (such as Word pro cessors, desktop publishing, spreadsheets, presentation software, databases, simula tions, games, graphing and data analysis software, video and picture editing software, etc.) Graphing/scientific calculators Portable Global Positioning Systems (GPS) Digital data collection devices (such as pH, pressure and temperature probes, digital microscope, Navigator systems) Videoconferencing, teleconferencing Internet technologies (such as e-mail, websites, online databases, virtual field trips, online simulations and games, Wikis, blogs, pod casts, videocasts, Google Earth and other Google tools, online learning communities) Data collection telecollaborative activiti es (such as Journey North, SCOPE, Amazing Space) Learning management systems (such as WebCT, Blackboard)

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142Scientific Inquiry with T echnology Teacher Perceptions and Practices Survey (SIT-TPPS) Indicate how well prepared you currently feel to do each of the following in your science classroom with or without technology Indicate how often the following happens in your science instruction To support students to: Not adequately prepared Somewhat prepared Fairly well prepared Very well prepared Never Rarely (e.g.,a few times a year) Sometimes (e.g.,once or twice a month) Often (e.g.,once or twice a week) Almost or all science lessons Inquiry Skills 1 Ask researchable questions 1 2 3 4 1 2 3 4 5 2 Conduct experiments 1 2 3 4 1 2 3 4 5 3 Collect, organize, analyze data 1 2 3 4 1 2 3 4 5 4 Identify their own misconceptions of science content 1 2 3 4 1 2 3 4 5 5 Explain cause-effect relationships 1 2 3 4 1 2 3 4 5 6 Test scientific explan ations against current scientific knowledge 1 2 3 4 1 2 3 4 5 7 Find biases or flaws in their scientific explanations 1 2 3 4 1 2 3 4 5 8 Discuss scientific explanations/ideas/models with others 1 2 3 4 1 2 3 4 5 9 Critique experiments 1 2 3 4 1 2 3 4 5

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143 Indicate how well prepared you currently feel to use each of the following in your science classroom Indicate how often you use the following in your science instruction Not adequately prepared Somewhat prepared Fairly well prepared Very well prepared Never Rarely (e.g.,a few times a year) Sometimes (e.g.,once or twice a month) Often (e.g.,once or twice a week) Almost or all science lessons Technology tools 1 Presentation devices (such as video projectors, LCD panels) 1 2 3 4 1 2 3 4 5 2 Smart Board/Promethean interactive boards 1 2 3 4 1 2 3 4 5 3 Wireless communication devices (such as PDAs, student digital response systems) 1 2 3 4 1 2 3 4 5 4 Graphing/scientific calculators 1 2 3 4 1 2 3 4 5 5 Portable Global Positioning Systems (GPS) 1 2 3 4 1 2 3 4 5 6 Digital data collection devices (such as pH, pressure and temperature probes, digital microscopes, Navigator systems) 1 2 3 4 1 2 3 4 5 7 Videoconferencing, teleconferencing 1 2 3 4 1 2 3 4 5 8 Word processing (e.g., Word) 1 2 3 4 1 2 3 4 5 9 Spreadsheets (e.g., Excel) 1 2 3 4 1 2 3 4 5 10 Presentation software (e.g., Power Point) 1 2 3 4 1 2 3 4 5 11 Database software 1 2 3 4 1 2 3 4 5 12 Educational games 1 2 3 4 1 2 3 4 5

