Differences in academic achievement, school affiliation, student and teacher efficacy beliefs, parents' perceptions and ...

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Differences in academic achievement, school affiliation, student and teacher efficacy beliefs, parents' perceptions and teacher instruction between highly mobile students placed at stable and traditional schools
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Anusavice, Sandra Lee Hatch
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Table of Contents
    Title Page
        Page i
    Dedication
        Page ii
    Acknowledgement
        Page iii
    Table of Contents
        Page iv
        Page v
        Page vi
    List of Tables
        Page vii
        Page viii
        Page ix
    Abstract
        Page x
        Page xi
    Chapter 1. Introduction
        Page 1
        Page 2
        Page 3
        Page 4
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    Chapter 2. Review of literature
        Page 12
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    Chapter 3. Materials and methods
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    Chapter 4. Results and discussion
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    Chapter 5. Summary and conclusions
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    References
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    Appendix A. Observation questions
        Page 184
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    Appendix B. Student survey
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    Appendix C. Teacher survey
        Page 189
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    Appendix D. Student interview questions
        Page 191
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    Appendix E. Student assent script
        Page 193
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    Appendix F. Institutional review board approval
        Page 195
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    Appendix G. Parent consent forms
        Page 197
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    Appendix H. Teacher consent form
        Page 201
        Page 202
    Biographical sketch
        Page 203
        Page 204
        Page 205
        Page 206
Full Text










DIFFERENCES IN ACADEMIC ACHIEVEMENT, SCHOOL AFFILIATION,
STUDENT AND TEACHER EFFICACY BELIEFS, PARENTS' PERCEPTIONS,
AND TEACHER INSTRUCTION BETWEEN HIGHLY MOBILE STUDENTS
PLACED AT STABLE AND TRADITIONAL SCHOOLS













By

SANDRA HATCH ANUSAVICE


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


1999






























For my husband, Ken, who always knew I could do it.














ACKNOWLEDGMENTS


Many people have helped me along the way to this accomplishment. Foremost

among this group are the teachers and school administrators who allowed me to be a part

of their classrooms for a year. I appreciate their graciousness and assistance. I also thank

my friends and colleagues Susan Culbert and Jan Scott from whom I learned so much

about exemplary teaching.

I would especially like to thank my committee members Dr. Robert Wright, Dr.

Jin-Wen Hsu, and Dr. James Doud for their excellent teaching, their faith in me, and

their support throughout this process. For my committee chair, Dr. Linda Behar-

Horenstein, there are not words to express my gratitude. She has been my mentor, my

teacher, and my friend. Because of her guidance, my journey toward the Ph.D. has been a

joyous experience, a growth of intellect and understanding that she has shaped and

nurtured. A mere 'Thank You' cannot suffice. I hope that my work will be a tribute to

her leadership.

Finally, I thank my family who contributed so much to my achievement: my

parents, Rudolph and Kitty Hatch, who gave me a love of language and taught me its

power; my children, Jill and Joel, who have always encouraged me to 'go for it'; and my

wonderful husband, Ken, who has helped me build the bridge between dreams and reality

and supported me in all things.














TABLE OF CONTENTS
page


A CKN OW LEDGM EN TS ................................................................................................. iii

LIST OF TABLES ................................................ vii

ABSTRA CT ....................................................................................................................... x

CHAPTERS

1. IN TRODU CTION ........................................................................................................ I

Background for the Study .............................................................................................. 3
Statem ent of the Problem s .............................................................................................. 4
Statem ent of Hypotheses ............................................................................................... 5
Definition of Term s ........................................ ................................................................ 6
Significance of the Study ................................................................................................. 9
Lim itations ......................................................................................................................... 10

2. REVIEW OF LITERATURE ................................................................................. 12

Student M obility 1............................................................................................................... 12
Research on Student M obility ................................................................................. 13
Im pact on Student Achievem ent ............................................................................ 14
Im pact on School System s ........................................................................................... 20
Teacher Efficacy ................................................................................................................. 24
Teacher Efficacy Beliefs and Teacher Behavior ...................................................... 24
Teacher Efficacy Beliefs and Student Achievem ent ..................................................... 29
Teacher Efficacy Beliefs, Management, and Instructional Styles ................................ 31
Student Effi cacy ................................................................................................................ 32
Instructional Strategies ................................................................................................. 35
S u m m ary ............................................................................................................................ 3 9

3. M ATERIAL S AND M E TH OD S ................................................. 41

T h e S ettin g s ........................................................................................................................ 4 1
P a rtic ip a n ts ........................................................................................................................ 4 3


iv








Tasks and M aterials .................................................................................................... 45
Operational Definition of Variables .............................................................................. 48
Instrum entation ........50...................................................................................................... 50
Curriculum -Based Assessm ents ............................................................................... 50
Parent Survey ............................................................................................................... 51
Teachers' Instructional Strategies .......................................................................... 53
School Affi liation ......................................................................................................... 54
D ata Collection ................................................................................................................... 54
Data Analysis ..................................................................................................................... 55

4. RESULTS AND DISCU SSION ............................................................................ 57

Quantitative Analysis ................................................................................................... 57
Research Question 1: Academic Achievement, Student Absenteeism,
Discipline Referrals ..................................................................................................... 58
Academ ic Achievem ent .......................................................................................... 58
Student Absenteeism .............................................................................................. 59
Discipline Referrals ..................................................................................................... 62
Research Question 2: Teacher Efficacy ....................................................................... 63
Research Question 3: Parent Perceptions of Learning Environment ........................... 64
Research Question 4: Student Self-Effi cacy .......................................................... 66
Qualitative Analysis 6......................................................................................................... 66
Instructional M odels and Learning Environm ent ........................................................ 67
Teachers' Instructional Activities ............................................................................... 75
Instructional Activities of Students ..................................... 78
Teachers' and Students Instructional Roles .......................................................... 80
Exam ples of Instructional Strategies ........................................................................... 89
Research Question 6: Students' Efficacy Beliefs ..................................................... 105
Teacher Responses to Students ................................................................................107
Exam ples of Teacher Responses ............................................................................... 109
Quality of Teacher Discipline ................................................................................... 125
Exam ples of Teacher Disciplinary Practices ............................................................. 128
Teacher and Participant Interactions ................................................................................ 141
Teacher-to-Participant Interaction ............................................................................. 141
Participant-to-Teacher Interaction ............................................................................. 143
Participant-to-Peer Interaction ................................................................................... 145
Research Question 7: Students' Sense of School Affiliation ..................................... 146
S u m m ary ......................................................................................... ................................. 15 2

5. SUM M ARY AND CON CLU SION S ..................................................................... 155
F in d in g s ........................................................................................................................... 1 5 6
Student Achievem ent .................................................. ................... .............. 156
Attendance ................................................................................................................ 156








D iscipline Referrals .................................................................................................. 156
Teachers' Efficacy Beliefs ........................................................................................ 157
Student Beliefs of Academ ic Efficacy .........1............................................................ 158
Parent Perceptions of Learning Environm ent ............................................................ 158
Instructional Strategies ............................................................................................... 159
Teachers' Instructional Activities and Roles ............................................................ 160
Instructional Supports and Instructional Grouping .................................................. 162
Teacher Responses to Students .............................................................................. 164
Teacher D isciplinary Actions ................................................................................... 165
Teacher Interaction with Participants ....................................................................... 166
Participant Interaction w ith Teachers ...................................................................... 167
Participant Interaction with Peers .............................................................................. 168
Participant Affiliation w ith School ............................................................................ 169
Student Instructional Activities and Roles ................................................................ 170
Conclusions ...................................................................................................................... 172
Recom m endations ........................................................................................................ 174
Policy Im plications ......................................................................................................... 176


REFERENCES.


APPENDIX A

APPENDIX B

APPENDIX C

APPENDIX D

APPENDIX E

APPENDIX F

APPENDIX G

APPENDIX H


O bservation Q uestions ...................................................................... 184

Student Survey ......................................... 186

T eacher Surv ey .............................. ................................................... 189

Student Interview Questions ................................. 191

Student A ssent Script ........................................................................ 193

Institutional Review Board Approval ............................................... 195

Parent C onsent Form s ........................................................................ 197

Teacher Consent Form ................................................ 201


BIOGRAPHICAL SKETCH ........................................................................................... 203














LIST OF TABLES


Table pae

3.1 Demographic Profile of Home Base and Comparison Site Teachers .................. 45

4.1 Mean Scores and Standard Deviations for Participants' ITBS Scores (1996-97) in
R eading and M athem atics ..................................................................................... 59

4.2 Mean Scores, Standard Deviations, and Adjusted Post-Test Means for Pre- and
Post-test in Reading and Mathematics (CBA) ...................................................... 60

4.3. ANCOVA Summary Table for CBA Reading Scores .......................................... 60

4.4 ANCOVA Summary Table for CBA Mathematics Scores ................................... 61

4.5 Means and Standard Deviations of Participants' Absences for the 1997-98 School
Y e ar .................................................................................................. ......................... 6 1

4.6 ANOVA Summary Table for Absences ............................................................... 61

4.7 Means and Standard Deviations for Discipline Referrals of Participants for the 1997-
98 School Y ear by G roup ........................................................................................... 62

4.8 ANOVA Summary Table for Participant Discipline Referrals by Group ............ 63

4.9 Mean Scores and Standard Deviations for Participants' Academic Self-Efficacy
Survey R esponses by G roup .............................................................................. 66

4.10 Inform ation-Processing M odels ............................................................................ 69

4 .1 1 So cial M o dels ................................ .................................................. ......................... 70

4.12 Personal M odels .................................................................................................... 71

4.13 Behavioral System M odels ................................................................................... 72








4.14 Percentages of Instructional Models Used (and Number of Observed Uses) by
G ro u p ......................................................................................................................... 7 4

4.15 Mean Number of Teaching Models Observed by Teacher Group ....................... 75

4.16 Percentages of Instructional Activities Observed (and Number Observed) for
T eachers by G roup .............................................................................................. 77

4.17 Percentages of Participants' Observed Instructional Activities (and Number
O bserved) by G roup ............................................................................................ 79

4.18 Percentages of Observed Instructional Roles (and Number of Observed Roles) for
T eachers by G roup .............................................................................................. 8 1

4.19 Percentages of Students' Instructional Roles (and Number Observed) by Student
G ro u p .............................. .................................................................................... ...... 8 4

4.20 Percentages of Observed Instructional Organization (and Number Observed) by
G roup ....... ................................................................................................... . . 85

4.21 Percentages of Observed Leadership of Instructional Groups (and Number
O bserved) by G roup ............................................................................................ 86

4.22 Percentages of Observed Type and Use of Instructional Supports (and Number
O bserved) by G roup ............................................................................................ 88

4.23 Percentages of Participants' Observed Academic Self-Efficacy Behaviors (and
Number Observed) by Student Group ................................................................... 106

4.24 Percentages of Observed Teacher Responses to Students (and Number Observed) by
G ro u p .................................................................................................................... .10 8

4.25 Frequency (and Total Number) of Observed Teacher Disciplinary Actions by Group
.... ............................................................................................................................. 1 2 7

4.26 Frequency (and Total Number) of Observed Teacher Responses to Participants by
G ro u p ....................................................................................................................... 14 3

4.27 Percentages of Observed Participant-to-Teacher Interactions (and Number
O bserved) by Student G roup ................................................................................. 144

4.28 Percentages of Observed Participant-to-Peer Interactions (and Number Observed) by
S tu dent G rou p .......................................................................................................... 146










4.29 Percentages of Observed School Affiliation Behaviors (and Number Observed) by
Stu den t G rou p ......................................................................................................... 14 8

4.30 Frequency of School Affiliation Interview Responses (and Number of Students
R esponding) by Student G roup .............................................................................. 150














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

DIFFERENCES IN ACADEMIC ACHIEVEMENT, SCHOOL AFFILIATION,
STUDENT AND TEACHER EFFICACY BELIEFS, PARENTS' PERCEPTIONS,
AND TEACHER INSTRUCTION BETWEEN HIGHLY MOBILE STUDENTS
PLACED IN STABLE OR TRADITIONAL SCHOOLS

By

Sandra Lee Hatch Anusavice

August, 1999

Chairman: Linda S. Behar-Horenstein
Major Department: Educational Leadership

This study explored the difference between highly mobile students who attended

Home Base school, a stable educational placement, with similarly transient students in

local elementary schools by: (a) academic achievement; (b) discipline referrals; (c)

absenteeism; (d) students' academic self-efficacy beliefs; (e) parents' satisfaction with

the school; (f) teachers' beliefs about these students' ability to be successful in school;

(g) teachers' self-efficacy beliefs about their ability to successfully teach these students;

and (h) the type and variety of instructional strategies used by teachers.

The experimental and control student groups were matched demographically

across race, gender, grade level, prior academic achievement, number of school moves, and

length in residence at the school. Sources of data included classroom observations,








participant responses to teacher, student, and parent surveys, interviews with students,

pre- and post-test scores on a Curriculum-Based Assessment (CBA) in reading and

mathematics, and attendance and disciplinary data for the 1997-98 school year.

No significant differences in mathematics gains between the two groups of

students were found. However, the students from Home Base school made significantly

greater gains in reading than did the comparison group (p=.04). Participants at Home

Base school had fewer discipline referrals and higher absenteeism than those at

comparison site schools. Students at Home Base school had a greater sense of academic

self-efficacy than did students at the comparison sites (p=.02). Parents at Home Base

school felt they were more welcome in their child's classroom (p=.004) and that they had

been consulted more frequently for ideas about how to improve the school (p=.024) than

did parents of students at the comparison sites. Teachers at the Home Base and

comparison site schools reported no difference in both their beliefs about the students'

ability to be successful in school and their ability to successfully teach these students.

Multiple classroom observations conducted over a seven-month period revealed that

Home Base teachers used a wider range of instructional strategies and teaching models,

facilitated an engaging learning environment, offered more praise to the students, had more

positive interactions with students and took a more active and supportive instructional

role than did the comparison site teachers.















