Effectiveness of concrete and computer simulated manipulatives on elementary students' learning skills and concepts in e...

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Effectiveness of concrete and computer simulated manipulatives on elementary students' learning skills and concepts in experimental probability
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Thesis (Ph.D.)--University of Florida, 2001.
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Includes bibliographical references (leaves 146-160).
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by Felicia M. Taylor.

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Table of Contents
    Title Page
        Page i
    Dedication
        Page ii
    Acknowledgement
        Page iii
    Table of Contents
        Page iv
        Page v
    Abstract
        Page vi
        Page vii
    Chapter 1. Description of the study
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
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        Page 15
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    Chapter 2. Literature review
        Page 17
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    Chapter 3. Methodology
        Page 71
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    Chapter 4. Results
        Page 84
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    Chapter 5. Conclusion
        Page 93
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        Page 96
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        Page 98
    Appendix A. Experimental probability instrument
        Page 99
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
    Appendix B. Experimental fraction instrument
        Page 107
        Page 108
        Page 109
        Page 110
    Appendix C. Treatment lessons
        Page 111
        Page 112
        Page 113
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    Appendix D. Teacher consent form
        Page 139
        Page 140
    Appendix E. Parental consent form
        Page 141
        Page 142
        Page 143
    Appendix F. Student consent script
        Page 144
        Page 145
    References
        Page 146
        Page 147
        Page 148
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        Page 150
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    Biographical sketch
        Page 161
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        Page 163
Full Text










EFFECTIVENESS OF CONCRETE AND COMPUTER SIMULATED
MANIPULATIVES ON ELEMENTARY STUDENTS' LEARNING SKILLS AND
CONCEPTS IN EXPERIMENTAL PROBABILITY














By

FELICIA M. TAYLOR


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


2001




















This dissertation is dedicated to my mother,

Laura Ann Roberts,

whose love and belief I was never without

and

to my husband,

Reginald Eugene Taylor,

my mate and friend.













ACKNOWLEDGMENTS


My gratitude is extended to the many people who provided a great deal of support

for me before and during the dissertation process.

To Dr. Thomasenia Lott Adams, my chair and mentor who provided support and

educational challenges, I owe my future contributions as a mathematics educator. I am

thankful to my committee members, each of whom provided guidance throughout the

dissertation process. To Dr. Miller, I owe great thanks for his statistical and professional

advice. To Dr. Sebastian Foti, and Dr. Li-Chien Shen, I owe thanks for their academic

guidance.

I am especially indebted to Dr. Israel Tribble of the Florida Endowment Fund for

Higher Education for supporting me as a McKnight Doctoral Fellow financially and

emotionally.

I am forever grateful to my family, especially my husband Reginald Eugene

Taylor, who was all I needed him to be; my mother, Laura Roberts; my siblings, Carolyn,

Prudence, Eric, and Chantell; and my father-in-law, Eddie Lee Taylor, whom I

considered my father. These very special people have provided me with the love and

support that only family can provide. I owe a continuous effort to improve self and

empower others through education.














TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ............................................. iin

ABSTRACT ........................................................................................................ vi

CHAPTERS

I DESCRIPTION OF THE STUDY ................................................................. I

Introduction .................................................................................................. I
Purpose and Objectives of the Study ........................................................... 12
Rationale for the Study ................................................................................ 13
Significance of the Study ............................................................................ 14
Organization of the Study ............................................................................ 16

2 LITERA TURE REVIEW ............................................................................ 17

Overview ..................................................................................................... 17
Theoretical Fram ework ............................................................................... 17
Constructivism -Grounded Research ........................................................... 22
Concrete Manipulatives for Teaching Mathematics .................................... 44
Technology and Simulated Manipulatives in the Classroom ....................... 56
Probability .................................................................................................. 62
Sum m ary .................................................................................................... 68

3 M ETHODOLOGY ..................................................................................... 71

Overview of the Study ................................................................................. 71
Research Objective ..................................................................................... 71
Description of the Research Instrum ents .................................................... 72
Pilot Study ................................................................................................... 74
Research Population and Sam ple ................................................................. 78
Procedures .................................................................................................. 79
Data Analysis .............................................................................................. 82









4 RESU LTS ...................................................................................................... 84

Other Findings ............................................................................................ 89
Lim itations of the Study .............................................................................. 91

5 CON CLU SION ......................................................................................... 93

Sum m ary .................................................................................................... 93
D iscussion .................................................................................................. 94
Im plications ................................................................................................ 96
Recom m endations ....................................................................................... 97