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144 Indicate how well prepared you currently feel to use each of the following in your science classroom Indicate how often you use the following in your science instruction Not adequately prepared Somewhat prepared Fairly well prepared Very well prepared Never Rarely (e.g.,a few times a year) Sometimes (e.g.,once or twice a month) Often (e.g.,once or twice a week) Almost or all science lessons 13 Virtual experiences (such as Google Earth and Starry Night, a virtual planetarium) 1 2 3 4 1 2 3 4 5 14 Graphing and data analysis software 1 2 3 4 1 2 3 4 5 15 Video editing 1 2 3 4 1 2 3 4 5 16 Image/picture editing 1 2 3 4 1 2 3 4 5 17 E-mail 1 2 3 4 1 2 3 4 5 18 Webpage design 1 2 3 4 1 2 3 4 5 19 Accessing online databases 1 2 3 4 1 2 3 4 5 20 Internet searches 1 2 3 4 1 2 3 4 5 21 Online simulations 1 2 3 4 1 2 3 4 5 22 Online science games 1 2 3 4 1 2 3 4 5 23 Wikis 1 2 3 4 1 2 3 4 5 24 Blogs 1 2 3 4 1 2 3 4 5 25 Podcasts, videocasts 1 2 3 4 1 2 3 4 5 26 Data collection telecollaborative activities (e.g., Journey North, SCOPE, Amazing Space) 1 2 3 4 1 2 3 4 5

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145 Please indicate your level of agreem ent with the followi ng statements Strongly Disagree Disagree Neutral Agree Strongly Agree 1 I integrate technology to enable students to ask scientifically oriented questions. 1 2 3 4 5 2 A science teacher should integrate technology to enable students to obtain evidence from various sources. 1 2 3 4 5 3 A science teacher should integrate technology to stimulate students to inquire about scientific phenomena. 1 2 3 4 5 4 I integrate technology to involve students in authentic/real world scientific issues. 1 2 3 4 5 5 I integrate technology to enable students to condu ct successful empirical i nvestigations. 1 2 3 4 5 6 A science teacher should integrate technology to enable students to evaluate their proposed explanations based on eviden ce and scientific knowledge. 1 2 3 4 5 7 I integrate technology to enable students to relate new concepts/ideas with their prior knowledge. 1 2 3 4 5 8 A science teacher should integrate technology to facilitate students collection, organization, and analysis of scientific data. 1 2 3 4 5 9 I integrate technology to encourage students to compare the ideas they have developed as a result of classroom inquiry against scientific facts. 1 2 3 4 5 10 I integrate technology to enable students to formulate explanations and coherent arguments to address scientifically oriented questions. 1 2 3 4 5 11 I integrate technology to enable students to make reasoned judgments based on scientific evidence. 1 2 3 4 5 12 A science teacher should integrate technology to enable students to formulate explanations of experimental and observational evidence. 1 2 3 4 5 13 A science teacher should integrate technology to enable students to use technology as instruments to make scientific observations. 1 2 3 4 5

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146 Please indicate your level of agre ement with the follo wing statements Strongly Disagree Disagree Neutral Agree Strongly Agree 14 I integrate technology to enable students to evaluate scientific explanations. 1 2 3 4 5 15 I integrate technology to improve students skills to check their results against existing scientific knowledge. 1 2 3 4 5 16 A science teacher should integrate technology to enable students to use technology as instruments to collect data for conducting scientific experiments. 1 2 3 4 5 17 I integrate technology to enable students to critique experiments with others. 1 2 3 4 5 18 I integrate technology to enable students to di scuss their scientific explanations/ideas/models with others. 1 2 3 4 5 19 I integrate technology to stimulate my students skills to develop hypotheses based on their own observations and measurements of scientific phenomena. 1 2 3 4 5 20 A science teacher should integrate technology to develop students knowledge and understanding of scientific ideas based on data collected from students. 1 2 3 4 5 21 A science teacher should integrate technology to encourage students to gather and use data to develop explanations for scientific phenomena. 1 2 3 4 5 22 I integrate technology to encourage students to justify their scientific arguments with others. 1 2 3 4 5 23 A science teacher should integrate technology to enable students to make connections between their results and existing scientific knowledge. 1 2 3 4 5 24 I integrate technology to enable students to identify and overcome their misconceptions of science content covered. 1 2 3 4 5 25 I integrate technology to enable students to find biases or flaws in the reasoning connecting scientific evidence and explanations. 1 2 3 4 5