CHAPTER 1
INTRODUCTION


As American society has become increasingly mobile, student achievement,

teacher workload, and school success measures have been affected (Lash & Kirkpatrick,

1990). Student mobility is underestimated and perhaps even misunderstood. Teachers'

attention to academic problems related to student mobility often seems inadequate, if not

inappropriate (Nelson, Simoni, & Adelman, 1996). Estimates of student mobility vary

between 17% and 23%; Lash and Kirkpatrick (1990) reported that 23% of children in

primary grades relocate each year,

Much of the research on student mobility suggests that the frequency of moves,

especially at the primary grades, is associated with lower academic performance and

poorer adjustment to school (Alspaugh, 1991; Blane, Pilling, & Fogelman, 1985; Ingersoll,

Scamman, & Eckerling, 1989). Of particular concern are those students who move within

the school district. Alexander, Entwistle, and Dauber (1996) reported distinct migration

patterns based on racial-ethnic background and socioeconomic status (SES). Students

moving outside the district were typically more affluent, predominately white, and their

parents were more highly educated, Students transferring within the district were more

frequently poor and/or minorities and more likely to be living in a single-parent family

headed by a high-school dropout. Schools with the highest proportion of low-income








families had significantly higher rates of student mobility (Nelson, Simoni, & Adelman,

1996). Some researchers, however, dispute the belief that mobility is a cause of poor

academic achievement (Adduci, 1990; Evans, 1996; Paredes, 1993). Whether mobility

causes low academic achievement and poor school adjustment or whether it is merely one

of several risk factors, few researchers deny that transience, particularly during the early

grades, is detrimental to learning.

Teachers most often have the responsibility for integrating new students into the

classroom (Lash & Kirkpatrick, 1990). This integration is especially difficult to

accomplish when students move outside of normal transition times (Alexander,

Entwistle, & Dauber, 1996). In a study of 21 teachers, Lash and Kirkpatrick (1990)

found that most teachers reported little or no benefit in working with children who move

frequently. The teachers believed that transient students created extra work for them,

made classroom management more difficult, and presented instructional problems. They

also believed these mobile students had more difficulty in social adjustment and in making

friends. Although Title I provides funds for migrant students, few services have been

provided for the locally transient populations. Migrant students may actually be less

disruptive to schools and teachers than the locally transient students because migrant

students move as a group at predictable times during the school year and often return to

the same school the following season (Lash & Kirkpatrick, 1990). Of much greater

concern is the transient student who moves in and out of school within a district during

the school year. These students move individually in response to the turmoil in their

lives and may change schools several times within the course of an academic year.








Nelson, Simoni, and Adelman (1996) have suggested that school districts might

address the problem of locally transient students by eliminating required changes in

school zones when a student moves within the district. A mid-sized county in a

southeastern state instituted such changes by establishing a specialized center school for

highly mobile students in primary grades. Because such pilot programs are quite new,

little research has been conducted. How effective are such centers? To date, their

effectiveness remains an area ripe for investigation.

Background for the Study

Participants in this study were selected from a segment of the county school

population which was designated as highly mobile. Characteristically, these children

changed school zones several times during the academic year. Such transitions disrupt

both the learning process and the student's ability to form social links to the school. In

January of 1997, the school district established a special center, hereafter referred to as

the Home Base School, for students in grades K-2 who met the criteria of "highly mobile"

(two or more moves resulting in changes of school zones within the last two years).

Parents of students who qualified for the program were able to choose whether to send

their children to the Home Base School or the regular school for which they were zoned.

One benefit of attending the Home Base School was that the school district agreed to

provide transportation for the students to this site regardless of where the family moved

within the school district. Thus, the Home Base School provided a stable academic

placement for its students In 1997-98, the Home Base School added a third grade class

to accommodate an increasing number of students. At present, the Home Base School has








grown to include a fourth and fifth grade class. Other grades may be added in coming

years. The purpose of this study emerged partly in response to the school district's

interest in determining whether placement at the Home Base School was making a

difference in academic achievement and school adjustment among students. Towards that

end, the purpose of this study was to determine if there were differences among (a)

students' academic achievement, their affiliation with school, and their efficacy beliefs

about their ability to be successful in school; (b) teachers' instructional behaviors and

their efficacy beliefs about their ability to successfully teach these students; and (c)

parents' satisfaction with the learning environment when compared by school sites.

Statement of the Problems

The following questions were developed to guide this investigation.

1. Is there a difference between students at the Home Base School and

comparison site schools when compared by (a) academic achievement; (b) rate of

absenteeism, and (c) number of disciplinary referrals?

2. Is there a difference between teachers at the Home Base School and comparison

site schools in their efficacy beliefs and their perceptions of highly mobile students'

ability to be successful in schools or their perceptions of their ability to successfully

teach these students?

3. Is there a difference between parents of students at the Home Base School and

comparison site schools in their perceptions of the learning environment their children

experience?








4. Is there a difference between teachers at the Home Base School and comparison

site schools in their use of instructional strategies?

5. Is there a difference between students at the Home Base School and

comparison site schools in their beliefs about their ability to be successful in school?

6. Is there a difference between the students at the Home Base School and

comparison site schools in their sense of school affiliation?

Statement of Hypotheses

The following hypotheses were proposed:

1. There will be no differences between students at the Home Base School and

comparison site schools when compared by (a) academic achievement; (b) rate of

absenteeism; and (c) number of disciplinary referrals.

2. There will no difference between teachers at the Home Base School and

comparison site schools in their efficacy beliefs and perceptions of highly mobile

students' ability to be successful in school and their ability to successfully teach these

students.

3. There will be no difference between parents of students at the Home Base

School and comparison site schools in their perceptions of the learning environment.

4. There will be no difference between teachers at the Home Base School and

comparison site schools in their use of instructional strategies.

5, There will be no difference between students at the Home Base School and

comparison site schools in their beliefs about their ability to be successful in school.








6. There will be no difference between students at the Home Base School and

comparison site schools in their sense of school affiliation.

Definition of Terms

The following definitions are provided to clarify important terms used within this

study.

1. Academic gains are the mean changes in reading and mathematics scores as

determined by calculating the difference between the beginning of the school year to the

end of the school year scores on pre- and post-tests using the reading and mathematics

portions of the Curriculum-Based Assessment.

2. Comparison group is a group of second and third grade students enrolled in area

elementary schools (Cooke Elementary and Stanton Elementary) who met the

requirements for high mobility (two or more changes of school zones within two years)

and who had also been in residence at their current school for the spring of 1997.

3. Curriculum-Based Assessment refers to an assessment test developed by

University of Florida researchers that is aligned to a school district's curriculum.

4. Discipline referrals are the official referral forms for disciplinary infractions.

Records of these forms are maintained by the school and include the student's name, the

type of infraction, the date of infraction, and the punishment given.

5. Experimental group refers to the group of second and third grade students who

were enrolled at the Home Base School in the spring of 1997. This group (26 total)

comprised those students presumed to have a "stable educational placement" beginning in

the 1997-98 school year.








6. Grade level is the level to which the student is assigned. Students selected for

this study were enrolled in grades 2 and 3.

7. High-risk students are students who bring to school a variety of life conditions

which are associated with an increased risk of school failure.

8. Home Base School is a special center where highly mobile students may enroll.

As long as students remain within the district, they will be transported to this school,

thus eliminating the need to change schools each time the family moves to another school

zone.

9. Instructional strategies are the teacher's observable methods of presenting

material to students.

10. Iowa Test of Basic Skills (ITBS) is a standardized measure of student

academic achievement.

11. Learning environment is the perception of school atmosphere or learning

milieu experienced by teachers, students, and parents.

12. Length of time in residence refers to the length of time in which the student

has been enrolled at his or her current school.

13. Models of teaching (teaching models) are conceptual frameworks that teachers

use to plan instruction and encourage specific educational outcomes.

14. Parents' satisfaction with school refers to the parents' self-reported beliefs

about their feelings of contentment with the learning environment of their child's school.

15. Prior academic achievement is the academic progress of students, as measured

by scores on the Iowa Test of Basic Skills, prior to the beginning of the study.









16. Rate of absenteeism refers to the number of days a student is absent in a given

school year.

17. School system is the local school district.

18. Student achievement is the academic achievement of students in reading and

mathematics as measured by the reading and mathematics portion of the Curriculum-

Based Assessment.

19. Student mobility is the movement of students from school to school within or

between school districts.

20. Students' academic self-efficacy is the extent to which students believe they

can be successful at academic endeavors.

21. Teacher attitudes about teaching highly mobile students are the beliefs teachers

hold about the value and rewards of working with students who change schools

frequently.

22. Teacher efficacy is the teacher's self-reported beliefs about his or her ability to

effectively teach students.

23. Teachers' beliefs about success potential of highly mobile students refer to the

beliefs teachers hold about the ability of highly mobile students to be successful in school.

These beliefs are related to the amount of work teachers are willing to invest in highly

mobile students.

24. Title I is legislation which provides additional funding for schools with a high

percentage of impoverished students as judged by qualification for free and reduced lunch








subsidies. Students are selected for services partially based upon scores in reading and

mathematics which fall beneath the 35th percentile on nationally normed measures.

25, Traditional elementary schools are the area schools for which each student is

zoned. In this district, these schools typically serve 800-1000 students with a wide range

of economic and academic backgrounds.

Significance of the Study

The impact of student mobility on school performance has been difficult to

determine, but many studies have indicated that transience is at least a complicating factor

in the lives of students already at risk, Theoretically, this study addressed the question

of whether or not controlling the mobility factor allowed students otherwise at risk for

school failure to realize academic gains. The results may also help identify the type of

instructional behaviors that support successful academic outcomes, enhance healthy

emotional development, and promote positive social interactions for students who are at

risk due to mobility. Practically, this study provided information about the success of

the Home Base School in meeting its mission and may help to identify what type of

instruction facilitates positive student outcomes. A thorough search of the literature

suggests that presently the Home Base School may be the only program of its type in the

country. The results provide important data about the impact of a stable educational

placement on highly mobile students who are at risk.

This study also addressed questions about whether teachers who choose to work

with highly transient students have a greater sense of self-efficacy, employ a wider array

of instructional strategies, and hold more positive attitudes about transient students, To








what extent, if any, are teacher skills different in the Home Base School compared to

traditional elementary schools? If teachers in the Home Base School do have a different

skill set and different attitudes and beliefs about teaching highly mobile children, what

practical wisdom might accrue? Perhaps some directions can be identified for teacher in-

service preparation that will help elementary school teachers become more adept at

dealing with the particular problems of locally transient students.

Limitations

The following limitations for this study were noted.

1. The study involved a small number of students; 10 students at the Home Base

School and 9 students at the comparison site schools completed the study. However,

because the Home Base School was so recently established, only 26 students in second

and third grade met the initial requirement for length in residency at the school.

Therefore, the sample group represents a significant percentage of the population under

investigation. Participants at the Home Base School were randomly selected from the

experimental population. Students in the comparison group were matched as closely as

possible to the experimental group to provide a stratified sample. All of the locally

transient students shared many characteristics. The population from which all the

participants were drawn qualified for subsidized lunch (free or reduced lunch prices),

most had ITBS reading and mathematics scores below the 50th percentile in reading

and/or mathematics, and the majority of students in this group were minorities.

2. The use of student interns and long-term substitutes in district classrooms was

another limitation. These workers do not represent "regular" or "normative" classroom





II


teaching. Many more interns or substitutes were used in the comparison site classrooms

than in the Home Base School. Obviously, these teachers are not of the same ilk as the

experienced, professional teachers. The greater use of interns and substitute teachers at

the comparison site schools may have influenced aspects of the learning environment.















CHAPTER 2
REVIEW OF LITERATURE


The purpose of this study was to compare highly mobile second and third

grade students who selected a stable placement at the Home Base School with highly

mobile students who were attending their zoned schools. The students were compared

by (a) academic achievement; (b) rate of absenteeism; (c) number of discipline referrals;

(d) students' sense of academic self-efficacy and (e) their sense of school affiliation. The

parents of students at the Home Base School were compared with parents of students at

their zoned schools on a measure of school satisfaction. The teachers at the Home Base

School were compared to teachers at the comparison sites on (a) the types and varieties

of instructional strategies used; (b) teachers' attitudes towards working with highly

mobile students, and (c) teachers' sense of efficacy and their beliefs about the ability of

highly mobile students to be successful in school. The review of the literature will

examine four major issues relevant to this study: (a) student mobility, (b) teacher

efficacy; (c) student efficacy; and (d) instructional strategies,

Student Mobility

In this section, following an overview of research on student mobility, its impact

on student achievement and its impact on school systems will be discussed.








Research on Student Mobility

The increasing mobility of students in the United States has raised important

questions about the effect of mobility on student achievement. Many of the students

who are highly mobile move short distances within the local school district and can be

designated "locally transient." The following discussion will explore factors that lead to

local transience and the impact of mobility on student achievement, on school systems,

and on teachers.

Mobility occurs both within and external to a school district. The highly mobile

student shares characteristics with both migrant students (Lash & Kirkpatrick, 1990) and

homeless students (Stronge, 1993) because of the precarious housing or uncertain

employment which often contribute to frequent moves. However, while both migrant and

homeless populations have programs in place to assist them, the highly mobile student

often does not. For purposes of this study, the student who has changed school zones

within the local district two or more times within the last two years of schooling has been

designated as "locally transient." However, the mobility for participants in this study

ranged from a minimum of two school changes to a maximum of eight school changes

within two years. Ten of the participants had changed schools three or more times within

the last two years.

Students who move about within a given school district have a very different

demographic profile than students who move in and out of school districts (Alexander,

Entwistle, & Dauber, 1996). The locally transient student is often disadvantaged by

poverty, racial minority, and single-parent or no-parent families. These families are often








impacted by sociological factors such as lack of affordable housing, unemployment or

underemployment, substance abuse, divorce, and domestic violence (Stronge, 1993).

Thus the locally transient student is likely to come to school with a variety of cultural

and academic disadvantages that place him or her at risk of failure.

Impact on Student Achievement

Studies of the effect of school mobility on student achievement have yielded

confusing and contradictory results. In a study of 10th-grade students, Adduci (1990)

reported that mobility had no impact on student achievement. She measured mobility by

looking at the number of uninterrupted years in the district, the number of moves, and the

distance of the moves. She also considered types of transfers: no transfers, within

district transfers, out of district transfers, and transfers into the district from outside the

United States. Achievement was measured by averaging the student scores on the

writing, mathematics, and reading portions of the state-mandated High School Placement

Test (HSPT) while controlling for socioeconomic status (SES), language, and family

structure. Of the three variables studied--mobility, family structure, and language spoken

in the home--Adduci found only student language to be related to school achievement.

Students whose families were non-English speaking scored lower on the measure of

academic achievement than students who were native English speakers.

Straits (1987) determined that the detrimental effects of mobility were related to

parents' educational level. He suggested that mobility seemed to adversely affect the

school achievement of students with less-educated parents. Mobile students with well-

educated parents seemed to have support systems outside of the school that ameliorated








the possible harmful effects of mobility. Blane, Pilling, and Fogelman (1985) asserted

that the effects of mobility could not be separated from other risk factors such as race,

socioeconomic status, and parental education level. The authors used multivariate

analytical techniques to measure the effect of school mobility on student achievement and

behavior. Their results indicated that while the achievement scores for the more mobile

students were lower, the differences in performance existed prior to the move and were

caused by factors other than mobility.