APPENDICES

A EXPERIMENTAL PROBABILITY INSTRUMENT ................................... 100

B EXPERIMENTAL FRACTION INSTRUMENT ......................................... 108

C TREA TM EN T LESSON S ........................................................................... 112

D TEA CH ER CON SEN T FORM ..................................................................... 140

E PAREN TA L CON SEN T FORM .................................................................. 142

F STU D EN T CON SEN T SCRIPT ................................................................... 145

REFEREN C ES ..................................................................................................... 146

BIO G RA PH ICA L SKETCH ................................................................................. 161













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

EFFECTIVENESS OF CONCRETE AND COMPUTER SIMULATED
MANIPULATIVES ON ELEMENTARY STUDENTS'
LEARNING SKILLS AND CONCEPTS IN
EXPERIMENTAL PROBABILITY

By

Felicia M. Taylor

August 2001

Chairperson: Thomasenia Lott Adams
Major Department: School of Teaching and Learning

This study was designed to investigate the impact of using concrete

manipulatives and computer-simulated manipulatives on elementary students' learning

skills and concepts in experimental probability. Of primary interest to the researcher was

students' ability to predict outcomes of simple experiments. A secondary interest was the

incidental fraction leading that might occur as students engage in the experimental

probability experiences.

The research sample consisted of 83 fifth-grade students. There were four

treatment groups. Treatment Group I students received computer instruction. Treatment

Group II students received manipulative guided instruction. Treatment Group III

students received both computer instruction and manipulative guided instruction.

Treatment Group IV served as a control group and received traditional instruction. The

researcher used an analysis of covariance (ANCOVA). After controlling for initial








differences, the researcher concluded that students experiencing the computer instruction

only significantly outperformed students experiencing a traditional instructional

environment regarding experimental probability learning skills and concepts.














CHAPTER 1
DESCRIPTION OF THE STUDY


Introduction

While the curriculum in the United States continues to focus on basic skills well

past the fourth grade, classrooms in Japan and Germany emphasize more advanced

concepts--including algebra, geometry, and probability (Riley, 1998). The aim of the

United States is to be first in science and mathematics, teachers may need to introduce

children to more advanced topics at earlier grade levels. Principles and Standards for

School Mathematics, published by the National Council of Teachers of Mathematics

(NCTM, 2000), recognizes probability as an advanced topic that should be learned by

students in the K- 12 curriculum. In the Principles and Standards, emphasis is placed on

introducing the same topics and concepts throughout the Pre K-12 curriculum. Young

children need to explore the process of probability. The study of probability in the early

grades provides a stronger foundation for high school students (NCTM, 2000). Bruner

(1960) stated in his discussion of the spiral curriculum,

If the understanding of number, measure, and probability is
judged crucial in the pursuit of science, then instruction in these
subjects should begin as intellectually honestly and as early as
possible in a manner consistent with the child's forms of thought.
Let the topics be developed and redeveloped in later grades.
(pp. 53-54)

He also believed,

If one respects the ways of thought of the growing child, if one is
courteous enough to translate material into his logical forms and








challenging enough to tempt him to advance, then it is possible to
introduce him at an early age to the ideas and styles that in later life
make an educated man. (p. 52)

The foregoing reasons seem to point to the teaching of experimental probability concepts

beginning in the elementary grades.


Probability

"As with other beautiful and useful areas of mathematics, probability has in

practice only a limited place in even secondary school instruction" (Moore, 1990, p. 119).

The development of students' mathematical reasoning through the study of probability is

essential in daily life. Probability represents real-life mathematics. Probability also

connects many areas of mathematics, particularly numbers and geometry (NCTM, 1989).

"Research in medicine and the social sciences can often be understood only through

statistical methods that have grown out of probability theory" (Huff, 1959, p. 11). Moore

(1990) stated,

Probability is the branch of mathematics that describes randomness.
The conflict between probability theory and students' view of the
world is due at least in part to students' limited contact with
randomness. We must therefore prepare the way for the study of
chance by providing experience with random behavior early in the
mathematics curriculum. (p. 98)

An understanding of probability theory is essential to understand such things as politics,

weather reports, genetics, state lotteries, sports, and insurance policies. As shown Table

1, the list of questions presented in the different areas shows the need for experimental

probability. These questions require considerations of probabilities and what they mean

(Huff, 1959).









Table 1

Real Life Mathematics Using Experimental Probability


Example


Description


Politics


Weather Reports


Genetics


State Lotteries





Sports


A random survey of voters show that 381 out of 952 are planning
to vote for Candidate McCain in the primary election. What is
the probability that a randomly selected voter will vote for
Candidate McCain?

The probability of rain today is 70%. What are the odds in favor
of rain? What are the odds against rain?