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147 Please indicate your level of agre ement with the follo wing statements Strongly Disagree Disagree Neutral Agree Strongly Agree 26 A science teacher should integrate technology to enable students to ask scientifically oriented questions. 1 2 3 4 5 27 I integrate technology to enable students to obtain evidence from various sources. 1 2 3 4 5 28 I integrate technology to stimulate students to inquire about scientific phenomena. 1 2 3 4 5 29 A science teacher should integrate technology to involve students in authentic/real world scientific issues. 1 2 3 4 5 30 A science teacher should integrate technology to enable students to conduct successful empirical investigations. 1 2 3 4 5 31 I integrate technology to enable students to evaluate their proposed explanations based on evidence and scientific knowledge. 1 2 3 4 5 32 A science teacher should integrate technology to enable students to relate new concepts/ideas with their prior knowledge. 1 2 3 4 5 33 I integrate technology to facilitate students collec tion, organization, and analysis of scientific data. 1 2 3 4 5 34 A science teacher should integrate technology to encourage students to compare the ideas they have developed as a result of classroom inquiry against scientific facts. 1 2 3 4 5 35 A science teacher should integrate technology to enable students to formulate explanations and coherent arguments to address scientifically oriented questions. 1 2 3 4 5 36 A science teacher should integrate technology to enable students to make reasoned judgments based on scientific evidence. 1 2 3 4 5 37 I integrate technology to enable students to formulate explanations of experimental and observational evidence. 1 2 3 4 5 38 I integrate technology to enable students to use technology as instruments to make scientific observations. 1 2 3 4 5

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148 Please indicate your level of agre ement with the follo wing statements Strongly Disagree Disagree Neutral Agree Strongly Agree 39 A science teacher should integrate technology to enable students to evaluate scientific explanations. 1 2 3 4 5 40 A science teacher should integrate technology to improve students skills to check their results against existing scientific knowledge. 1 2 3 4 5 41 I integrate technology to enable students to use technology as instruments to collect data for conducting scientific experiments. 1 2 3 4 5 42 A science teacher should integrate technology to enable students to critique experiments with others. 1 2 3 4 5 43 A science teacher should integrate technology to enable students to discuss their scientific explanations/ideas/models with others. 1 2 3 4 5 44 A science teacher should integrate technology to stimulate students skills to develop hypotheses based on their own observations and measurements of scientific phenomena. 1 2 3 4 5 45 I integrate technology to develop students knowledge and understanding of scientific ideas based on data collected from students. 1 2 3 4 5 46 I integrate technology to encourage students to gather and use data to develop explanations for scientific phenomena. 1 2 3 4 5 47 A science teacher should integrate technology to encourage students to justify their scientific arguments with others. 1 2 3 4 5 48 I integrate technology to enable students to make connections between their results and existing scientific knowledge. 1 2 3 4 5 49 A science teacher should integrate technology to enable students to identify and overcome their misconceptions of science content covered. 1 2 3 4 5 50 A science teacher should integrate technology to enable students to find biases or flaws in the reasoning connecting scientific evidence and explanations. 1 2 3 4 5