While acknowledging the validity of the Blane et al. study (1985), Ingersoll,

Scamman, and Eckerling (1988) found that mobility was detrimental to student

achievement even when socioeconomic factors were controlled. They reported that

achievement levels of the more mobile student populations were consistently lower than

the achievement levels for more stable students. The impact of mobility on achievement

was greatest in the early grades; the higher the grade level in school, the less detrimental

mobility seemed to be for student achievement.

Benson, Haycraft, Steyaert, and Weigel (1979) found a negative relationship

between mobility and both student achievement and school adjustment. The authors

collected data on more than 1,000 sixth-grade students in Larimer County, Colorado.

Four major variables were considered- socioeconomic status, mobility, achievement, and

adjustment. Mobility was measured by the number of schools each child had attended.

Achievement was measured by the percentile ranking on the reading subtest of the

Stanford Achievement Test. Adjustment was determined by using the Classroom

Behavior Inventory which rated both peer adjustment and general adjustment.








Correlational data indicated that mobility was negatively related to all of the variables.

However, the authors cautioned that while mobility was a factor in achievement and

adjustment, many other factors may contribute to students' difficulty in school. For

example, they found a correlation between mobility and low socioeconomic status which

could also impact student achievement. Bolinger and Gilman (1997) also found a

significant correlation between student mobility and low test scores in language, although

the correlation was not significant between student mobility and the entire battery of

achievement test scores.

Reynolds (1991) hypothesized that mobility may be particularly damaging to

low-income children already academically at risk. Indeed, mobility is often a consequence

of poverty. Reynolds selected 1,539 African-American and Hispanic students from

government-funded kindergarten programs and followed them for three years, through the

end of their second-grade class. Student readiness attributes were collected at the

beginning of the study, and student progress was assessed by test scores and teacher

reports at predetermined points during the study. Teachers also reported their

perceptions of each student's motivation and the level of parental involvement. Reynolds

demonstrated that mobility negatively impacted both reading and mathematics

achievement. However, he also cautioned that mobility is only one factor in a complex

process. Mobility may actually be an indicator of other unmeasured variables such as

family disruptions, economic hardship, or broken marriages which might influence student

achievement.








A study conducted by the Cleveland Public Schools (1989) compared the most

stable students (those who remained in the same school for a complete school year) with

the least stable students (those who moved at least once during the school year).

Students were compared on the variables of attendance and tardiness, suspension,

withdrawal or dropping out, promotion, and achievement in reading and mathematics as

measured by normed and criterion referenced tests. Results indicated that the mobile

students tended to be from lower income families, have higher rates of absenteeism and

suspensions from school, tended to be retained or drop out of school, and have lower

achievement scores in reading and mathematics. Ligon and Paredes (1992) compared four

groups of elementary students in Austin, Texas. The students were classified as stable

over time, moved during the current year, moved during the previous year, or mobile over

time. They found that achievement differed among groups at all grade levels, with the

most stable group always having the highest achievement.

In a legislative report to U.S. Representative Mary Kaptur (General Accounting

Office, 1994), aides indicated that children from low-income families or inner-city schools

had higher mobility rates than suburban, middle-class children. While 17% of all third

graders had changed schools frequently (three or more times since first grade), they

observed that more than 30% of the children who resided with families that earned less

than $10,000 had moved frequently. In contrast, only 8% of children in families earning

$50,000 or more per year had changed schools three or more times by third grade

(Mehana & Reynolds, 1995). Twenty-five percent of inner city third graders had

changed schools frequently, compared to 15% of suburban third graders. Students who








qualified as Limited English Proficient (LEP) also had higher rates of mobility, and about

34% of third grade students classified as LEP had changed schools frequently. Among

these highly mobile third graders, 41% were below grade level in reading, compared to

26% of stable third grade students. Thirty-three percent of the highly mobile students

were below grade level in mathematics, compared to 17% of the stable third graders

(General Accounting Office, 1994).

Kerbow (1996) noted that earlier studies of mobility failed to concentrate only on

intra-district mobility. Students who move within a district often manifest very different

demographic characteristics than students who move outside of a district (Alexander,

Entwistle, & Dauber, 1996). Most studies also failed to consider the impact of mobility

on student achievement over a period of time. Kerbow hypothesized that mobility had

cumulative effects on achievement. That is, the more frequently a student changed

schools, the more likely he or she would be to fall behind academically. As he followed

students who moved repeatedly during their elementary school years, Kerbow found that

generally the mobile students had a lower achievement growth than more stable students

and that the gap in achievement widened as students moved more frequently. The highly

mobile students fell farther and farther behind their stable counterparts over time.

Kerbow, too, was reluctant to attribute low achievement only to mobility but concluded

that mobility was a factor which combined with other impediments, such as family

structure and socioeconomic level, to interfere with student success. For example, he

reported that 75% of the frequent movers were African-American. Seventy-eight percent

of highly mobile students qualified for free/reduced lunch subsidies. Although only 22%








of the highly mobile students lived in two-parent families, nearly 25% of them lived with

neither parent. Thus, highly mobile students are often disadvantaged by a lack of stable

family structure and lower socioeconomic status. These factors also imperil their school

performance.

Alexander, Entwistle, and Dauber (1996) observed that frequency of moves was

associated with lower test scores and poorer grades. Students who have moved

frequently were also at greater risk for being retained a grade. While other factors such as

previous academic performance, race/ethnicity, and SES could have greater influence on

student achievement than mobility, Alexander et al. concluded that mobility was at least a

complicating factor in a child's adjustment to school, particularly in the early grades.

Mehana and Reynolds (1995) examined sixth grade children in the Chicago Public

Schools who had participated in Reynold's earlier longitudinal study in 1986. By the

sixth grade, 81% of the children in their original study were still enrolled in the Chicago

Public Schools. Mehana and Reynolds wanted to determine the frequency of moves

among low-income children between first and fifth grade. They also searched for

predictors of school mobility and tried to determine the impact of school mobility on

achievement if other factors such as family background and previous academic

achievement were discounted. They found that income (as determined by subsidized

lunch eligibility) and parent education were predictors of mobility. In other words,

poorer children whose parents were less educated moved more frequently than other

children. Mehana and Reynolds observed that a decline in reading achievement was

correlated with mobility. No similar declines were observed for mathematics








achievement. They concluded that "poverty [was] a significant predictor of mobility and

frequent mobility a significant predictor of lower reading achievement in Grade 6" (p. 15).

Impact on School Systems

Estimates of student mobility vary, but studies generally report that between 17%

and 23% of school children in the United States move each year (Ingersoll, Scamman, &

Eckerling, 1988; Lash & Kirkpatrick, 1990). Most of these students move within the

same school district which makes planning and evaluation very difficult for school

administrators. Schools with a large transient population may be unfairly penalized when

measuring school improvement (ERIC Clearinghouse on Urban Education, 1991; Ingersoll,

Scamman, & Eckerling, 1988, Kerbow, 1996; Mao, Whitsett, & Mellor, 1997).

In his study of Chicago schools, Kerbow (1996) examined the movement histories

of sixth grade students during the spring of 1994. Mobility was high; over 36% of the

students changed schools at least once within a two-year period. Of greatest interest was

the discovery that mobility is largely an internal problem. Eighty-seven percent of the

students who moved transferred from one Chicago school to another. In fact, most of the

mobility occurred within clusters of schools, similar in SES and racial makeup as well as

in achievement rankings, that seemed to exchange students frequently. Mobility within a

district is not just a product of factors beyond school control such as family instability

and economic hardship. Student mobility rate may also be affected by policies and

procedures related to zoning and school transfers within the district.

Mobility not only affects the learning of the transient students but may also

impact the instructional strategies, curriculum planning, and the learning of stable students








within the school. Lash and Kirkpatrick (1990) observed that the primary responsibility

for integrating new students into the school fell to the classroom teacher. Teachers

reported they rarely knew in advance when new students would be added to their classes.

Student records generally arrived several weeks after the students, so teachers had to rely

on informal interviews or testing to determine adequate placement for transfer students.

Almost all teachers felt that working with highly transient populations created special

problems and extra work for them. Frequently, these problems were related to

instruction. Teachers found it necessary to review, re-teach, or backtrack to bring new

students up to speed, thus slowing down the progress of other students.

Mao, Whitsett, and Mellor (1997) found that student mobility had a negative

effect not only on individual student achievement but also on school and district

accountability ratings. Thus, it appears that high mobility rates may harm the learning of

other students in the classroom and affect the measures of school success for a school or

district. However, another study by Heywood, Thomas, and White (1997) failed to

show any correlation between the mobility of students in a classroom and the

achievement of stable students within the class.

Kerbow (1996) examined school enrollment across an entire academic year in an

attempt to understand how mobility affected classroom instruction. He found that in

schools with highly mobile populations, the classroom population was in flux most of the

year. Chicago teachers employed in unstable schools reported lower levels of

collaboration with peers, less focus on student learning, and less openness to instructional

innovation, Seventy-seven percent of teachers reported that they reviewed material due








to the addition of new students during the school year. Kerbow demonstrated that this

emphasis on review and more routine instruction had the effect of decreasing the

instructional pace for all students. Furthermore, a slower pace of curriculum was

cumulative; by fourth grade, the curriculum pacing at highly mobile schools lagged almost

an entire grade level behind that of less mobile schools. The impact of mobility on

student achievement may affect not only the students who transfer but also the

instruction offered to the stable students within the system.

School reform movements have failed to consider mobility as a factor in measuring

school performance. Efforts at school improvement assume that schools will have a

stable cohort of students over time, when in fact, the students who are tested may not be

the students who were taught. Kerbow (1996) discovered that after four years, in the

more mobile Chicago elementary schools, less than 30% of the student body will have

attended since the beginning of first grade. For this reason, program evaluation becomes

much less meaningful in highly mobile schools.

Ligon and Paredes (1992) also explored how mobility impacts school programs.

Most instructional programs are designed for a student population that remains stable for

at least a given school year and ideally for the entire grade span covered by a school.

School districts have not devised systems of curriculum or measurements to deal with a

segment of the school population which is increasingly transient. Ligon and Paredes

suggested that an index of student stability would be useful in evaluating school

accountability measures. School progress could be evaluated by looking only at the








students who had been in place long enough to have benefited from the instructional

programs.

Ligon and Paredes (1992) surveyed state departments of education in all 50 states

as well as extra-state jurisdictions, Department of Defense Dependents Schools, and the

District of Columbia to determine how student mobility statistics were calculated. The

results demonstrated that school districts calculated mobility statistics using available

data rather than appropriate data. The methods of calculation differed widely. Ligon and

Paredes organized the different mobility formulas they encountered into one of the

following four categories: (a) stability indices which describe the proportion of students

who are enrolled for the entire school year; (b) turbulence indices which describe the total

number of changes/moves by students compared to the total number of students enrolled;

(c) mobility indices which describe the proportion of students who move and change

school assignment within a school year; and (d) mobility counts which is the actual

number of student school changes within a school year. Such variations in the manner of

calculating mobility data make national comparisons of mobility rates impossible. Ligon

and Paredes suggested that several standardized methods of calculation be introduced,

based on the information needed or questions asked. For example, to measure the impact

of school programs, an index of student stability could be used. An index of student

mobility and a mobility impact index could be used to assess the true mobility rates and

indicate how mobility affected individual student achievement. One of the demonstrable

difficulties with previous studies is that researchers have used widely varying measures to

determine mobility and to select their experimental populations. If researchers are to








form a clearer understanding of the link between mobility and learning, some consensus

much be reached on how to measure mobility. In addition, mobility must be taken into

account when determining school progress and accountability.

While the literature has not established a definite causal link between high mobility

and lower academic achievement, studies indicate a correlation between mobility and

achievement. Until now, no studies have explored how the absence of mobility could

improve student achievement, regardless of socioeconomic and cultural factors. The

present study makes an important and unique contribution to the field by examining the

effectiveness of a stable school placement for locally transient students.

Teacher Efficacy

Theories about the role of personal efficacy in cognitive functioning have been

expanded to include the effects of teacher efficacy on student achievement and teachers'

instructional and management behaviors. The following section will examine studies

which discuss the relationship between teacher efficacy beliefs and (a) teachers' behavior,

(b) student achievement, and (c) teachers' management and instructional styles.

Teacher Efficacy Beliefs and Teacher Behavior

Teacher efficacy measures the teachers' beliefs about the effectiveness of their

efforts to improve student achievement (Ross, 1994). According to Ross, the greatest

contributions to teacher efficacy theories were based on Rotter's work on locus of

control, Weiner's attribution categories, and Bandura's self-efficacy studies. Bandura

(1989) theorized that self-reflection enabled people to evaluate and change their own

behavior. A part of these self-evaluations included what he termed self-efficacy beliefs,








one's beliefs in one's ability to achieve a particular outcome. One's perception of self-

efficacy is a mediation between thought and action. As Bandura (1989) claimed, having

skills and knowledge does not necessarily guarantee that one can use them successfully in

a variety of demanding and unpredictable circumstances. Ashton and Webb (1986)

defined teacher efficacy as "teachers' situation-specific expectation that they can help

students learn" (p.3). Efficacy beliefs influence activities individuals choose to do as well

as the effort, persistence, and resilience they demonstrate when pursuing the task. The

higher one's sense of self-efficacy, the more likely one is to choose a task and pursue it

with effort, persistence, and perseverance (Pajares, 1996). Teacher efficacy has two

dimensions: general teaching efficacy, an individual's belief that teaching can influence

student learning; and a sense of personal teaching efficacy, an individual's beliefs in his or

her own ability to influence the learning of students in his or her classroom (Ashton &

Webb, 1986). Self-efficacy beliefs are correlated with self-concept, attributions,

motivation, and academic achievement. Although self-efficacy beliefs are most highly

correlated with achievement when the constructs are particular to certain skills, even

generalized self-efficacy perceptions are accurate predictors of general academic

achievement (Bandura, 1989; Pajares, 1996).

When Bandura's theories are applied to teaching, efficacy beliefs are thought to

influence teacher behavior in four distinct processes. A higher sense of efficacy may help

teachers set higher goals for themselves and their students, be willing to work harder to

achieve those goals and persist despite obstacles or initial lack of success. Teachers with

high efficacy beliefs may also be willing to accept a greater responsibility for student








achievement rather than attributing student outcomes to factors beyond teacher control.

High efficacy beliefs contribute to greater stress-coping mechanisms which help teachers

manage day-to-day pressures and achieve greater job satisfaction. Since efficacy beliefs

have great influence on choices of activities, teachers with higher efficacy beliefs may

demonstrate a greater commitment to teaching (Ross, 1994). Thus the premise of teacher

efficacy is that a teacher's beliefs about his or her ability to teach will impact student

achievement. A greater sense of efficacy should promote student learning.