In genetics, we can use probability to estimate the likelihood for
brown-eyed parents to produce a blue-eyed child. The gene for
brown eyes in humans is dominant over the gene for blue eyes. If
each parent has a dominant brown and a recessive blue gene,
what is the chance that their child will have blue eyes?

A lottery consists of choosing 6 numbers, the first 5 numbers
being one of the digits 0-9 and the sixth number being from 0-
39. How many possible sets of lottery numbers are available for
selection? If the winning numbers are randomly selected, what
is the probability of winning with a single ticket?

Softball statistics (NCTM, 1989, p. I11)


Home runs
Triples
Doubles
Singles
Walks
O uts
Total


Insurance Policies


Above is the record of a player's last 100 times at bat during the
softball season. What is the probability the player will get a
home run? What is the probability that the player will get a hit?

Insurance companies use the principle of probability to
determine risk groups. One way of figuring out what insurance
you need is by looking at the probability of the event and the
financial loss it would cause. What is the probability of men
between 18-25 years of age being involved in a car accident?
Determine if the cost of each occurrence is high or low.


9
2
16
24
11
38
100








The inclusion of activities dealing with experimental probability in the elementary

school enhances children's problem-solving skills and provides variety and challenges for

children in a mathematics program (Kennedy & Tipps, 1994). Current and past

recommendations for the mathematics curriculum identify experimental probability as

one of several critical basic skill areas that should occupy a more prominent place in the

school curricula than in the past (National Council for Supervisor of Mathematics

(NCSM), 1989; Mathematical Sciences Education Board [MSEB], 1990; Willoughby,

1990; NCTM, 2000).

From a historical perspective, members of the Cambridge Conference on School

Mathematics (1963) also acknowledged the role probability and statistics played in our

society. The Cambridge Conference was an informal discussion of the condition of the

mathematics curriculum in the United States at the elementary and secondary level. The

members of the conference addressed revisions to the mathematics curriculum. They

recommended that probability and statistics not only be included as part of the modem

mathematics of that day; but also these were recommendations that they considered for

1990 and 2000. Other researchers have also suggested that elements of statistics and

probability be introduced in the secondary school curriculum and possibly at the

elementary level as part of the basic literacy in mathematics that all citizens in society

should have (Schaeffer, 1984; Swift, 1982).

Therefore, changes are being made today to introduce probability into the

elementary school curriculum (NCTM, 2000). Experience with probability can

contribute to students' conceptual knowledge of working with data and chance (Pugalee,

1999). This experience involves two types of probability--theoretical and experimental.






5

There may be a need for students to be exposed to more theoretical models involving

probability. Theoretical models organize the possible outcomes of a simple experiment.

Some examples of theoretical models may include making charts, tree diagrams, a list, or

using simple counting procedures. For example, when asked to determine how many

times an even number will appear on a die rolled 20 times, students can list the ways of

getting an even number on a die (2, 4, 6) and may conclude that one should expect an

even number one-half of the time when a die is rolled. Then, students can roll the die 20

times, record their actual results and make conclusions based on their experiment.

Experimental models are then the actual results of an experiment or trial. Another

example that involves experimental modeling is the following:

If you are making a batch of 6 cookies from a mix into which you
randomly drop 10 chocolate chips, what is the probability that you will
get a cookie with at least 3 chips? Students can simulate which cookies
get chips by rolling a die 10 times. Each roll of the die determines
which cookie gets a chip. (NCTM, 1989, pp. 110-111)

The researcher's interest was with experimental probability and not on theoretical

probability but used the term probability throughout the study, since there is no

way to teach experimental without theoretical.

A secondary interest with experimental probability involves incidental fraction

learning. Incidental learning is unintentional or unplanned learning that results from other

activities. Proponents of incidental learning believe that effective learning can take place

this way. For example, the incidental learning theory suggests that children learn

mathematics better if it is not methodically taught to them (Clements & Battista, 1992).

In addition, Brownell (1935) suggested that incidental learning could help counteract the

practice of teaching arithmetic as an isolated subject. Studying and solving probability








problems give students many opportunities for practicing and reinforcing previously

learned concepts of basic mathematics skills (Horak & Horak, 1983; NCTM, 2000) that,

according to the Allen, Carlson, and Zelenak (1996), are inadequate. "For example,

fraction concepts play a critical role in the study of probability... as well as whole

number operations and the relationships among fractions, decimals, and percents"

(NCTM, 1989, p. 111).

There are also opponents of incidental learning. These opponents believe that

although the study of probability may provide incidental learning for fractions, it may not

be enough to develop students' ability and concepts of fractions. They also suggest that

incidental learning does not provide an organization for the development of meaningful

concepts and intelligent skills in which the development of authentic mathematic ability

is possible (Brownell, 1935).