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149 APPENDIX C LITERATURE BASE USED TO DEVELOP THE ITEM POOL

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150Table C-1. Definition and skills table of th e essential features of scientific inquiry for the development of the instrument { NRC (2000). Inquiry and the National Science Education Standards: A guide for te aching and learning. Washington, DC: National Academy Press} 1. Teacher engages students in scient ifically oriented questions A question robust and fruitful enough to drive an inquiry genera tes a "need to know" in students, stimulating additional questi ons of "how" and "why" a phenomenon occurs. The initial question may originate from the learner, the teacher, the instructional materials, the Web, some other source, or some combination. The teacher plays a critical role in guiding the identification of questions, particularly when they come from students. Fruitful inquiries evolve from questions that are meaningful and relevant to students, but they also must be able to be answered by student s' observations and scientific knowledge they obtain from reliabl e sources. The knowledge and procedures students use to answer the questions must be accessible and manageable, as well as appropriate to the students' developmental le vel. Skillful teachers help students focu s their questions so that they can experience both interesting and productive i nvestigations (NRC, 2000, p. 24). Teacher and student skills & questions to consider Derived from NRC (2000): Lending to empirical investigation Leading to gathering and using data to develop explanations fo r scientific phenomena Asking why and how questions Generating a need to know in students, stimulating additional questions of how and why a phenomenon occurs Stimulate interest in science Generate/ask questions that center on objects, organisms, and events in the natural world Questions connect to the science concepts described in the content standards, guide the identification of questions, help stude nts focus their questions so that they can experien ce both interesting and productive investigations Addendum from th e literature: Seeking information from experts Seeking for information/researching conjectures Planning/designing/conducting empirical investigations Encouraging students to demand more knowledge Developing an appreciation of how we know Posing real world questions/dea ling with authentic problems Engaging students in identifying/ diagnosing problems/defining and representing a problem

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151 2. Teacher encourages students to give priori ty to evidence, which allows them to develop and evaluate explanations that address scientifically oriented questions Students use evidence to develop explanations for scientific phenomena. They observe plants, animal, and rocks, and carefully describe their characteristics. They take measurements of temperature, distances, and time, and carefully record them. They observe chemical reactions and moon phases and chart their progress. Or they obtain ev idence from their teacher, instructional materials, the Web, or elsewhere, to "f uel" their inquiries (NRC, 2000, p. 26). Teacher and student skills & questions to consider Derived from NRC (2000): The use of empirical evidence as the basis for explanations about how the natural world works Getting accurate data from observations of phenomena Obtaining evidence from observations and measurements taken in natural settings such as oceans, or in contrived settings such a s labs Using senses, instruments Gather data over a wide range of naturally occurring conditions and over a long enough period of time so that they can infer wh at the influence of different factors might be The accuracy of the evidence gathered is verified by checking m easurements, repeating the observations, or gathering different kinds of data related to the same phenomenon The evidence is subject to ques tioning and further investigation Students use evidence to develop expl anations for scientific phenomena Students observe plants, animals, and rocks, and carefully describe their characteristics Taking measurements of temperature, distan ces, and time, and carefully record them Students obtain evidence from their teachers, instru ctional materials, the we b, to fuel their inquiry Addendum from the literature: Making observations Reviewing what is already known in light of experimental evidence Using tools to gather, anal yze, and interpret data Predicting, collecting, and analyzing data Dealing with data Developing hypotheses Designing experiment or study Observing, exploring, and generating strategies

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1523. Teacher helps students formulate explanations from evidence to address scientifically oriented questions Explanations are ways to learn about what is unfamiliar by relating what is observed to what is already known. So, explanations go beyond current knowledge and propose some new unde rstanding. For science, this means buildin g upon the existing knowledge base. For st udents, this means building new ideas upon their current understandings. In both cases, the result is proposed new knowledge. For examp le, students may use observational and other evidence to propose an explana tion for the phases of the moon; for why plants die under certain conditions and thrive in others; and for the relations hip of diet to health (NRC, 2000, p. 26). Teacher and student skills & questions to consider Derived from NRC (2000): Emphasizes the path from evidence to e xplanation rather than the criteria fo r and characteristics of the evidence. Scientific explanations are based on reason. They provide cau ses for effects and establish relationships based on evidence and logical argument. They must be consistent with experimental and observational evidence about nature, they respect rules of evidence, ar e open to criticism, and require the use of various cognitive processes ge nerally associated with science e.g. classification, analysis, inference, and prediction, and general processes such as cr itical reasoning and logic. Explanations ar e ways to learn about what is unfamiliar by relating what is observed to what is already known. So, explanations go beyond current knowledge and propose some new understanding. Building upon the existing knowledge base; building new ideas upon their current un derstandings: The result is proposed knowledge. (e.g.) student s may use observational and other evidence to propose an explanation fo r the phases of the moon; for why plants die under certain conditions and thrive in others; relationship of diet to health Addendum from the literature: Forming coherent arguments The activities of students in which they develop knowledge a nd understanding of scientific id eas, as well as an understanding o f how scientists study the natural world Developing epistemological understanding about the nature of science and the development of scientific knowledge Developing a broader understanding of science Constructing/formulating explanations Formulating and testing scientific rules and laws Interpreting, evaluating Students describe objects and events Drawing inferences Using logical & critical thinking to formulate conclusions Identifying ones own assumptions Improving students critical reasoning and problem solving skills Enabling students to develop their own ideas by buildi ng connections between their existing ideas and new ideas Uncovering new scientific principles and refining their preexisting understandings Relating information with prior knowledge and then integrating into larger knowledge structures Making a reasoned judgment based on appropriate evidence Dealing with misconceptions