Fletcher (1990) demonstrated a link between teachers' efficacy and their

perceptions about students' ability to learn. The teachers with higher efficacy beliefs

were more likely to view student ability as an incremental trait that can be increased by

effective instructional practices. Fletcher also found that teacher efficacy beliefs were

influenced by teacher involvement in organizational decisions, the behavior and attitude of

students in the school, and the amount of control teachers have over classroom

instruction. Ashton and Webb (1986) indicated that teachers' efficacy beliefs are

impacted by their assumptions about how capable their students are of learning the

material presented. Raudenbush, Rowan, and Cheong (1992) distinguished between

teacher efficacy beliefs and outcome expectations. While the two are related, teachers

could have high efficacy beliefs and still have lower outcome expectations based on

external factors such as home environment or ability level that they perceive to affect

student performance.

Classroom characteristics may have an effect on teacher efficacy. Elementary

school teachers generally indicated higher efficacy levels than high school or middle school








teachers. Within high schools, teacher efficacy tended to increase as grade level increased.

However, this correlation was indicated in only one study (Raudenbush, Rowan, &

Cheong, 1992), so these findings are limited by a lack of corroboration. Raudenbush et al.

also demonstrated that high school teachers' efficacy ratings increased as the ability

grouping level of the students improved and as a function of the teacher's sense of

preparedness to teach the course. Increasing the number of low achieving students in a

class decreased a teacher's sense of efficacy (Ashton, Webb, & Doda, 1983). Although

Hoover-Dempsey, Bassler, and Brissie (1992) found no correlation between school SES

and teacher efficacy, Bandura (1993) reported that a combination of low SES, high

student turnover, and high absenteeism created poor achievement patterns which lessened

teachers' efficacy beliefs.

Teachers with high efficacy beliefs are more likely to use a variety of instructional

strategies including active learning (Ross, 1994). However, no assumptions about

causality are being made here. Researchers cannot determine if a greater sense of efficacy

leads teachers to use more demanding instructional techniques or if the mastery of

difficult but effective techniques increases a teacher's sense of efficacy. Teachers with

higher efficacy scores had a more positive attitude towards implementing new

instructional strategies. Teacher efficacy beliefs have also been correlated with a

willingness to take responsibility for the learning of special needs students and to meet

difficult instructional challenges (Ross, 1994). Teachers with low self-efficacy exhibited a

weaker commitment to teaching, spent less time on academic pursuits, and were more

likely to become victims of burnout (Bandura, 1993). Hoover-Dempsey et al. (1992)








corroborated earlier findings that indicated higher efficacy teachers reported greater

parental involvement in their classroom activities. It is unclear whether higher rates of

parental involvement help teachers develop a greater sense of efficacy or if higher efficacy

teachers may be more open to and solicitous of parental involvement. The researchers

suggested that an interactive relationship exists which allows both parents and teachers to

approach working together less defensively.

High-efficacy teachers were more likely to expect academic achievement, use

strategies that minimized conflict, and maintain a warm, accepting classroom atmosphere

(Ashton, Webb, & Doda, 1983). Furthermore, the students of higher efficacy teachers

had higher achievement than students of teachers with low efficacy beliefs (Ashton,

Webb, & Doda, 1983; Ross, 1994). Ashton and Webb (1986) also found that teachers'

instructional styles were related to their beliefs about general teaching efficacy. High

efficacy teachers were more likely to hold students responsible for learning and to foster

student initiative by encouraging them to attempt challenging problems and questions.

However, teacher efficacy beliefs may not be a stable measure but may vary according to

the level of class teachers are assigned, their perceptions of their own preparation to teach

the course, the age of the student, and organizational factors with the school

(Raudenbush, Rowan, & Cheong, 1992). The study by Raudenbush et al. cautioned

against the division of teachers into high and low efficacy categories because efficacy

beliefs may change situationally.











Teacher Efficacy Beliefs and Student Achievement

Teacher efficacy has an impact on student achievement. Ross (1992)

demonstrated that student achievement was higher in classes led by teachers with high

personal efficacy scores than in classes led by teachers with low scores. Raudenbush et

al. (1992) showed that teachers with higher efficacy beliefs are more likely to implement

demanding but powerful teaching strategies. High efficacy teachers tended to select

instructional strategies based on applicability to fostering student development whereas

low efficacy teachers tended to select strategies based on their potential to reduce noise

and confusion. Woolfolk and Hoy (1990) reported that efficacy was related to classroom

management styles. Teachers with higher efficacy ratings preferred student autonomy to

custodial control and created warmer, more secure classroom environments (Ashton,

Webb, & Doda, 1983; Woolfolk & Hoy, 1990). Higher efficacy teachers tended to have

more positive feelings and higher expectations for low achieving students, whereas low

efficacy teachers tended to ignore low ability students in favor of high achievers (Ashton,

Webb, & Doda, 1983). Dembo and Gibson (1985) reported that high efficacy teachers

were more likely to maintain student engagement with instruction over longer periods of

time. Higher efficacy teachers also tended to have a stronger academic focus, spend more

time in direct instruction, devote more time to large group instruction, and spend more

time monitoring student performance (Gibson & Dembo, 1984). Low efficacy teachers

were more likely to criticize students for wrong answers while high efficacy teachers were

more effective in using questioning techniques for leading students to correct responses.








These differences between high and low efficacy teachers might be explained by

Bandura's (1989) arguments that people who have low efficacy beliefs tend to lack faith

in their coping mechanisms and view their environment as potentially threatening.

Ashton and Webb (1986) reported that high efficacy and low efficacy teachers

differed particularly in their treatment of low-achieving students. These differences might

be caused by low achieving students having a more extrinsic motivation or lacking

academic confidence and therefore being more susceptible to teacher beliefs about their

ability (Midgley, Feldlaufer, & Eccles, 1989). Low efficacy teachers might also be more

likely to tell low-achieving students about their limited expectations of student

achievement. Teacher efficacy may have a greater impact on low-achieving students than

on high achievers because lower ability students may be more influenced by teacher

expectations and because student failure often spurs high efficacy teachers to greater

effort (Midgley, Feldlaufer, & Eccles, 1989; Ross, 1994).

Ashton and Webb (1986) found that the impact of teacher efficacy on student

achievement may be subject-matter specific. Their study indicated a higher correlation

between teacher efficacy rating and student achievement in mathematics than in

communications. However, teachers' efficacy beliefs were predictive of student

achievement in both mathematics and language, with higher efficacy teachers producing

greater student achievement. Raudenbush et al. (1992) also found teacher efficacy to be

dependent on subject matter taught. Ross (1992) found that a sense of personal teaching

efficacy had greater impact on student achievement than a sense of general teaching

efficacy. Midgley et al. (1989) reported that students' attitudes about their mathematics








efficacy was impacted positively or negatively over two years, based on whether they

had a high efficacy or low efficacy teacher. Students with low efficacy teachers became

more negative in their perceptions of their own mathematical efficacy and perceived

mathematics to be more difficult as the years progressed, while students with high

efficacy teachers became more confident in their ability to do well and perceived

mathematics to become less difficult over time. Midgley et al. (1989) suggested that the

impact of teacher efficacy on student achievement is subtle and pervasive over time.

Anderson, Greene, and Loewen (1988) found that teachers generally have a higher sense

of personal efficacy and a lower sense of the efficacy of teachers in general to influence

educational outcomes.

Teacher Efficacy Beliefs, Management, and Instructional Styles

Teacher efficacy is related to classroom management styles, organizational beliefs,

convictions about the nature of ability, and general educational philosophy. Identifying

high efficacy and low efficacy teachers is not a simple process, nor is definition of high or

low efficacy standardized across research studies (Woolfolk & Hoy, 1990). However,

many studies suggest that teacher efficacy may have a significant impact on student

efficacy and on student achievement (Ashton, Webb, & Doda, 1983; Ashton & Webb,

1986; Dembo & Gibson, 1985; Fletcher, 1990; Gibson & Dembo, 1984; Midgley,

Feldlaufer, & Eccles, 1989; Ross, 1992; Ross, 1994; Woolfolk & Hoy, 1990)..

Often teachers expect highly mobile students to do poorly. Two studies

(Newman, 1988; Sewell, Rodriguez, Chandler-Goddard, & Angelettie-Wallace, 1982)

indicated that students who entered the classroom mid-year faced a negative prejudgment








by teachers. Teachers expected lower academic performance, poorer attendance, and a

more negative attitude from these students. Lash and Kirkpatrick (1990) found that

students who entered school after the beginning of the school year were less likely to

remain until the end of the year.

The locally transient student does not move in groups at predictable times as

migrant students do, but rather in individualistic patterns. Teachers find this

individualistic mobility pattern to be the most disruptive. Teachers in urban schools

where individualistic mobility is the norm reported the least benefit from and satisfaction

in working with highly mobile students (Lash & Kirkpatrick, 1990). Successful

adaptation into a new school requires a student to find acceptance among new peers,

conform to the academic and behavioral standards, and be accepted by the teacher as a

class member (Newman, 1988). If teachers have negative attitudes about working with

mobile children and low efficacy beliefs about their ability to teach these students, the

likelihood of a child adjusting well to a classroom is diminished.

Student Efficacy

Student efficacy is thought to be a predictor of academic success. In the following

section, a review of studies that have explored the relationship between student efficacy

and student achievement and studies that have tried to determine ways to build positive

efficacy beliefs for students is presented.

"Agency" describes one's conviction in one's ability to manage behavior,

understand events, and exercise personal control and competence. Weiner (1976)

theorized that agency could be viewed as external, related to causes outside one's control,








or internal, related to causes within one's power. At-risk students more often exhibit an

external locus of agency, blaming factors outside their control for their failures (Higgs,

1995). Weiner (1976) suggested that how students attribute the cause of their successes

and failures may be a more accurate predictor of success and failure than ability. Efficacy

beliefs are defined as the expectation that one can perform to a certain level of

accomplishment. Outcome beliefs are the predictions of the results of one's performance.

Perceived control, efficacy, and outcome expectations are all related constructs, but the

present study attended to students' self-efficacy beliefs as they related to academic

performance.

Student achievement has been correlated with students' efficacy beliefs about their

ability to do well in school (Anderson, Greene, & Loewen, 1988; Bandura, Barbaranelli,

Caprara, & Pastorelli, 1996). Furthermore, teachers' efficacy beliefs were correlated with

students' efficacy beliefs, suggesting that teachers who believe they can help students

learn foster students' confidence in their own abilities. Researchers have not been able to

determine whether the efficacy beliefs cause high achievement, result from high

achievement, or if achievement and belief reinforce each other. Certainly students cannot

succeed simply by being overconfident in skills they do not possess. However, high self-

efficacy beliefs in academic pursuits can give children an advantage in selecting challenging

goals and motivating them to persevere when difficulties arise as well as impacting their

performance (Ames, 1999; Bandura, 1993; Pajares, 1996), At the same time, performance

impacts self-efficacy beliefs, so that students who progress toward their learning goals

also build their sense of efficacy (Bandura, 1993; Schunk, 1989). Students' beliefs about








their ability to master academic challenges predict their academic achievement; positive

efficacy beliefs increase motivation and improve achievement (Bandura, 1993; Schunk,

1991).

Ames (1999) reported that "young children tend to have an optimistic view of

their ability, high expectations for success, and a sort of resilience after failure" (p. 138).

Older children tend to allow other people's perceptions of their ability to help shape their

perceptions of their efficacy. Schunk (1989) claimed that positive teacher feedback can

raise students' sense of efficacy. Teachers who provide effective learning strategies and

persuade students that they have the ability to use the strategies effectively promote

learning (Paris & Byrne, 1986; Schunk, 1991). Learning environments that characterize

ability as an acquirable rather than inherent quality, emphasize cooperation rather than

competition, and help students assess their own progress over time rather than comparing

them to others all help build a greater sense of student self-efficacy and encourage

academic achievement (Bandura, 1993).

Schunk (1981) found that self-efficacy beliefs were predictive of arithmetic

performance in elementary school children. Furthermore, efficacy beliefs could be

enhanced through providing problem-solving techniques, corrective feedback, and

opportunities for self-corrective feedback. Ames (1999) and Schunk (1989) both

cautioned that children make a shift from connecting effort to success to attributing

success to ability. Students who lack a strong sense of academic self-efficacy

demonstrate achievement anxiety which is often exacerbated by failure (Bandura, 1993).

Thus, success tends to breed success while failure breeds failure. However, Schunk








(1981) has shown that "effort attributional feedback," or telling students that they

possess the ability to do a task if they persist and work hard, can improve efficacy

beliefs, particularly in young children. In addition, Schunk and Cox (1986) demonstrated

that teaching students certain strategies such as "continuous verbalization" (talking their

way through the steps to solve a problem) promoted achievement and increased self-

efficacy perceptions. Rewards also impact students' efficacy beliefs. Performance-based

awards, reflecting progress toward a goal, are more effective in building efficacy than

participation-based awards (Schunk, 1989).

Student self-efficacy is an important component of academic achievement.

Studies have indicated that perceived self-efficacy can predict students' academic

performance. Furthermore, students' self-efficacy is altered by the efficacy beliefs of

their teachers, the kind of feedback they are given, and the learning strategies they are

taught.

Instructional Strategies

The purpose of teaching is not only to help students master a body of knowledge

but to help them become more masterful learners. As students progress in school, the

instructional strategies teachers use should enable students to approach future learning

tasks more successfully and more independently (Joyce & Weil, 1996). Effective

teachers create a variety of learning environments in which children can acquire content

and develop successful study methods. Joyce and Well (1996) have studied these

learning environments and have classified one aspect of instructional strategies, models of

teaching, into four major groups that share certain characteristics. The groups are








identified as (a) the social family; (b) the information-processing family; (c) the personal

family; and (d) the behavioral family. No one model of teaching is suitable for every

lesson; instead, effective teachers will use a variety of instructional models to achieve

their academic purposes (Joyce & Weil, 1996; Joyce, Weil, & Wald, 1981).

The selection of an instructional strategy (model of teaching or teaching model)

helps determine teacher behavior which orders the classroom. Once an instructional

strategy has been selected, the teacher will establish the tasks to be performed, set up a

social system or climate of acceptable behavior within the classroom, and regulate the

pace and diversity of instructional challenges students will face (Joyce, 1981). Thus, one

might hypothesize that the variety of instructional strategies used by a teacher will reflect

the intellectual richness of a classroom environment. Teachers who use a wide variety of

instructional models offer more opportunities for students to expand their own learning

strategies. Students, particularly those who might be classified as at-risk, are often

resistant to instructional strategies that require them to behave in new ways. Joyce and

Weil (1996) recommended that teachers create a "dynamic disequilibrium" which will

expose students to new ways of thinking and learning that may be temporarily

uncomfortable but which can result in greater intellectual growth. With adequate support,

these students can re-learn their adaptations to the educational environment and acquire

more effective learning strategies.