Manipulatives

The use of different modes of representation can promote meaningful learning,

retention, and transfer of mathematical concepts (Lesh, 1979). One example includes the

use of manipulatives. Manipulatives may be physical objects (e.g., base ten blocks,

algebra tiles, pattern blocks, etc.) that can be touched, turned, rearranged, and collected.

Manipulatives may be real objects that have social application in everyday situations, or

they may be objects that are used to represent an idea. Manipulatives enhance

mathematics achievement for students at different grade levels (Suydam, 1984). Various

types of manipulatives used for teaching and learning mathematics are presented as

follows: tangrams, cuisenaire rods, geoboards, color tiles, pattern blocks, coins, color

spinners, number spinners, snap cubes, base ten blocks, dice, fraction strips, dominoes,








clock dials, color counters, and attribute blocks. Examples of the mathematics content

for some of the manipulatives are given:

1. Geoboards can be used for activities involving geometric shapes,
symmetry, angles and line segments, and perimeter and fractions in a
problem-solving context.

2. Tangrams can be used for creating and comparing size and shape,
measurement, properties of polygons, and transformations as well as
developing spatial visualization.

3. Color tiles can be used to teach topics such as fractions, percents, and
ratios; probability, sampling, graphing, and statistics; and
measurement.

Students' knowledge is strongest when they connect real-world situations,

manipulatives, pictures, and spoken and written symbols (Lesh, 1990).

Manipulatives help students construct meaningful ideas and learn mathematics more

easily (Bums, 1996; Clements & McMillan, 1996).

Support for the use of manipulatives come from Piagetian theory, in
which cognitive development is described as moving from concrete to
abstract, through a series of developmental stages that are roughly age-
related. A concrete-operational child cannot handle abstract concepts
before arriving at the appropriate stage. However, with manipulatives,
it is possible for such a student to take the first steps towards exploring
the concepts; manipulatives are concrete introductions to abstract
ideas. (Perl, 1990, p. 20)

The use of manipulative materials appears to be of definite importance in how well

children understand and achieve in mathematics in different content areas.


Technology

Technology takes a special place in the child-driven learning environment as a

powerful tool for children's learning by doing (Strommen & Lincoln, 1992). The NCTM

(2000) technology principle indicated that mathematics instructional programs should use








technology to help all students understand mathematics and prepare students to use

mathematics in a growing technological world. The use of computer technology is an

integral part of the vision of the future of teaching and learning mathematics. One of the

position statements of NCTM (1998) is as follows:

The appropriate use of instructional technology tools is central to the
learning and teaching of mathematics and to the assessment of
mathematics learning at all levels. Technology has changed the ways
in which mathematics is used and has led to the creation of both new
and expanded fields of mathematical study. Thus, technology is the
driving change in the content of mathematics programs, in methods for
mathematics instruction, and in the ways that mathematics is learned
and assessed. [On-line]

Inclusion of technology in classroom projects enhances the authenticity of the task

(Greening, 1998).

Technological aids allow greater realism in the classroom (MSEB, 1990).

Progression in technology has increased the boundaries of mathematics and emphasized

the importance of the integration of technology in the mathematics curriculum. At every

grade level, there are ways in which students can experience mathematics with

technology that provide deeper and more substantial understanding (NCTM, 2000).

Children's traditional classroom tools--pencil, notebooks, and texts--are still vital

but inadequate for children to adequately solve problems, completely modify ideas, and

thoroughly extend their learning experience. "Rather than simply listening to teachers

talk, watching the teacher write symbolic procedures on the board, and doing pencil and

paper practice, children should learn through meaningful hands-on activities with

manipulative materials, pictures, and technology" (Weibe, 1988, p. 66). "Computers can

provide an important link in the chain, a connection between the concrete manipulatives

and the abstract, symbolic, paper and pencil representation of the mathematical idea"








(Perl, 1990, p. 21). From a Vygotskian perspective, "appropriately implemented,

technology offers tools to the classroom that may promote high level thinking skills and

support concept development" (Harvey & Charnitski, 1998). Clements (1998) also

believed that the computer can provide practice on arithmetic processes and foster deeper

conceptual thinking.

Computer simulations can help students develop insight and confront

misconceptions about probabilistic concepts. "With the computer and an LCD [Liquid

Crystal Diode] panel such as the PC [Personal Computer] viewer, this process becomes

easier and more powerful" (Perl, 1990, p. 22).