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1534. Teacher helps students connect explanations to scientific knowledge. Teachers he lp students evaluate their explanations in light of alternative explanations, particularly those reflecting scientific understanding Alternative explanations may be reviewed as students engage in dialogues, comp are results, or check th eir results with those proposed by the teacher or instru ctional materials. An essential component of this characteristic is ensu ring that students mak e the connection between their results and scientif ic knowledge appropriate to their level of development. That is, student explanati ons should ultimately be consistent with currently accepted scientific knowledge (NRC, 2000, p. 27). Teacher and student skills & questions to consider Derived from NRC (2000): Evaluation and possible elimination or revisi on of explanations is one feature that di stinguishes scientific from other forms of inquiry and subsequent explanations Does the evidence support the proposed explanation? Does the explanation adequately answer the questions? Are there any apparent biases or flaws in th e reasoning connecting evid ence and explanation? Can other reasonable explanations be derived from the evidence? Alternative explanations may be reviewed as students engage in dialogues, comp are results, or check th eir results with these proposed by the teacher or instructional materials Students make the connections between their results and scientific knowledge appropriate to their level of development. That is student explanations should ultimately be consis tent with currently accep ted scientific knowledge Addendum from the literature: Students test their explanations ag ainst current scientific knowledge Proposing answers, explanations, and predictions Distinguishing alternatives Evaluating/Considering alte rnative explanations Building theories

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154 5. Teacher encourages students to communicate and justify their proposed explanations Having students share their explanations provides others the opportunity to ask que stions, examine evidence, identify faulty reasoning, point out statements that go beyond the evidence, and suggest alternative explanations for the same observations. Sh aring explanations can bring into question or fortify the connections students have made among the evidence, existing scientific knowledge, and their proposed explanations. As a result, students can resolve contradi ctions and solidify an empirically based argument (NRC, 2000, p. 27). Teacher and student skills & questions to consider Derived from NRC (2000): Having students share their explanations provides others the opportunity to ask que stions, examine evidence, identify faulty reasoning, point out statements that go beyond the evidence, and suggest alternative explanations for the same observations Sharing explanations can bring into questi on or fortify the connections students have made among the evid ence, existing scienti fic knowledge, and their proposed explanations. As a result, students can resolve contradi ctions and solidify an empirically based argument Addendum from the literature: Communicating the results findings, and ideas Critiquing experiments Debating with peers Communicating and defending hypotheses, models, and explanations Persuading peers Argumentation