Three assumptions underlie exploration of diverse instructional strategies. First,

teachers can choose from a wide variety of alternative approaches to instruction if they

are skilled in using different models of teaching. Second, the instructional method chosen








not only influences what is learned, it also influences how it is learned. Finally, students

are an integral part of the learning experience, and they react differently to each teaching

method (Joyce & Calhoun, 1996).

The use of powerful instructional models has been shown to counteract the

negative effects of race, gender, and socioeconomic status (SES) in the classroom. Sharan

and Shachar (1988) used a group investigation model with high and low SES students. A

control group of high and low SES students were taught using traditional whole group

instruction. The low SES students taught by group investigation made twice the academic

gains of low SES students taught in the traditional way and even exceeded the gains made

by high SES students in the control group. High SES students taught by group

investigation greatly exceeded the gains of high SES students in the control group, as well.

Group investigation proved to be a powerful instructional strategy for both socially

advantaged and disadvantaged students.

In another study, writing teachers used an inductive teaching model with fourth

grade students (Joyce, Calhoun, Carran, Simser, Rust, & Halliburton, 1996). They helped

students explore techniques used by published authors as a learning tool for improving

writing. The teachers collected writing samples from students at intervals and the

samples were graded by a panel of experts who did not know the students. The end-of-

year writing scores were higher for the fourth graders taught with the inductive thinking

model than end-of-year scores for the eighth graders the previous year. All students,

from the poorest writers to the best writers, made substantial gains over the year. In

addition, the gap between male and female performance narrowed significantly.








One poorly performing middle school had a promotion rate of only 30% and low

academic gain scores (Joyce, Murphy, Showers, & Murphy, 1989), Attempts to

improve school performance, such as smaller classes, increased counseling, and special

programs for at-risk students, had not produced any significant improvement. The

researchers trained the faculty in using new models of teaching such as group investigation

and inductive thinking. When these strategies were implemented in the classroom,

dramatic improvement followed. By the end of the first year, the promotion rate was up

to 70%; at the end of the second year, it was 95%. The researchers believed that

increasing the instructional repertoire of teachers not only increased learning for students

but also reduced off-task behavior and improved school climate.

In a review of research on good teaching, Porter and Brophy (1988) reported that

effective teachers offer balanced and integrated learning opportunities which are tailored

to fit the needs of their students. Teachers who help students learn metacognitive

strategies and provide reinforcement of those strategies through frequent practice enhance

student achievement. Brophy and Good (1986) also examined a number of studies that

related teacher behavior to student achievement. They found that student achievement

was improved when teachers provided context and structure for the information

presented. The studies they reviewed also suggested that low SES students require a

warmer, more accepting classroom climate, more active learning, and smaller increments of

instruction with higher success rates.

The ideal learning environment encourages depth of understanding, encourages

excitement about learning, and promotes social as well as cognitive growth. Schools with








a positive learning environment demonstrated more time spent on instructional activities,

an emphasis on teamwork rather than individual competition, high expectations for

student achievement, and more effective patterns of behavior reinforcement (Kohn, 1996).

Using a variety of instructional strategies can improve student achievement by

providing students with metacognitive skills which enable them to become independent

learners. Student efficacy beliefs can be enhanced by helping students learn new

strategies for acquiring knowledge. Furthermore, the conditions which lead to a variety of

instructional strategies can be linked to teacher efficacy beliefs and to school climate. All

of these factors--efficacy beliefs, instructional practices, and learning environment--are

interrelated and have an impact on student achievement.

Summary

Most researchers agree that student mobility is a detrimental factor in student

achievement, even though many other disadvantages may exist in the lives of highly

mobile students, The research also supports the belief that teachers with high personal

efficacy who use a repertoire of powerful instructional strategies can help students

overcome the otherwise limiting factors of race, gender, and SES. Student efficacy beliefs

are often influenced by the expectations of their teachers; in turn, student efficacy and

school climate can also influence student achievement. The Home Base School was

formed to meet the needs of locally transient students who come to school with a variety

of other disadvantages. The teachers for the school were recruited to work with a

population of high-risk students. This study investigated whether the efforts to provide

a stable school placement and recruit teachers who desire to work with this population of






40

students led to improvement in students' academic achievement. The study also explored

qualitative factors such as teacher efficacy, instructional strategies, student efficacy, and

the learning environment in some area elementary schools as compared to the Home Base

School. This study investigated whether the Home Base School offered a significantly

improved educational opportunity for this group of highly mobile students.

















CHAPTER 3
MATERIALS AND METHODS


In an effort to fully describe the methods and procedures used in this study, this

section begins with a discussion of the settings where the study was conducted. A

discussion of the participants, tasks and materials, variables, instrumentation, and data

collection procedures is presented. This chapter concludes with a discussion of the data

analysis. All names of schools, centers, teachers, and students have been changed to

protect the anonymity of those who participated in the study.

The Settings

On January 27, 1997, the Home Base School opened in a mid-sized southeastern

city. The school was designed to offer a stable school setting to students considered at

risk of academic failure due to high mobility. Several conditions govern student admission

to the school. First, only students in grades K-4 who changed schools at least twice

within the last two years were eligible for enrollment. Second, the parents of eligible

children must agree to their child's placement at the Home Base School, enrollment is

voluntary. Third, once the child is enrolled, the school district agrees to provide

transportation for the student to the Home Base School regardless of where the family








moves within the school district. Thus, even if a family moves frequently between school

zones, their children will not need to transfer to a new school site with each move.

The Home Base School is a small organization that operates under the auspices

and administration of the nearby Josephine Baker Elementary School and the local school

district administration, When this study was conducted, fewer than 100 students were

enrolled at the Home Base School. A Baker teacher on special assignment provides on-

site supervision and serves as the Director of the school. The Home Base School is

housed on the campus of Sylvia McLeod Center, which is a school for special-needs

students. Four regular classroom teachers are employed at the Home Base School.

Teachers at the Home Base School have been recruited specifically to work with a high-

risk population of students who have multiple disadvantaging factors. At the time this

study was undertaken, the student body at Home Base was 84% minority. All of the

students were below grade level academically and qualified for lunch subsidies (free or

reduced-price lunch), which was used as an indicator of low socioeconomic status (SES).

When the Home Base School first opened, it housed only grades K-2. By the

1997-98 school year, the school had grown to include classes through fourth grade.

Currently, the school administrators would like to add grade levels each year until they

can offer K-8 education for transient students. During the time of the study, all of the

classrooms were multi-age groupings: K-1; 1-2; 2-3; 3-4.

Two comparison site schools with high student mobility were chosen, Cooke

Elementary and Stanton Elementary. All three schools (both comparison sites and the

Home Base School) used varieties of scripted curriculum for mathematics and reading.








The mathematics curriculum was the same at all three sites; the Home Base School and

one comparison site used an identical reading curriculum while the other comparison site

used a scripted reading curriculum from a different publisher. Both comparison site

schools rotated students among several different teachers for core subjects. The Home

Base School generally kept students in self-contained classrooms except to accommodate

the needs of individual students.

Participants

Participants who completed the study include 10 students (6 males, 4 females, 5

African-American, 3 White, 2 bi-racial; 8 second graders, 2 third graders) from the Home

Base School and 9 students (4 males, 5 females; 6 African-American, 3 White; 5 second

graders, 4 third graders) from two comparison elementary schools who were identified as

highly mobile. The Home Base experimental participants (12 experimental students and 6

alternates) were randomly selected from the 26 students in second and third grade who

had attended the Home Base School in the spring of 1997 and thus could be considered to

have a stable educational placement for the 1997-98 school year. Of the 18 students from

Home Base School who were invited to be a part of the study, 14 returned consent forms

indicating their willingness to participate. Twelve students were selected as participants.

The two alternates were used in the pilot study to test the reliability of the survey

instrument. Two students from the Home Base School were lost during the year because

their families moved out of the school district. A stratified sample of participants from

the comparison site schools was selected from the identified group of highly mobile

students according to the following criteria: (a) number of school changes within the last








two years; (b) length in residence at the current school; (c) grade level; (d) race; (e) gender;

(f) SES, and (g) prior academic achievement. Of the 18 students from comparison site

schools invited to participate in the study, 9 returned consent forms. All of the

comparison site participants completed the study.

The teacher participants at the Home Base School (n=4) included one teacher with

a bachelor's degree and two teachers with Ph.D. degrees. The fourth teacher held a

temporary position for approximately one month and did not complete the demographic

information. All of the teachers at the Home Base School were white. Three of the Home

Base teachers were female and one was male. One teacher had less than 5 years teaching

experience, one had between 6 and 10 years of teaching experience, and one had more than

20 years of experience. Teachers at the comparison sites (n=l H) included four teachers

with bachelors' degrees and five teachers with masters' degrees. Two teachers declined to

provide demographic information. The teachers who did provide demographic

information were evenly distributed along the continuum of teaching experience. Two

teachers had less than 5 years experience, one teacher had 6-10 years of experience, two

teachers had 11-15 years of experience, two teachers had 16-20 years of experience, and

two teachers had more than 20 years of teaching experience. Two of the comparison site

teachers were African-American; nine were white. All of the comparison site teachers

were female. Table 3.1 shows the demographic profile of the Home Base and comparison

site school teachers.











Table 3. 1. Demographic Profile of Home Base and Comparison Site Teachers


1 Education Race Gender Years Teaching Experience
BA MA PhD W B M F <5 6- 11- 16- >20
10 15 20
Home Baseb 4 1 2 4 0 1 3 1 1 1
Comparison 11 4 5 9 2 11 2 1 2 2 2
Sitec
aTotal number of teachers observed at each site
bOne of the Home Base teachers did not provide demographic information
CTwo of the comparison site teachers did not provide demographic information


Tasks and Materials

At the beginning of the study, all participants were interviewed to determine a

sense of school affiliation. Each participant completed a survey to measure their sense of

academic self-efficacy. The interviews and surveys were administered to each student

individually by the researcher. From October, 1997, until April, 1998, each student was

observed for 30-40 minutes during an instructional period. Seven observations per student

were conducted. Field notes were collected to document the interactions of the student

with the teacher and his/her peers, the instructional methods being used, and the ways in

which the teachers attempted to facilitate student comprehension.

The teacher's instructional style was noted using researcher-developed questions

which were based on the instructional frameworks discussed by Joyce and Weil (1996) in

their book Models of Teaching. The researcher developed the following template of








questions (see Appendix A) to describe teachers' instructional styles and serve as a guide

for the observations.

1. Sequentially describe the tasks that students are asked to do.

2. Describe student and teacher roles and their respective involvement in the

lesson.

3. Describe the teacher's response to students' questions and answers.

4. Identify and describe how instructional supports are used throughout the

lesson.

5. Diagram the placement of students being observed.

6. Describe their social interaction with the teacher and their peers.

The survey instruments to measure both student efficacy (see Appendix B) and

teacher efficacy (see Appendix C) were adapted by the researcher from suggestions

provided by Dr. Albert Bandura (personal communication to Behar-Horenstein, August

1997). The researcher-designed instruments were pilot tested on sample populations of

teachers and students. Students at the Home Base School who were selected as alternates

for the study (n=2) were given the survey and interviewed by the researcher. Using

Cronbach's alpha, reliability for the student survey was estimated at ot=.68.

Six teachers who were not involved in the study but who also taught highly mobile

students volunteered to complete surveys for a pilot test of the teacher survey instrument

(Home Base teachers [n= 1 ]; comparison site teachers [n=3 ]; and alternate site teachers

[n=21). Using Cronbach's alpha, the reliability estimates for the teacher survey was








ot=.37. Teachers (n=15) whose students were involved in the study were given the

survey (see Appendix C) to determine their attitudes and feelings of efficacy towards

working with highly mobile students. Two teachers at the comparison site schools and

one teacher at the Home Base School did not return the survey. In total, 12 teacher

surveys were analyzed.

Parents of all students involved in the study (n=19) were surveyed to determine

their satisfaction with their child's school. The survey instrument was taken from

material provided by the Florida Successful Schools Project. Information on how to

obtain the survey instrument is provided on page 65. The reader can also review Chapter

1: Successful schools: Pilot project report (Florida Department of Education, 1993-94)

for additional information. Two parents declined to complete the survey, although they

did give permission for their children to participate in the study. Two parent surveys

were not returned. In total, 15 parent surveys were analyzed.

The Curriculum-Based Assessment (Mercer, Jordan, Miller, Schenck, Black,

Bock, & Kidwell, 1996) was administered as a pre- and post-test in reading and

mathematics to measure academic gains. Sample reading and mathematics tests can be

obtained from the technical manual for the Curriculum-Based Assessment. The pre-test

was given in the fall of 1997, and the post-test was given in the spring of 1998.

Attendance and disciplinary referral data was collected from each school.








Operational Definition of Variables

To qualify for placement at the Home Base School, a child must have moved to

different school zones at least twice within the last two years. The participants in this

study were comprised by a random sample of students in grades 2 and 3 who attended

the Home Base School during the spring of 1997 and returned to the school for the 1997-

98 school year. These students were assumed to have a stable placement for the 1997-98

school year. The comparison group was selected among students still in their zoned

schools who met the criteria of high mobility, length of time in residence at the school,

and grade level. Other moderating variables were matched as closely as possible, given the

limited number of students available within the comparison population. Length of time in

residence was determined by ranges of time based on enrollment for the 1996-97 school

year: more than 17 weeks; 12-17 weeks; 8-11 weeks, less than 8 weeks. Socioeconomic

status was determined by qualification for free or reduced lunch subsidies. Prior academic

achievement was determined by the reading and mathematics subtest scores of the Iowa

Test of Basic Skills (ITBS) based on the Spring 1997 test administration.

Academic gains for the 1997-98 school year were measured using a pre- and post-

test of the reading and mathematics portions of the Curriculum-Based Assessment test

(Mercer et al., 1996). Frequency of absenteeism was determined by the school-reported

attendance records for each participant. Discipline referral information was based on data

reported by the schools. School affiliation and academic self-efficacy were determined

from a survey based on Bandura's self-efficacy studies (see Appendix B) and an

interview conducted by the researcher with each participant (see Appendix D). School








affiliation and academic self-efficacy were also assessed qualitatively through classroom

observations of the participants. Parent attitudes about school climate were measured by

parent responses to The Successful Schools Parent Survey. Instructional methods used

by teachers were assessed by observation and classification using the instructional models

described in Models of Teaching (Joyce & Weil, 1996) and adapted by Behar-Horenstein

and Ganet-Sigel (in press). The teacher attitudes about working with highly mobile

children were assessed using a survey designed by the researcher based on Bandura's self-

efficacy studies (see Appendix C).