Probability concepts and their meaning depend not only on the level of
theory and on their representation but also the means of working with
them. Tools to represent knowledge or to deal with knowledge have a
sizeable impact on subjects' individual formation of this knowledge.
Clearly, the advent of computers marks a huge change.., the
computer will radically change available means of working on
problems and means of representation. (Kapadia & Borovcnik, 1991,
p. 21)

An advantage of simulation by computer as compared to physical random

generators is the possibility for larger numbers of repetitions and extensive exploration of

assumptions (Kapadia & Borovcnik, 1991). Although this is true, students in the

elementary grades need to work with random events first generated through physical

simulation and then followed by simulations done electronically using available software.

This would lead to deeper understandings as students reach middle and high school

(NCTM, 2000). Moore (1990) stated,

The first step toward mathematical probability takes place in the
context of data from chance devices in the early grades. Computer
simulation is very helpful in providing the large number of trials
required if observed relative frequencies are to be reliably close to
probabilities. (p. 120)









Moore also stated, "Simulation, first physical and then using software can demonstrate

the essential concepts of probability" (p. 126).


Constructivism

The constructivist perspective on learning suggests that knowledge is not received

passively; it has to be built up (Maher, 1991). Constructivists generally agree on the

following basic tenets (Noddings, 1990):

1. All knowledge is constructed. Mathematical knowledge is
constructed, at least in part, through a process of reflective
abstraction.

2. There exist cognitive structures that are activated in the
processes of construction. These structures account for the
construction; that is they explain the result of cognitive
activity in roughly the way a computer program accounts for
the output of a computer.

3. Cognitive structures are under continual development.
Purposive activity induces transformation of existing
structures. The environment presses the organism to adapt.

4. Acknowledgement of constructivism as a cognitive position
leads to the adoption of methodological constructivism.
a. Methodological constructivism in research develops
methods of study consonant with the assumption of
cognitive constructivism.
b. Pedagogical constructivism suggest methods of teaching
consonant with cognitive constructivism (p. 10).

Essentially, students construct knowledge from their own experiences--personal and

academic. One goal from a constructivist perspective is for students to develop

mathematic structures that are more complex, abstract, and powerful than what

students currently possess (Clements & Battista, 1990; Cobb, 1988). Teachers who

adhere to a constructivist perspective emphasize understanding and building on








students' thinking. A comparison of things that teachers with a constructivist

perspective do differently compared to a traditional curriculum is listed in Table 2.


Table 2

Differences Between Two Teaching Styles


Traditional


Constructivist


Emphasis is on basic skills

Teachers follow fixed curriculum
guidelines.

Activities rely heavily on texts
and workbooks

Teachers give students
information; students are
viewed as black slates.

Students mostly work alone.
Teachers look for correct answers
to assess learning.

Assessment is viewed as separate
from teaching and occurs mostly
through testing.


Emphasis is on big concepts

Teachers allow student questions
to guide the curriculum.

Activities rely on primary
sources of data and manipulatives

Teachers view students as
thinkers with emerging theories
about the world.

Students primarily work in groups.
Teachers seek students' points of
view to check for understanding.

Assessment is interwoven with
teaching and occurs through
observation and student exhibits and
portfolios.


(Anderson, 1996)


Concrete manipulatives and computers can be a powerful combination in the

mathematics curriculum (Perl, 1990). There has been a growing need to investigate how

to integrate the use of computers with manipulative materials to facilitate mathematics

instruction and to meet the needs of different learners (Berlin & White, 1986). Aligning

computer usage and concrete manipulatives with the constructivist perspective may help








to improve students' performance in mathematics. For example, manipulative materials

appeal to several senses and are characterized by a physical involvement of pupils in an

active learning situation (Reys, 1971).

In addition, computers also serve as a catalyst for social interaction with other

students--allowing for increased communication (Clements, 1998). Social interaction

constitutes a crucial source of opportunities to learn mathematics (Piaget, 1970).

Involving children in the process of doing mathematics and using concrete materials

relates to their development where children are actively thinking about the mathematics.

While children work with objects and discuss what they are doing, they begin to

recognize a sense of relationship among mathematics concepts (Suydam, 1984) that

support their learning.


Purpose and Objectives of the Study

The purpose of this study was to examine the effectiveness of concrete

manipulatives and computer-simulated manipulatives on elementary students'

experimental probability learning skills and concepts. The objectives of the study were

as follows:

1. To determine the effectiveness of computer-simulated manipulatives on
elementary students' learning skills and concepts in experimental
probability,

2. To determine the effectiveness of concrete manipulatives on elementary
students' learning skills and concepts in experimental probability,

3. To determine the effectiveness of both concrete manipulatives and
computer-simulated manipulatives on elementary students' learning skills
and concepts in experimental probability, and

4. To determine if incidental fraction learning occurs as students engage in the
experimental probability experiences.








Rationale for the Study

The most important use of studying probability is to help us make decisions as we

go through life (Newman, Obremski, & Schaeffer, 1987). For example, in issues of

fairness, students may pose a question based on claims of a commercial product, such as

which brand of batteries last longer than another (NCTM, 2000).