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155Table C-2. Essential features of scientif ic inquiry for the development of the inst rument. (1) Teacher e ngages students in scientifically oriented questions. (2) Teacher encourag es students to give priority to eviden ce. (3) Teacher helps students formulate explanations from evidence to address scie ntifically oriented questions. (4) Teacher helps students connect explanations to scientific knowledge. (5) Teacher encourages students to communicate and justify th eir proposed explanations 1 2 3 4 5 posing (real world) questions (NRC, 1996; Crawford, 2000; Roth & Michelle, 1998; Keys, 1997) making observations (NRC, 1996; Brendzel, 2005; Sherman & Sherman, 2004; Bodzin, 2005) the activities of students in which they develop knowledge and understanding of scientific ideas, as well as an understanding of how scientists study the natural world (NRC, 1996) proposing answers, explanations, and predictions (NRC, 1996) communicating the results, findings (their ideas) ( NRC, 1996; Sherman & Sherman, 2004; Bodzin, 2005). developing question, defining and representing problem (Lee et al., 2004) reviewing what is already known in light of experimental evidence (NRC, 1996) forming coherent arguments (Linn et al., 2004) distinguishing alternatives (Linn et al., 2004) critiquing experiments (Linn et al., 2004) examining books and other sources of information to see what is already known (NRC, 1996) using tools to gather, analyze, and interpret data (NRC, 1996; Brendzel, 2005; Keys, 1997) develop epistemological understandings about the nature of science and the development of scientific knowledge (Abd-elKhalick et al., 2004) test those explanations against current scientific knowledge (NRC, 1996) debating with peers (Linn et al., (2004) seeking information from experts (Linn et al., 2004) developing hypothesis (Wichmann et al., 2003; Crawford, 2000; Sherman & Sherman, 2004; Bodzin, 2005) formulating explanations (Abdel-Khalick et al., 2004; Keys, 1997) considering alternative explanations (NRC, 1996; Schwab, 1960, cited in NRC, 2000, p.21) communicating, and defending hypothesis, models, and explanations (Abd-el-Khalick et al., 2004) researching conjectures (Linn et al., 2004) dealing with data (Crawford, 2000) using logical and critical thinking to formulate conclusions (Sherman & Sherman, 2004; Bodzin, 2005) building theories (Crawford, 2000) persuading peers (Roth & Michelle, 1998) searching for information (Linn et al., 2004) predicting, collecting and analyzing data (Sherman & Sherman, 2004; Bodzin, 2005) formulate and test scientific rules and laws (Looi, 1998) evaluate alternative explanations (Sherman & Sherman, 2004; Bodzin, 2005). discussion-verbal interactions with peers and teacher(Westbrook, 1997) planning (designing and conducting) investigations (NRC, 1996; Linn et al., 2004; Abd-el-Khalick et al., 2004); Crawford, 2000; Keys (1997) designing experiment or study (Lee et al., 2004) an understanding of the nature of science (Sherman & Sherman (2004) argumentation (Zembal-Saul & Land, 2002; McDonald, 2004),

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156Table C-2. Continued. engaging students in the intentional process of diagnosing problems (Linn et al., 2004; Lee et al., 2004) observing, exploring and generating strategies, organizing, analyzing (Lee et al., 2004) interpreting, and evaluating (Lee et al., 2004) debates about the use of evidence (Schwab, 1960, cited in NRC, 2000, p.21) relevant inquiry skills such as identifying problems (Abd-elKhalick et al., 2004) interdisciplinary contexts (Myers & Botti, 1997) developing a broader understanding of science (Bodzin, 2005) generating research questions, (Abd-el-Khalick et al., 2004) students describe objects and events (NRC, 1996) engaging in inquiry helps students develop an appreciation of how we know (Sherman & Sherman, 2004) construct explanations (NRC, 1996) encouraging them to demand more knowledge (Edelson et al., 1999) identifying assumptions (NRC, 1996) applying their scientific understanding in the pursuit of research questio ns (Edelson et al., 1999) use critical and logical thinking, (NRC, 1996) authentic problems (Flick, 1997) improves their critical reasoning and problem solving skills (Bodzin, 2005) enables students to develop their own ideas by building connections between their existing ideas and new ideas (Berge & Slotta, 2005) drawing inferences (Crawford, 2000)