The independent variables in this study were (a) placement at the Home Base

School; (b) the variety of instructional methods used by the teachers; and (c) teacher

attitudes about working with at-risk students.

The moderator variables were (a) length of time in residence at the school; (b)

number of school changes within the last two years; (c) grade level; (d) gender; (e)

race/ethnicity, (f) socioeconomic status; and (g) prior academic achievement.

The dependent variables measured in this study consisted of determining if there

were differences between the experimental group and the comparison group by (a)

academic achievement in reading and mathematics; (b) students' sense of school

affiliation, (c) students' self-reports of academic efficacy; (d) rate of absenteeism; and (e)

the number of discipline referrals.

The researcher conducted classroom observations of teachers and students on a

daily basis over a seven-month period to obtain qualitative data about the instructional








strategies teachers used and the learning environment. This procedure minimized the

obtrusiveness of the study.

Instrumentation

Curriculum-Based Assessments

The Curriculum-Based Assessment (CBA) was devised by university professors

and special education coordinators to provide an equitable assessment for all students

within the school system (Mercer et al., 1996). The reading and mathematics portions of

the assessment were used as a pre- and post-test to measure academic progress during the

1997-98 school year. The information on reliability and validity reported below was

taken from the assessment test manual. The curriculum-based assessments were pilot

tested in 1994-1995. The standards were normed on tests administered during 1995-

1996. Matched to the curriculum of the local school district, the curriculum-based

assessment established norm-referenced standards based on the local population of

elementary school students.

Oral fluency was used as a valid measure of reading achievement. The CBA

measures in oral fluency report the total number of words read in one minute and the total

number of words read correctly. Students were given three reading passages from basal

reading series to read aloud, representing reading selections from the beginning, middle,

and end of the school year. The reading passages were selected by grade level. The

students were given one minute per passage to read aloud. The composite reading score

represented an average of the total correct words on each passage. A summary of validity

studies in the curriculum-based reading assessment indicated correlations ranging from .73








to .90 on most general standardized measures of reading and correlations ranging between

.70 and .91 on most tests that measured reading comprehension. Overall, 74% of the

standardized reading measures that were reported had a correlations of .70 or higher; 26%

of the standardized tests had correlations ranging from .46 to .69.

Mathematics achievement was measured by counting the total number of correct

problems solved during the 45-minute testing period. The number and type of problems

varied by grade level. The manual noted that the research on validity of the mathematics

portion of the CBA had not yielded the same high correlations as reading. A summary of

validity studies which correlated the CBA mathematics test with well-known

standardized tests indicated that 38% of these tests had a.50 correlation or above with

the CBA. Sixty-two percent of these standardized measures had correlations up to .49.

The difference in validity between the reading and mathematics measures may be caused

by an inaccurate reflection of the mathematics curriculum or the reading requirements

within the test may result in penalizing poor readers.

The reliability estimates for the CBA in reading ranged from .89 to .99. In the

mathematics portion, 88% of the measures indicated reliability estimates of .70 or higher.

Testing directions and tests are available in the technical manual for the Curriculum-Based

Assessment.

Parent Survey

The Successful Schools Parent Survey was developed in 1993-94 by the Florida

Successful Schools Project. Survey questions were adapted from a survey used by the

San Diego County Office of Education. The parent survey was designed primarily to








measure the Home-School-Community Relations correlate of effective school attributes.

The survey was pilot tested in 16 schools across six Florida school districts representing

rural, urban, and suburban districts throughout the state from the Panhandle to South

Florida. The participants in the pilot test were selected from schools that qualified for

Title I funding (35% or more of the student body were on free or reduced lunch

subsidies). The student body at these schools was characteristically comprised by high

ratios of minority students, high numbers of Limited English Proficiency students, and/or

large numbers of migrant students. Reliability studies and predictive validity estimates

were based on data from the four highest achieving and four lowest achieving pilot

schools. The reliability of the parent survey was based on the internal consistency of

items or groups of items referring to related concepts of effective schools. Predictive

validity was determined by correlating survey responses among high and low achieving

schools and by correlating survey responses with student achievement at the pilot

schools. Using Cronbach's alpha, reliability for the parent survey was estimated at

(x=.92. The predictive validity of the parent survey was insufficient to distinguish

between high achieving and low achieving schools except for the items relating to a Safe

and Orderly Environment. Parents of students at low-achieving schools were much more

likely to indicate a need for stronger discipline policies, improved student behavior, and

safer facilities than were parents at high-achieving schools. The survey was designed to

provide an assessment of parents' perceptions of school climate and satisfaction with the

school, however. While low and high achieving schools could not be predicted on the








basis of the survey, the questions did reveal adequate information about school

satisfaction.

Teachers' Instructional Strategies

The questions for classroom observations were developed by the researcher based

on Joyce and Weil's instructional models framework (Joyce & Weil, 1996). Questions

about how instruction is implemented formed the basis for observation within the

classroom (see Appendix A). Multiple observations were conducted in each classroom.

The dimensions along which observations were conducted included:

1. What are the sequential tasks students are asked to do? (Syntax)

2. What are the student and teacher roles and their respective involvement in the

lesson? (Social systems)

3. What are the teacher's responses to students' questions and answers?

(Principles of reaction)

4. What instructional supports are being used and how are they used? (Support

system)

5. What is the social interaction between the teacher, the student under

observation, and his or her peers? (Social interaction)

Each lesson was analyzed inductively, guided by Spradley's (1980) scheme of

domain analysis and by the constant-comparative method proposed by Glaser and

Strauss (1967). Domain analysis worksheets were used to determine the variety of

instructional strategies used.








School Affiliation

Students were interviewed individually by the researcher about issues related to

school affiliation (see Appendix D). The interviews were transcribed and examined using

content analysis to uncover themes related to school affiliation such as liking school,

feeling that one's teacher wanted him or her to do well in school, having a sense of the

purpose of schooling, and feeling accepted at school. The results of content analysis were

grouped into themes and compared qualitatively between students at the Home Base

School and students at the comparison sites. Student responses to each theme were

tallied and compared by group.

Data Collection

Students in both the experimental and comparison groups were observed for a 30-

40 minute instructional period from October 1997 through April 1998. A total of seven

observations (approximately one every three weeks) was completed for each student.

Field notes were recorded to document the interactions between the teacher and the

student under observation, the peer interactions and behavior of the student, and the

instructional models used by teachers.

Student, parent, and teacher surveys and student interviews were conducted

during the first four weeks of the study. The data from these surveys and interviews

were coded and analyzed. Student survey data were used to measure students' sense of

academic self-efficacy. Student interview information was used to measure school

affiliation. Teacher survey data were used to measure teacher efficacy and beliefs about








working with highly mobile students. Parent survey data were used to determine parental

satisfaction with the school.

Test data from the pre- and post-test administration of the Curriculum-Based

Assessment (Mercer et al., 1996) were used to determine academic achievement in reading

and mathematics for the 1997-98 school year. Attendance records and discipline referral

records for the 1997-98 school year were obtained from the local schools.

Data Analysis

Two-tailed t-tests were used to test the null hypotheses. The level of significance

for each statistical test was set at 0t--.05.

A stable placement at the Home Base School compared to placement in a local

elementary school was the primary independent variable. Parents' perceptions of school

climate, teachers' and students' perceptions of efficacy, and students' academic

achievement--all measured variables--were interval in nature. Attendance rates and

number of discipline referrals were also interval in measurement. Prior academic

achievement was measured by using the reading and mathematics subtest scores for each

participant from the 1996-97 Iowa Test of Basic Skills. These measures were continuous

in nature.

A t-test comparing mean scores for the experimental group and the comparison

group on the 1996-97 Iowa Test of Basic Skills was used to measure prior academic

achievement. Discipline referral data and absences were analyzed using a t-test to








compare mean scores of each group. Student academic efficacy was measured by using a

t-test to compare group means.

A t-test was performed on the teacher survey results to measure teacher efficacy

and beliefs about working with highly mobile children. A chi-square analysis was

performed on parent survey data to measure school satisfaction/perception of school

climate.

Pre- and post-test data from the Curriculum-Based Assessment were analyzed

using ANCOVA. The adjusted means for reading achievement and mathematics

achievement between the two groups were compared.

Classroom observations were recorded using continuous observation techniques

guided by the questions developed by the researcher from Joyce and Weil's (1996)

instructional frameworks. The observations were analyzed inductively as described

earlier. Student school affiliation was also analyzed quantitatively and qualitatively using

the students' responses to interviews, the student survey, and classroom observation.

Reliability was corroborated and validity was assured through a dependability audit in

which another trained graduate student reviewed the domains and coded selected samples

of the observations.
















CHAPTER 4
RESULTS AND DISCUSSION


The data collected in this study were analyzed using quantitative and qualitative

methods. The first portion of this chapter will examine results of the quantitative data.

The second section of this chapter will explore the results of the qualitative data.

Quantitative Analysis

The following research questions from this study were explored using quantitative

analysis.

1. Is there a difference between students at the Home Base School and the

comparison sites when compared by (a) academic achievement; (b) rates of absenteeism;

and (c) number of discipline referrals?

2. Is there a difference between teachers at the Home Base School and teachers at

the comparison sites in their efficacy beliefs and their perceptions of highly mobile

students' ability to do well in school?

3. Is there a difference between parents of students at the Home Base School and

parents of students in the comparison site schools in their perceptions of the learning

environment?

4. Is there a difference between students at the Home Base School and the

comparison site schools in their perceptions of their own academic efficacy?








Research Question 1: Is there a difference between students at the Home Base School and

the comparison sites when compared by (a) academic achievement (b) rates of

absenteeism, and (c) number of discipline referrals?

Academic Achievement

After students were selected for the study, the 1996-97 scores on the Iowa Test

of Basic Skills (ITBS) for reading and mathematics were collected as part of the

demographic data for each participant. A t-test was performed on mathematics subtest

scores and on reading subtest scores for each group to determine prior academic

achievement. The mean scores for each group in reading and mathematics are shown in

Table 4.1.

The t-tests indicated no significant difference between the two groups of students

(Reading: p=0.28; Mathematics: p= 0.21). Thus, both groups can be assumed to have

similar prior academic achievement before the beginning of the study.

To measure academic achievement during the study, a pre-test and post-test were

given in reading and mathematics using the Curriculum-Based Assessment (CBA) (Mercer

et al., 1996). ANCOVA analysis of reading scores indicated that students at the Home

Base School made significantly higher gains in reading than students at the comparison

sites (p=.04). The academic gains in mathematics between the two groups of students

were not statistically significant (p=.92). The mean scores (M) and standard deviation

(SD) for each group on the post-tests are given in Table 4.2. The null hypothesis that

there would be no difference in academic achievement between students at the Home Base








and comparison site schools was rejected for the measure of reading achievement and was

not rejected for the measure of mathematics achievement.

Table 4.1. Mean Scores and Standard Deviations for Participants' ITBS Scores (1996-97)
in Reading and Mathematics

ITBS Subtest na Mean SD

Reading

Home Base 10 32.1 29.6

Comparison Sites 9 20.4 14.6

Mathematics

Home Base 9 30.6 32.7

Comparison Sites 9 15.4 12.3

aNumbers of children in each group for whom ITBS scores from 1996-97 were available.


Table 4.3 shows the ANCOVA table for the CBA reading scores on the pre- and

post-tests. Table 4.4 presents the ANCOVA table for the CBA mathematics scores on

the pre- and post-test.

Student Absenteeism

The total number of student absences recorded by the school for the 1997-98

school year were collected for each participant in the study. Absences were analyzed

using one-way ANOVA. Mean absences for the year for each group of students are

shown in Table 4.5, The ANOVA summary table for absences is shown in Table 4.6.












Table 4.2. Mean Scores, Standard Deviations, and Adjusted Post-Test Means for Pre-
and Post-test in Reading and Mathematics (CBA)


CBA Subtests na Pre-Test SD Post- SD Adjusted
Mean Test Post-
Mean Test
Mean
Reading

Home Base 10 23.7 16.7 46.7 28.7 66.9

Comparison 9 56.3 34.7 75.0 44.2 52.6
Sites

Mathematics

Home Base 10 36.0 17.6 59.6 18.7 52.4

Comparison 9 19.0 12.9 43.5 25.2 51.6
Sites

aNumber of students in each group taking the pre- and post-test in reading and
mathematics


Table 4.3. ANCOVA Summary Table for CBA Reading Scores
Dependent Variable: Post-Test

Source DF Type III SS Mean Square

Group 1 686.96 686.96

Pre-Test 1 20733.48 20733.48

Error 16 2304.62 144.04

Total 18 26831.79

Note: Pre-Test is the covariant


F Value

4.77

143.94


P

0.0442

0.0001











Table 4.4. ANCOVA Summary Table for CBA Mathematics Scores
Dependent Variable: Post-Test

Source DF Type III SS Mean Square F Value p

Group 1 2.58 2.58 0.01 0.9283

Pre-Test 1 3320.99 3320.99 10.74 0.0047

Error 16 4947.64 309.23

Total 18 9488.00
Note: Pre-test is the covariant



Table 4.5. Mean and Standard Deviations of Participants' Absences for the 1997-98
School Year

na Mean SD

Home Base 10 13.7 7.9

Comparison Sites 9 6.8 4.6

'Number of students in each group




Table 4.6. ANOVA Summary Table for Absences

Source DF Sum of Mean Square F Value p
Squares

Between Group 1 219.75 219.75 5.00 0.039

Within Group 17 746.99 43.94

Total 18 966.74









The students at Home Base School had approximately twice as many absences as

the students in the comparison sites. The difference in rates of absenteeism was

significant (p=.039). The hypothesis that there would be no difference between the

Home Base School and the comparison site schools in terms of absenteeism was rejected.

Given the higher absences of the Home Base students (approximately twice as many

absences as the comparison site students), the gains in reading become even more

significant.

Discipline Referrals

Discipline referral data for the 1997-98 school year were collected from the

schools for each student in the study. The data were analyzed using one-way ANOVA.

The mean number of referrals for each group is listed in Table 4.7. The ANOVA

summary table for discipline referrals is shown in Table 4.8.



Table 4.7. Means and Standard Deviations for Discipline Referrals of Participants for the
1997-98 School Year by Group

Group na Mean SD

Home Base 10 0.00 0

Comparison Sites 9 0.88 1.2

aNumber of students in each group









Table 4.8. ANOVA Summary Table for Participant Discipline Referrals by Group

Source DF Sum of Mean Square F Value p
Squares


Between Group 1 3.74 3.74 4.94 0.04

Within Group 17 12.89 0.76

Total 18 16.63


The Home Base students had fewer discipline referrals than comparison site

students. In fact, Home Base teachers handled discipline themselves within the classroom

setting. When students failed to respond to the teacher-imposed discipline, they were

counseled by the director. No instance of Home Base teachers using the formal discipline

referral was observed during this study. The null hypothesis that there would be no

difference between students at the Home Base School and the comparison site schools in

terms of discipline referrals was rejected (p = .04).