Benefits of Probability Knowledge in Early and Later Grades

The committee for the Goal for School Mathematics, the report of the 1963

Cambridge Conference, recommended introducing basic ideas of probability very early in

the school program. The study of probability allows a learner to make sense of

experiences involving chance.


If Studied in Early Grades, Teachers Need Information

As previously mentioned in the Introduction, Bruner (1960) believed that by

constantly reexamining material taught in elementary and secondary schools for its

fundamental character, one is able to narrow the gap between advanced knowledge and

elementary knowledge. Bruner said that if you wish to teach calculus in the eighth grade,

then begin in the first grade by teaching the kinds of ideas and skills necessary for the

mastery of calculus in later years. This is one of the changes made in the Principles and

Standards--to include the same concepts throughout all grade levels, teaching necessary

skills appropriate at that grade level, and continuing at each grade level. Bruner believed

concepts should be revisited at increasing levels of complexity as students move through

the curriculum rather than encountering a topic only once (Schunk, 1991). This would

also apply to teaching probability. If students are to understand probability at a deeper








level in high school and college, then the skills necessary for its mastery must begin in

the elementary grades (NCTM, 2000). Therefore, it is important that evidence be

available to help teachers develop appropriate topics in the elementary school curriculum.

The kind of reasoning used in probability is not always intuitive, and so it may not be

developed in young children if it is not included in the curriculum (NCTM, 2000).


Lack of Research in Early Grades

In the past, the teaching of probability reasoning, a common and important feature

of modem science, was rarely developed in our educational system before college

(Garfield & Ahlgren, 1988; Bruner, 1960). Currently, probability and statistics are often

included in the secondary school curriculum only as a short unit inside a course

(Shaughnessy, 1992, 1993). Recommendations concerning school curricula suggest that

statistics and probability be studied as early as elementary school (MSEB, 1990; NCTM,

2000). However, much of the existing research on probability is on the conception of

beginning college students or secondary school students (Shaughnessy, 1993). Very little

research in the development of children's understanding of probability concepts has been

done with young children (Watson, Collis, & Moritz, 1997). The learning of probability

in early grades will provide students with a stronger foundation for further study of

statistics and probability in high school.


Significance of the Study

Little research has been conducted about how to teach probability effectively in

the early grades. Much of the research done on probabilistic intuitions of students from

elementary school through college level has been conducted in other countries such as








Europe and Germany (Garfield & Ahigren, 1988). If students in the United States are to

be first in mathematics and science, there must be adequate research with a population of

U.S. students to develop a better understanding of the difficulties these students

encounter in understanding probability and statistics. It is not possible to learn much

about learners unless we learn about learners' learning specific mathematics (Papert,

1980). This study provides information about children's learning of probability.

Many middle and high school students have difficulty understanding how to

report a probability. Inadequacies in prerequisite mathematics skills and abstract

reasoning are part of the problem (Garfield & Ahlgren, 1988). These difficulties may be

due to little or no curriculum instruction for probability given at the elementary school

level. The challenge is to relate to children and engage them in learning experiences in

which they construct their own understanding of probability concepts.

There is a scarcity of literature in terms of teaching and learning probability from

a constructivist perspective at the elementary school level. Recent research efforts

emphasize constructivist approaches to teaching and learning mathematics (Thornton &

Wilson, 1993). One implication for a constructivist theory of knowledge, according to

Confrey (1990), is that students are continually constructing understanding of their

experiences. In addition, this is the first research study conducted using the software

developed by Drier (2000) in whole class instruction. Previous researchers (Clements &

McMillan, 1991; Jiang, 1993) have concluded that computer-simulated manipulatives

enhance students' mathematics learning ability. However, most of those studies involved

older children. This researcher used computer-simulated manipulatives at the elementary

level to teach probability. Because children's thinking processes are much less








developed, there is doubt about whether computer-simulated manipulatives are similarly

effective for young children. Usually when probability is taught, it is done using concrete

manipulatives. Because much of the research on probability learning skills and concepts

has focused on university-level students, additional research is needed to create a

framework from which to understand elementary students' probability learning better.


Organization of the Study

This chapter included the purpose of the study, its rationale, and its significance to

the field of mathematics education. The review of relevant literature presented in

Chapter 2 includes the theoretical framework, studies of probability, studies involving

concrete manipulatives, and studies involving technology with a focus on computer-

simulated manipulatives. Reported in Chapter 3 are the design and methodology of the

study. Chapter 4 contains the results of the analysis and the limitations of the study. A

summary of the results, implications, and recommendations for future research is

presented in Chapter 5.