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157Table C-2. Continued. uncovering new scientific principles and refining their preexisting understandings (Edelson et al., 1999) relating information with prior knowledge and then integrating into larger knowledge structures (Myers & Botti, 1997) making a reasoned judgment based on appropriate evidence (Lee at al., 2004) deal with misconceptions (Cognition and Technology Group at Vanderbilt, 1992; Nickerson, 1995)

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158 LIST OF REFERENCES Abd-El-Khalick, F., BouJaoude, S., Duschl, R. A ., Hofstein, A., Lederman, N. G., Mam lok, R., Niaz, M., Treagust, D., & Tuan, H. (2004). I nquiry in science edu cation: International perspectives. Science Education, 88 (3), 397-419. Abrams, E. (1998). Talking and doing scienc e: Important elements in a teaching-forunderstanding approach. In J. J. Mintzes, J. S. Wandersee, & J. D. Novak (Eds.), Teaching science for understanding: A Human Constructivist Approach (pp. 307) San Diego, CA: Academic Press. Alagic, M., Yeotis, C., Rimmington, G. M., & Koert, D. (2003). Inquiry and information technology integration: Cognitive apprentices hip learning environment model (CALEM). Proceedings of the Society for Information Technology and Teacher Education, USA, 114, 826-833. American Association for the A dvancement of Science. (1990). Science for all Americans New York: Oxford University Press. American Association for the A dvancement of Science. (1993). Benchmarks for science literacy New York: Oxford University Press. Anderson, R. D. (2002). Reforming Science Teac hing: What research says about inquiry, Journal of Science Teacher Education, 13 (1), 1-12. Bebell, D., Russell, M., & ODwyer, L. (2004). Measuring teachers technology uses: Why multiple-measures are more revealing. Journal of Research on Technology in Education. 37(1), 45-63. Becker, H. J. (1994). How exemplary compute r-using teachers differ from other teachers: Implications for realizing the pot ential of computers in schools. Journal of Research on Computing in Education, 26 (3), 291-321. Bencze, L., & Hodson, D. (1999). Changing practice by changing practice: Toward more authentic science and science curriculum development. Journal of Research in Science Teaching, 36 (5), 521-539. Benson, A. & Bruce, B. C. (2001). Using the Web to promote inquiry and collaboration: A snapshot of the Inquiry Pages development. Teaching Education, 12(2), 153-163. Berge, O., & Slotta, J. D. (2005, June). Learning technology st andards and inquiry-based learning. Paper presented at the meeting of the In formation Science and IT Education Joint Conference, Arizona. Retrei ved October 12, 2006, from http://www.hia.no/iris 28/Docs/IRIS2028-1011.pdf

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159 Berger, C. F., Lu, C. R., Belzer, S. J., & Voss, B. E. (1994). Research on the uses of technology in science education. In D. L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 466). New York: Macmillan. Bitner, N., & Bitner, J. (2002). In tegrating technology into the clas sroom: Eight keys to success. Journal of Technology and Teacher Education, 10 (1), 95-100. Bodzin, A. M. (2005). Implementing web-based scientific inquiry in preservice science methods courses. Contemporary Issues in Technol ogy and Teacher Education, 5 (1), 50-65. Bodzin, A. M., & Beerer, K. M. (2003). Promoting inquiry-based scie nce instruction: The validation of the Science T eacher Inquiry Rubric (STIR) Journal of Elementary Science Education, 15 (2), 39-49. Brandon, P. R., & Taum, A. K. H. (2005, April). Instrument development for a study comparing two versions of inquiry scie nce professional development. Paper presented at the annual meeting of the American Educational Research Association, Montreal. Bransford, J., Brown, A., & Roney, R. (1999). How people learn: Brain, mind and school Washington, DC: National Academy Press. Brendzel, S. (2005). Strategies for successful science teaching Maryland: University Press of America, Inc. Brown, T. A. (2006). Confirmatory factor anal ysis for applied research New York: Guilford Press. Bruce, B. C., & Bishop, A. P. (2002). Using the Web to support inqu iry-based literacy development. Journal of Adolescent and Adult Literacy, 45 (8), 706-715. Bryant, F. B., & Yarnold, P. R. (1995). Principal-components analysis and exploratory and confirmatory factor analysis. In L. G. Grimm & P. R. Yarnold (Eds.). Reading and understanding multivariate statistics (pp.99-136). Washington, DC: American Pyschological Association. Carin, A. A., & Bass, J. E. (2001). Methods for teaching science as inquiry (8th ed.). Upper Saddle River, NJ: Merrill/Prentice Hall. Carnes, G. N. (1997, March). Teacher conceptions of inquir y and related teaching practices Paper presented at the annual meeting of the National Association for Research in Science Teaching, Chicago, IL. Chiero, R. T. (1997). Teachers perspectives on factors that affect computer use. Journal of research on Computing in Education, 30 133-145.