Research Question 2 Is there a difference between teachers at the Home Base School and

teachers at the comparison sites in their efficacy beliefs and their perceptions of highly

mobile students' ability to do well in school?

Fourteen teachers at the Home Base School and at the comparison site schools

were asked to complete a researcher-designed survey to measure their own sense of

teaching efficacy and their beliefs about the ability of highly mobile students to be

successful in school (see Appendix C). Three teachers at the Home Base School and nine








teachers from the comparison sites agreed to complete the surveys. Two teachers at the

comparison sites and one teacher at the Home Base School did complete the surveys. A

t-test was used to analyze the survey data. No significant differences were found

between teachers at the Home Base School and teachers at the comparison site schools for

measures of teaching efficacy or their perceptions about the ability of highly mobile

students to be successful in school (p=.34).

Research Question 3: Is there a difference between parents of students at the Home Base

School and parents of students in the comparison site schools in their perceptions of the

learning environment?

Parents of participating students were asked to complete the Successful Schools

Parent Survey (Florida Successful Schools Project, 1994) to assess their satisfaction with

their child's learning environment. A total of 19 parents were asked to respond. Two

parents indicated on the informed consent form that they were unwilling to participate in

the survey, although they gave permission for their children to participate in the study.

Two parent surveys were not returned. A total of 15 surveys were collected (10 from

Home Base School [100%] and 5 from the comparison sites [55%]).

The parent survey asked parents to respond to a series of statements about their

child's school by selecting one of three choices: agree, disagree, or no opinion. The

statements pertained to learning environment issues and included sentences such as "The

teachers help my child learn." and "Teachers at this school expect all children to be

successful." A complete copy of the parent survey may be obtained by contacting the








Florida Successful Schools Project, Florida Title I State Evaluation Advisory Panel,

Tallahassee, Florida,

A chi-square analysis was performed on the parent surveys. The results indicated

two areas of difference. The parents of the Home Base School participants indicated that

they felt more welcome in their child's classroom than did parents of participants at the

comparison site school (p=.004) in response to statement 5 of the survey: "I feel

welcome when I visit my child's classroom." Parents of the Home Base students

indicated that they felt more involved and perceived themselves as having been consulted

for ideas to a greater extent than did parents of students at the comparison sites (p=.02)

in response to statement 11 of the survey: "I have been asked to give ideas on how to

improve the school." The null hypothesis that there would be no difference between

parents at the Home Base and comparison school sites in terms of their satisfaction with

their child's learning environment was rejected.

During observations it was noted that parents of the highly mobile students were

much more frequently involved in activities at the Home Base School than at the

comparison site schools. Over the course of the seven months, parents and family

members of Home Base students were observed attending two programs performed by

students and visiting in the classroom on two occasions. Also, parents and family

members attended a breakfast at a local restaurant with their children's classes, a

Valentine's Day luncheon prepared by their children, and a Family Day held by the

school. At the comparison sites, no parental involvement from the participants' parents

was observed.








Research Question 4: Is there a difference between students at the Home Base School and

the comparison site schools in their perceptions of their own academic efficacy?

A survey of academic self-efficacy, based on Bandura's self-efficacy studies, was

administered individually to participants (See Appendix B). A t-test was used to analyze

the results of the survey data. Mean scores for each group are given in Table 4.9.


Table 4.9. Mean Scores and Standard Deviations for Participants' Academic Self-
Efficacy Survey Responses by Group

Group na Mean SD p


0.02
Home Base 10 4.25 0.44

Comparison Sites 9 3.81 0.34

Number of students in each group


Students at the Home Base School demonstrated a higher sense of academic self-

efficacy (p=.02) than students at the comparison sites. Qualitative data analysis also

supports the finding that Home Base participants demonstrated high academic self-

efficacy behaviors more frequently than did participants at the comparison sites.

Qualitative Analysis

The following research questions from this study were explored using qualitative


analysis.








5. Is there a qualitative difference between teachers at the Home Base School and

comparison site schools in their use of instructional strategies?

6. Is there a difference between the Home Base School and comparison site school

participants in their beliefs about their ability to do well in school?

7. Is there a difference between the Home Base School and comparison site school

participants in their sense of school affiliation?

Guidelines for structuring the observations were questions developed by the

researcher based on instructional frameworks suggested by Joyce and Weil (1996) (see

Appendix A), The data from the observations was analyzed as described previously in

Chapter 3. Behaviors were coded as they occurred, so that an individual student or

teacher might have many instances of a particular behavior whereas another student or

teacher might have only a few instances of the behavior. The frequency with which a

behavior occurs is an important qualitative factor in assessing the learning environment,

After each observation was coded by domain, behaviors were tallied by individual and

then by group. Group results are reported.

Instructional Models and Learning Environment

The first domain focused on the identification of the instructional model or models

being used throughout the instructional period. Instructional models are powerful ways

of teaching that provide students with tools to become effective learners (Joyce & Weil,

1996). The conceptual frameworks for an instructional model help teachers plan the

orientation to the material, the learning activities, the teacher's instructional behavior, the

materials and environment needed, and the level of structure provided by the teacher








(Behar-Horenstein, personal communication, April 1, 1999). Joyce and Weil (1996)

classify models of teaching into four major groups: information-processing, social,

personal, and behavioral systems.

Information-processing models focus on intellectual development, helping

students learn to construct knowledge through conceptual control of material (Joyce &

Calhoun, 1996). The information-processing models are listed in Table 4.10.

The social models of teaching and learning are designed to foster social interaction

and the development of learning communities. Students learn to work together and build

on the synergy that is created from collaboration (Joyce & Weil, 1996; Joyce & Calhoun,

1996). Social models of teaching are shown in Table 4.11.

Personal models of instruction help students develop self-confidence and build

emotional and mental health. They also help students begin to set personal goals and

aspirations for education, to acquire skills in thinking creatively, and to express

themselves effectively. Instructional models from the personal family help students build

empathy for others and acquire a realistic self-concept (Joyce & Weil, 1996; Joyce &

Calhoun, 1996). The personal family of teaching models in shown in Table 4.12.











Table 4.10. Information-Processing Models


MODEL


Inductive Thinking


Concept Attainment


To classify information and concepts, build
conceptual understanding of disciplines,
and learn to build and test hypotheses
based on classifications.

To learn specific concepts and strategies to
attain them, gain control over subject
matter, build hypotheses, and study
thinking.

To acquire scientific process, knowledge
bases, and major concepts of specific
disciplines. Conceptual thinking,
hypothetical reasoning and ability to think
critically are developed.

To learn how to reason causally, collect
data, build concepts, and develop and test
hypotheses.


Scientific Inquiry


Inquiry Training


Cognitive Growth

Advanced Organizer


Mnemonics


Synectics


To increase intellectual growth.


To increase ability to synthesize and
organize information from multiple sources.

To develop strategies for mastering new
concepts, facts, and ideas.

To use analogies to develop creative
capacity.


PURPOSE


Source: Behar-Horenstein and Ganet-Sigel, in press (adapted from Joyce and Calhoun
(1996). Creating learning experiences: The role of instructional theory and research.
Alexandria, VA: Association for Supervision and Curriculum Development pp. 10-11).










Table 4.11. Social Models


MODEL


Group Investigation


Social Inquiry


Jurisprudential Inquiry


Laboratory Method


Role Playing


Positive Interdependence


To reflect upon one's self and one's own
values, develop a commitment to improving
society, and participate productively in a
democracy.

To work together to solve social problems,
and develop strategies for problem-solving.

To analyze issues of public interest using a
legal framework.

To develop strong and sensitive social skills
while learning how to understand group
dynamics.

To learn strategies for resolving conflict and
social problems.

To learn how to work together
interdependently and appreciate the nature
of self-others relationships.


Structured Social Inquiry To learn to work together cooperatively in
the pursuit of academic inquiry.
Source: Behar-Horenstein and Ganet-Sigel, in press (adapted from Joyce and Calhoun
(1996). Creating learning experiences: The role of instructional theory and research.
Alexandria, VA: Association for Supervision and Curriculum Development pp. 10-11).


PURPOSE









Table 4.12. Personal Models


MODEL


Nondirective Teaching


Awareness Training



Classroom Meeting


Self-Actualization


Conceptual Systems


To build capacity for self-development and
create personal awareness.

To enhance personal growth through self-
understanding, increasing empathy, and
sensitivity towards others.

To increase responsibility to self and
others. To increase self-understanding.

To increase capacity for personal
development and self-understanding.

To increase one's own flexibility in
interacting with others and propel
individuals to higher levels of conceptual
development as they learn to process
information.


Source: Behar-Horenstein and Ganet-Sigel, in press (adapted from Joyce and Calhoun
(1996). Creating learning experiences: The role of instructional theory and research.
Alexandria, VA: Association for Supervision and Curriculum Development pp. 10-11).



Behavioral instructional models are based on the notion that behavior is governed

by stimulus and response. Behaviorists believe that, given favorable conditions and

appropriate time, anyone can learn (or change) specific observable behaviors (Joyce &


Weil, 1996). The behavioral family of models is shown in Table 4.13.


PURPOSE











Table 4.13. Behavioral Systems Models

MODEL PURPOSE

Social Learning To develop an understanding of one's own
behavior and its consequences. To develop
more adaptive behaviors in order to attain
goals.

Mastery Learning To master content in all subject areas.

Programmed Learning To master academic content. To help
students monitor their own growth and
modify learning strategies.

Simulation To apply problem-solving concepts and
problem-solving skills in situations that
approximate realistic conditions.

Direct Teaching To master academic content, enhance
motivation, and learn how to pace oneself

Anxiety Reduction To learn how to control aversive emotions
and avert dysfunctional responses.

Source- Behar-Horenstein and Ganet-Sigel, in press (adapted from Joyce and Calhoun
(1996). Creating learning experiences: The role of instructional theory and research.
Alexandria, VA: Association for Supervision and Curriculum Development pp. 10-11).



The explicit use of teaching models can provide powerful learning tools for

students which help them overcome the presumed limitations of race, gender, culture, and

socioeconomic status (Joyce & Weil, 1996). Many of these models have been shown to

accelerate the learning process for students of all abilities and in all curriculum areas

(Joyce & Calhoun, 1996). Thus, one of the most important questions in this study was








whether or not teachers at Home Base School and/or the comparison sites used a variety

of explicit teaching models to enhance learning.

Instructional models were identified in the analysis of each observation. Then

each occurrence of each model was tallied to indicate the variety of instructional models

used and the frequency with which they occurred in each classroom. A chart of

instructional models used was constructed for the classrooms observed at both the Home

Base School and the comparison site schools. Teachers at the Home Base School used a

greater variety of instructional models and used them with greater frequency than did

teachers at the comparison site schools. Table 4.14 indicates the models which were used

and the frequency with which each model occurred in both the Home Base School and the

comparison site schools. Direct instruction was the predominant teaching model in all

settings. All of the schools used scripted mathematics and reading programs which were

designed to be implemented using direct instruction.

Also of interest is the number of different instructional models used by each

teacher. Each model used by a teacher was recorded each time it was used and then the

number of different models used was counted by teacher. Teachers who used several

models tended to vary their techniques frequently. Table 4.15 reports the mean of

instructional models used for each group of teachers.











Table 4. 14. Percentages of Instructional Models Used (and Number of Observed Uses)
by Group

Instructional Model % for Home Base School % for Comparison Sites
(Family of Model) (Number Observed) (Number Observed)
(Na=100) (Nb=74)

Direct Instruction 51% 65%
(behavioral) (n=51) (n=48)

Simulation 11% 3%
(behavioral) (n=l) (n=2)

Inductive Thinking 11% 9%
(information-processing) (n=1 1) (n=7)

Synectics 8% 3%
(information-processing) (n=8) (n=2)

Classroom Meeting 6% 0%
(personal) (n=6) (n=0)

Advance Organizer 3% 0%
(information-processing) (n=3) (n=0)

Mnemonics 3% 0%
(information-processing) (n=3) (n=O)

Concept Attainment 2% 8%
(information-processing) (n=2) (n=6)

Nondirective Teaching 2% 0%
(personal) (n=2) (n=0)

Role-Playing 2% 1%
(social) (n=2) (n= 1)

Group Investigation 1% 1%
(social) (n=1) (n= 1)

No Instruction/No Model 0% 9%
(n=O) (n=7)

aNumber of observed occurrences at Home Base School
Number of observed occurrences at comparison site schools










Table 4.15. Mean Number of Teaching Models Observed by Teacher Group

Group na Mean

Home Base 4 6.25

Comparison Sites 11 1.82

aNumber of teachers observed in each group


While it is important to know if a variety of explicit teaching models are being

used, it is also important to classify teaching models by family and see if teachers are

using models from a variety of instructional families. Teachers at the Home Base School

were observed using teaching models from all of the four families of models. No models

from the personal family were observed at the comparison sites. For both groups, the

most often used teaching models came from the behavioral family (direct teaching,

simulation), with the information-processing family (inductive thinking, synectics,

advanced organizers, concept attainment, mnemonics) representing the next highest

frequency. Role-playing and group investigation were used infrequently, at the same

rates by the teachers at the Home Base School and comparison site schools.

Teachers' Instructional Activities

Kohn (1996) described an ideal learning climate as one in which students have

choices and participate in decision-making, teachers collaborate with the students in

learning activities, and students' interests help to shape the curriculum. He categorized

learning environments in classrooms as "working with" environments where teachers help








to create a positive learning situation or "doing to" environments where teachers seek to

control student behavior through punishments and rewards. Teachers' instructional

activities, their responses to all the students in their classes, and their interactions with

the students under observation all contributed to the learning environment of each

classroom.

The frequency of the instructional activities carried out by each teacher was

recorded. Activities were coded each separate time they occurred, so if a teacher changed

activities frequently he/she would have a higher total of instructional activities than the

teacher who followed one activity for a long period of time. The total number of

instructional behaviors for teachers from the Home Base School and the teachers from the

comparison site schools were counted separately, Table 4.16 lists the instructional

activities observed, the total number of each activity observed for the group of Home

Base teachers and the group of comparison site teachers, and the percentage that each

activity represented of the total observed instructional activities by group.

Teachers at the Home Base School spent more time demonstrating and modeling

the desired academic skill than did comparison site teachers. Teachers at the Home Base

School were also more likely to include building metacognitive strategies as part of their

regular instruction than were teachers at the comparison site schools.