CHAPTER 2
LITERATURE REVIEW


Overview

In this chapter, the researcher presents a review of the relevant literature. The

researcher focuses on studies pertaining to the following areas: constructivist theory,

concrete manipulatives, studies involving technology with a focus on computer-simulated

manipulatives, and studies of probability.


Theoretical Framework

Constructivism

A theory of instruction, which must be at the heart of educational psychology, is

principally concerned with how to arrange environments to optimize learning and transfer

or retrievability of information according to various criteria (Bruner, 1960). Learning is a

process of knowledge construction, dependent on students' prior knowledge, and attuned

to the contexts in which it is situated (Hausfathers, 1996). This definition is what the

constructivist believes. Constructivism focuses our attention on how students learn.

Constructivism implies that much learning originates from inside the child (Kamii &

Ewing, 1996). The students construct their own understanding of each mathematical

concept. Constructivism appears to be a powerful source for an alternative to direct

instruction (Davis, Maher, & Noddings, 1990). Mathematical learning is an interactive

and constructive activity (Cobb, 1988) that Constructivists in mathematics education








argue that cognitive constructivism implies pedagogical constructivism; that is,

acceptance of constructivist premises about knowledge and knower implies a way of

teaching that acknowledges learners as active knowers (Noddings, 1990). Direct

instruction does not suit children's thinking because it is based quite often on a direct-

teaching and practice model (Broody & Ginsburg, 1990). With direct instruction one

finds a relatively familiar sequence of events: telling, showing, and doing approach

(Confrey, 1990). It moves quickly, often overlooking students' development, preventing

assimilation of higher cognitive skills (Broody & Ginsburg, 1990; Confrey, 1990).

We must help students develop skills that will enable them to be more productive

when faced with real world situations. When students do not have the skills to solve

everyday problems, they have difficulty finding a solution. Owen and Lamb (1996) gave

the following example:

John and Terry worked at a fruit stand during the summer. The two ran
the register, which has a scale to weigh the fruit. One day the fruit stand
lost electricity. The customers wanted to buy bananas, apples, and
tangerines. John and Terry were not sure what to do without any
electricity. They suggested waiting until the owner returned. Terry
saw an old scale in the comer of the store. They decided to use the scale
and figure out the cost of the fruit using paper and pencil. John and
Terry tried to remember formulas taught to them in school earlier that
year but were stumped. The bananas cost 39c per pound and the customer
had 3-1/2 pounds of bananas. One boy suggested that when you have
fruit problems, you divide the money by the weight of the fruit. They
did this, came up with I1 c, and thought that was incorrect because of the
low price. They continued in this manner until they finally thought they
found the right answer. John and Terry's teachers used teaching
techniques that only provided them with minimal skills. Faced with a real
life problem, John and Terry could not rely on skills taught to them by
their teachers.








Constructivist Classrooms

Students learn through a social process in a culture or a classroom that involves

discovery, invention, negotiation, sharing, and evaluation (Clements & Battista, 1990)

and that actively constructs their learning. Social interactions constitute a crucial source

of opportunities to learn mathematics that in the process of constructing mathematical

knowledge involve cognitive conflict, reflection, and active cognitive reorganization

(Piaget, 1970). Vygotsky (1978) had this to say about students' social interaction:

Any function in the child's cultural development appears twice or
on two planes. First, it appears on the social plane, and then on
the psychological plane. First it appears between people as an
interpsychological category, and then within the child as an
intrapsychological category... social relations or relations,
among people genetically underlie all higher cognitive function
and their relationships. (p. 57)

Vygotsky also stated,

An essential feature of learning is that it creates the zone of
proximal development; that is, learning awakens a variety of
internal development processes that are able to operate only when
the child is interacting with people in his environment and in
cooperation with his peers. (p. 90)

To promote meaningful learning, teachers must know how to tailor instruction so

that it meshes with children's thinking (Brownell, 1935; Dewey, 1963; NCTM, 1989;

Piaget, 1970). When children have to give up their own thinking to obey the rules of the

teacher (Kamii & Ewing, 1996), they are forced to compromise their own thinking.

Children compromise their inventive, problem-solving methods for mechanical

applications of arithmetic skills (Carpenter, 1985; Resnick & Omanson, 1987).

Eventually, students may be interested only in getting the correct solution. In a

constructivist classroom, emphasis is not on wrong or right answers; students construct








meaning from preexisting knowledge within the student. Completing objectives or

preparing for tests is not the focus in the constructivist classroom. These do not

sufficiently determine if learning occurred or if there are any changes in a student's

conceptual understanding.