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160 Cognition and Technology Group at Vanderbilt. ( 1992). The Jasper experiment: An exploration of issues in learning and instructional design. Educational Technology Research & Development, 40 (1), 65-80. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Collins, A. (1991). The role of comput er technology in restructuring schools. Phi Delta Kappan, 73(1), 28-36. Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis Hillsdale, NJ: Lawrence Erlbaum Associates. Comstock, S. L., Bruce, B. C., Harnish, D. (2003, March). The Inquiry-Page: Collaborative technology into practice. Paper presented at the meeting of the14th annual International Society for Information Technology & Teacher Education Conference, Albuquerque, NM. Cooper, J. M., & Bull, G. L. (1997). Technolog y and teacher education: Past practice and recommended directions. Action in Teacher Education, 19(2), 97-106. Costello, A. B., & Osborne, J. W. (2005). Best practices in expl oratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10 (7). Retreived March 12, 2008, from http://pareonline.net/getvn.asp?v=10&n=7 Crawford, B. A. (1997, March). A community of inquiry: Changing roles for teachers and students. Paper presented at the annual m eeting of the National Association for Research in Science Teaching, Chicago, IL. Crawford, B. A. (2000). Embracing the essence of inquiry: New roles for science teachers. Journal of Research in Science Teaching, 37 916. DeBoer, G. E. (1991). A history of ideas in science educ ation: Implications for practice. NewYork: Teachers College Press. Dede, C. (2000). Emerging influences of information technology on school curriculum Journal of Curriculum Studies, 32 (2), 281-303. DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). California: Sage Publications. Drenoyianni, H., & Selwood, I. (1998). Concep tions or misconceptions? Primary teachers perceptions and use of co mputers in the classroom Education and Information Technologies, 3, 87-89.

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169 BIOGRAPHICAL SKETCH Ugur Baslan ti was born in Istanbul, Turkey, in 1975. He received his Bachelor of Science degree in chemistry education and his Master of Science degree in seco ndary school science and mathematics education with an emphasis on gift ed education from Bogazici University in Istanbul. He worked as a teaching and research a ssistant at this university for three years, but also taught chemistry, physics, and mathematics part-time in a private educational institution. In 2002 and 2004 he taught chemistry in a summer progr am for the gifted organized by the Center for Talented Youth at the Johns Hopkins University. He started working toward his doctorate degr ee in educational tec hnology in the School of Teaching and Learning at the University of Florida in 2002. During his doctoral studies Ugur taught Instructional T echnology Lab and Technology Integrated in Mathematics Curriculum to undergraduate and graduate pre-service teachers fo r two years and served as a research assistant on a variety of educational grants including Preparing Tomorrows Teachers to Use Technology (PT3) and Classroom Connectivity. His areas of interest are gifted underachievers, teacher education, and the integration of technology into mathematic s and science instruction.