Table 4.16. Percentages of Instructional Activities Observed (and Number Observed) for
Teachers by Group

Instructional Activity % for Home Base School % for Comparison Site
(Number Observed) Schools
(N a = 284) (Number Observed)
S=b 235)


Providing Explicit
Information


Demonstrating/Modeling


Disciplining Students


Facilitating Learning


Praising Students


Monitoring Student Work


Eliciting Student Responses


Evaluating Student
Performance

Building Metacognitive
Strategies


29%
(n=8 1)

11%
(n=31)

1%
(n=5)

12%
(n=33)

15%
(n=43)

10%
(n=27)

10%
(n=28)

6%
(n=18)

6%
(n=18)


31%
(n=74)

6%
(n=13)

9%
(n=21)

9%
(n=22)

9%
(n=21)

11%
(n=27)

14%
(n=32)

9%
(n=20)

2%
(n=5)


'Total number of instructional strategies observed of Home Base teachers
bTotal number of instructional strategies observed of comparison site teachers








A comparison between both groups of teachers revealed that the Home Base and

comparison site teachers spent almost equivalent amounts of time facilitating learning,

monitoring student work, eliciting student responses, and providing explicit information.

Teachers at the comparison sites used more instructional time for disciplining students

than did teachers at the Home Base School. Both groups of teachers spent slightly less

than one-third of the time providing explicit information.

Instructional Activities of Students

In the same manner as teachers' instructional activities, the activities in which

students engaged during instruction were coded, tallied by individual student, and then

calculated for each group. Behavior was coded for each separate time it was observed.

Students were considered to be attentive to the teacher if they appeared to be listening

quietly and if they followed teacher instructions as given. Students were considered to be

engaged with the task if they worked as instructed, even if the teacher was not lecturing or

demonstrating. Students would often remain on task and work on assignments, even

though they appeared to be inattentive while the teacher was talking. This accounts for

the two separate categories of behavior (attentive to the teacher and engaged with the

lesson) and for the higher number in the category "engaged with lesson." Students were

considered to be inattentive to the teacher if they were talking, playing, or disrupting

others while the teacher was instructing. They were considered to be unengaged with the

lesson if they delayed or refused to do the assigned task. Productive instructional

activities included being attentive to the teacher and being engaged with the task assigned.








Home Base students were more attentive (20%) than comparison site students (9%) and

more engaged (27%) than comparison site students (15%).



Table 4.17. Percentages of Participants' Observed Instructional Activities (and Number
Observed) by Group

Observed Instructional % Observed at Home Base % Observed at Comparison
Activities School Site Schools
(Number Observed) (Number Observed)
(.N = 255) ( = 360)


Productive activities
Attentive to Teacher 20% 9%
(n=51) (n=34)

Engaged with Lesson 27% 15%
(n=70) (n=54)

Unproductive activities
Inattentive to Teacher 22% 36%
(n=56) (n=128)

Unengaged with Lesson 31% 40%
(n=78) (n=144)

aTotal observed instructional activities of Home Base students
bTotal observed instructional activities of comparison site students




At the Home Base School, participants engaged in productive instructional activities 47%

of the time, compared with participants from the comparison site schools who engaged in

productive instructional activities only 24% of the time. Inattention to the teacher and

being unengaged with the work were considered unproductive instructional activities. In

both groups, students engaged in a higher incidence of unproductive instructional








activities than productive activities. The Home Base participants exhibited unproductive

instructional activities 53% of the time, compared with unproductive instructional

activities which were exhibited 76% of the time by participants at the comparison site

schools. Table 4.17 indicates the instructional activities and the number of times each

activity was observed.

Teachers' and Students' Instructional Roles

Teachers were considered to take an active instructional role when they

participated in the students' learning activities. Examples of this participation included

dialogues, demonstrations, or questioning. Teachers were considered to take a directive

role when they simply gave students instructions about what to do without any other

participation in the academic activity. Instructional roles were considered to be

supportive when teachers provided the organization and structure to engage students in

on-task behavior. Supportive roles also included the teacher's verbal encouragement to

students while they were working or assistance as the teacher circulated to answer

questions and provide help while students were working. If the teacher exhibited a

punitive or critical attitude towards student academic endeavors, their role was considered

to be unsupportive. In addition, a teacher's instructional role was termed unsupportive if

he/she distracted the students or failed to provide a quiet, organized atmosphere in which

to work. Occasionally, some teachers would attend to administrative tasks (grading

papers, entering grades, etc.) and ignore the students. These roles were also coded as

unsupportive. Table 4.18 shows the observed instructional roles of teachers by group.










Table 4.18. Percentages of Observed Instructional Roles (and Number of Observed
Roles) for Teachers by Group

Observed Instructional % Observed at Home Base % Observed at Comparison
Roles School Site Schools
(Number Observed) (Number Observed)

Active/Directive (a=86) (.N.b=69)

Active 65% 30%
(n=56) (n=21)

Directive 35% 70%
(n=30) (n=48)

Supportive/Unsupportive _.= 104) (N =70)

Supportive 96% 63%
(n=100) (n=44)


Unsupportive 4% 37%
(n=4) (n=26)

'Total number of observed roles for active/directive parameter at Home Base School
bTotal number of observed roles for active/directive parameter at comparison site schools
'Total number of observed roles for supportive/unsupportive parameter at Home Base
School
dTotal number of observed roles for supportive/unsupportive parameter at comparison
site schools
Note. Eight instances of unsupportive behaviors at the comparison sites were attributed
to interns or substitute teachers. Two instances of unsupportive behaviors at Home Base
School were attributed to an intern.



The behaviors listed in Table 4.18 were considered as two separate parameters.

The active/directive parameter reflected the teacher's level of involvement with the

students. That involvement was ranked as active if the teacher worked with the students








to facilitate comprehension of material. It was ranked as directive if the teacher was

delivering information to students, giving assignments, or otherwise orchestrating

students' work without proximity or personal involvement. At the Home Base School,

teachers took an active instructional role 65% of the time. At the comparison site

schools, teachers were actively involved 30% of the time.

The supportive/unsupportive parameter considered the teacher's role in

facilitating learning. A supportive role was one in which the teacher made sure that

students had every opportunity to do well. For example, the room was quiet and orderly,

materials were available as students needed them, assistance was available if students

needed help, and the teacher supervised student work, An unsupportive atmosphere was

one in which the teacher was uninvolved, not proximate, or totally disengaged from the

student activity. Home Base teachers played a supportive role almost without exception

(96% of observed behaviors). Comparison site teachers played a supportive role 63% of

the time.

It should be noted that of the 26 observed instances of unsupportive instructional

roles in the comparison site schools, almost one-third (n=8) were attributed to substitutes

(n=4) and interns (n=4). Half of the unsupportive instructional roles observed at the

Home Base School (n=2) were attributed to an intern.

Students were also given instructional roles by virtue of the instructional models

selected by the teachers. Students were judged to be self-directive if they had any choice

of activities. If students were told exactly what to do and how to do it, the student role

was judged to be other-directed. Home Base students were more frequently self-directed








(48%) when compared to the comparison site students (14%). Also, the students'

instructional role was evaluated according to whether students had freedom of movement

or restricted movement. In some classrooms, students had considerable flexibility to

move around the room as long as they were staying on task. These students were

considered to have freedom of movement. In other classrooms, students were not able to

move from their seats without teacher permission. These students were considered to

have restricted movement. Students at the Home Base School had considerably more

freedom of movement (46%) than students at the comparison site schools (4%).

Each parameter was considered as a whole when tabulating percentages. In other

words, self-directed/other-directed was considered to be one parameter, the total of which

equaled 100%. The same was true for freedom of movement/restricted movement. Table

4.19 indicates the instructional roles observed for the two groups of students by

percentage and by total number of observed behaviors. Observational data indicated that

students at the Home Base School had more opportunities to make choices about what

they would do and how to do it and had much greater freedom of movement than did

students at the comparison site schools.

Instructional grouping has an impact on learning environment. All of the sites

used combinations of whole group, small group, and individual instruction. In addition,

Home Base School occasionally combined the classes of two or more teachers for special

activities. However, the frequency of use varied between groups. Some teachers

maintained the same grouping for an entire instructional period. Other teachers shifted

often from one group size to another.











Table 4.19. Percentages of Students' Instructional Roles (and Number Observed) by
Student Group

Students' Instructional Roles % Observed at % Observed at
Home Base School Comparison Site Schools
(Number Observed) (Number Observed)

Self-Directed/Other-Directed (Na=81) (b=65)

Self-Directed 48% 14%
(n=39) (n=9)

Other-Directed 52% 86%
(n=43) (n=56)

Free/Resticted Movement (Nc=57) (Nd=55)

Freedom of Movement 46% 4%
(n=26) (n=2)
Restricted Movement 54% 96%
(n=31) (n=53)
aTotal observed instructional roles for self-directed/other-directed parameter at Home
Base School.
bTotal observed instructional roles for self-directed/other-directed parameter at
comparison site schools.
CTotal observed instructional roles for free/restricted movement parameter at Home Base
School.
dTotal observed instructional roles for free/restricted movement parameter at comparison
site schools.



Small group was the predominate instructional organization at the Home Base

School (42%) while whole group instruction was more frequently used at the comparison

site schools (54%). Table 4.20 indicates the frequency of instructional organizations by

group. Percentages and total observed frequencies are reported.










Table 4.20. Percentages of Observed Instructional Organization (and Number Observed)
by Group

Instructional Organization % Observed at % Observed at
Home Base School Comparison Site Schools
(Number Observed) (Number Observed)
(_Na= 99) (Nb= 72)

Combined Classes 3% 0%
(2 or more teachers) (n=3) (n=0)

Whole Group 25% 54%
(n=25) (n=39)

Small Group 42% 25%
(n=42) (n=18)

Individual 29% 21%
(n=29) (n=15)

aTotal observed instructional organizations at Home Base School
bTotal observed instructional organizations at comparison site schools




The leadership of the instructional group is as significant as the instructional

organization. Instructional groups were coded as being led by the teacher, an intern, a

substitute, or a teacher's aide. Table 4.21 indicates the frequency of group leadership by

these respective authority figures,

The major difference in leadership of instructional groups between the Home Base

School and the comparison site schools is found in the number of substitutes and interns

observed in the comparison sites. Twenty-eight percent of instructional groups observed

in the comparison sites were led by either a substitute or an intern. Only one of the








instructional groups observed at the Home Base School was led by an intern. No daily

substitutes were observed at the Home Base School.



Table 4.21. Percentages of Observed Leadership of Instructional Groups (and Number
Observed) by Group

Instructional Group Leader % Observed at % Observed at
Home Base School Comparison Site Schools
(Number Observed) (Number Observed)
(Na= 57) (b= 53)



Teacher-led Group 91% 70%
(n=52) (n=37)

Aide-led Group 7% 2%
(n=4) (n=)

Intern-led Group 2% 19%
(n=) (n= 10)

Substitute-led Group 0% 9%
(n:0) (n=5)

aTotal observed for Home Base School
bTotal observed for comparison site schools




Instructional supports both influence and are influenced by the teaching model

chosen. All of the schools involved in the study used a scripted curriculum for reading

and mathematics, so all classes depended to a large extent on commercially prepared

materials for instructional support and on direct instruction as a teaching model.

However, some teachers modified these instructional supports more than others by

adding teaching models that incorporated teacher-designed or student-designed materials.








During the analysis of data, observations were coded to determine whether the

instructional supports were pre-planned (commercially prepared), teacher-designed, or

student-designed. Obviously, a combination of these materials were often used in a given

lesson.

In addition, the use of instructional supports was coded as either prescribed or

emergent/creative. In prescribed uses, the instructional supports were designed to elicit

responses which were judged as correct or incorrect without an opportunity for students

to create personal meanings or multiple interpretations. In most cases, pre-planned

instructional supports were used in a prescribed manner, although some teacher-designed

materials were also used in this way. For example, a scripted reading lesson following the

teacher's manual, a pre-printed worksheet, and a spelling bee would all be considered as

prescribed use of instructional supports, even though the spelling bee was a teacher-

designed instructional support. Use of instructional supports was coded as

emergent/creative if the teacher allowed students to interact with the teaching materials to

develop personal understanding. Reading and discussing a story, writing an essay, and

performing a play are all examples of materials that are used in a creative or emergent

fashion.

The type and use of instructional supports was coded for each observation and

compiled by group. Table 4.22 indicates the type and use of instructional supports by

group.








Table 4.22. Percentages of Observed Type and Use of Instructional Supports (and
Number Observed) by Group

% Observed at % Observed at
Home Base School Comparison Site Schools
(Number Observed) (Number Observed)

Type of Instructional Support (_N = 108) (Nb = 78)

Pre-Planned 46% 62%
(n=50) (n=48)

Teacher-Designed 40% 29%
(n=43) (n=23)

Student-Designed 14% 9%
(n=15) (n=7)

Use of Instructional Supports (N = 93) N = 68)

Prescribed 54% 81%
(n=50) (n=55)

Emergent/Creative 46% 19%
(n=43) (n=13)

aTotal types of instructional supports observed at Home Base School
bTotal types of instructional supports observed at comparison site schools
'Total uses of instructional supports observed at Home Base School
dTotal uses of instructional supports observed at comparison site schools


Teachers at the Home Base School frequently expanded the commercial curriculum

to include teacher- and student-designed materials (a combined total of 54%), whereas

teachers at the comparison site schools more often relied on commercially prepared

materials (62%). In addition, teachers at the Home Base School more frequently used








instructional supports in an emergent or creative manner (46%) than did the teachers at

the comparison site schools (19%).

Examples of Instructional Strategies

The following excerpts from classroom observations are used to illustrate the

application of various instructional strategies in each site. In the first excerpt, the teacher

is using a direct instruction model.

October 27, 1997: [Home Base School] Mrs. Abbott teaches a combinedfirst

and second grade class at the Home Base School. Today she has brought in several types

of games. She has a Toss-Across game, bean bags, a Light-Bright game, a train station

with train tracks, little race cars, a Topple game, Checkers, and Legos. The teacher

begins by demonstrating tic-tac-toe. Then she brings out the Toss-Across game and

shows the students how to play it. She shows the students how to put together the train

tracks andfinally demonstrates how to play Topple. These are all games for the students

to play when they have finished their work. At the end of each day, students have center

time when they can select what they want to do. The teacher discusses with the students the

rules of the games. They also discuss the rules of sharing, how to care for the toys, and

the behavior she expects from the students.

In the following excerpt, the teacher uses role-playing to enhance her instruction.

March 26, 1998: [Home Base Schooll Mrs. Abbott tells the whole class that she

is going to introduce two important math concepts. She draws geometric shapes on the

board: a circle, a square, a triangle, an oval, a diamond, and a rectangle. As she points

to the shapes, the students call out the name of each shape. She tells students that they