Schulte (1996) described a curriculum unit on weathering and erosion taught in a

constructivist classroom. Students make predictions using hands-on exploration activities

with unexpected outcomes that arouse curiosity. Terms have not yet been introduced.

Students discuss the results of the activities with group members. The teacher poses

questions looking for misconceptions, and activities are sequenced to challenge students.

Once students begin to make connections between activities and understand the concept,

the vocabulary is introduced. Questions focus on application and are used to look for

student understanding. Alternative forms of assessment are used. For example, the

students are to make a book on erosion including pictures, drawings, and text using

landforms affected by erosion. Assessment may also focus on issues in society. For

example, students may be asked to research methods that people are using to stop erosion

in certain areas and design a beachfront home to build, considering the concept of

erosion. Schulte believed that learning depends on the shared experiences of students,

peers, and the teacher. He also felt that collaboration with others is important and that

cooperative learning is a major teaching method used in the constructivist classroom.

Using the constructivist premise would imply there are many roads to most

solutions or instructional endpoints (Noddings, 1990). Rote responses do not represent

students' understanding of concepts. Emphasis on instruction forces us to probe deeper

into students' learning and to ask the following questions (Noddings, 1990):








1. How firm a grasp do students have on the content?

2. What can students do with the content?

3. What misconceptions do students entertain?

4. Even if wrong answers are visible, are students constructing
their learning in a way that is mathematically recognizable?
(p. 14)

These address only a few questions at the heart of constructivist learning and teaching.

Most curriculums pay little or no attention to the developmental abilities of children.

Consequently, children do not learn much of what is taught to them (Brooks, 1987).

In a constructivist classroom there is consensus that the following things are

noticeable (Anderson, 1996; Glatthorn, 1994; Schulte, 1996):

1. Teacher asks open-ended questions and allows wait time for
responses.

2. Higher-level thinking is encouraged.

3. Students are engaged in experiences that challenge hypotheses
and encourage discussion.

4. Classes use raw data, primary sources, manipulatives, physical
and interactive materials.

5. Constructivism emphasizes the importance of the knowledge,
beliefs, and skills an individual brings to the experience of
learning; recognizes the construction of new understanding as a
combination of prior learning, new information and readiness to
learn.

In order for students to establish a strong foundation in mathematics, teachers

need to provide experiences to students that involve forming patterns and relations, which

are essential in mathematics (Midkiff & Thomasson, 1993). Caine and Caine (1991)

posited the following:








Children live with parallel lines long before they ever encounter
school. By the time parallel lines are discussed in geometry, the
average student has seen thousands of examples in fences, windows,
mechanical toys, pictures, and so on. Instead of referring to the
parallel lines students and teachers have already experienced, most
teachers will draw parallel lines on the blackboard and supply a
definition. Students will dutifully copy this "new" information into a
notebook to be studied and remembered for test. Parallel lines
suddenly become a new abstract piece of information stored in the
brain as a separate fact. No effort has been made to access the rich
connections already in the brain that can provide the learner with an
instant "Aha!" sense of what the parallel lines they have already
encountered mean in real life, what can be done with them, and how
they exist than as a mathematical abstraction. (p. 4)

From a constructivist perspective, mathematical learning is not a process of internalizing

carefully packaged knowledge but is instead a matter of reorganizing activity, where

activity is interpreted broadly to include conceptual activity or thought (Koehler &

Grouws, 1992).


Constructivism-Grounded Research

The constructivist teaching experiment is a technique designed to investigate

children's mathematical knowledge and learning experiences (Cobb & Steffe, 1983;

Cobb, Wood, & Yackel, 1992; Hunting, 1983). Several studies in mathematics

education, inspired by the constructivist perspective, provided some information on

children's thinking in mathematics (Bednarz & Janvier, 1988; Carpenter, Moser, &

Rombert, 1982; Ginsburg, 1983; Schoenfeld, 1987).

Bednarz and Janvier (1988) presented the results of a 3-year longitudinal study

using 39 children as they progressed from the first to the third grade. The main purpose

of the study was to develop a constructivist approach to the concept of numeration and its

learning, leading children to build a meaningful and efficient representation of numbers.








In order to evaluate the effects the constructivist approach had on children's

understanding of numeration and difficulties encountered, Bednarz and Janvier were

challenged to understand the way children were thinking. The researchers developed a

constructivist approach that "helped children to progressively construct a significant and

operational system for the representation of number" (p. 329). Of interest to the

researcher was how students carry out operations involving groupings with concrete

materials, and how students communicate representation of numbers. The constructivist

"approach developed required a knowledge of children's thinking (difficulties,

conceptions encountered by them), and continuous analysis of procedures and

representations used by them in learning situations" (p. 302). Quantitative results

revealed that the abilities and procedures developed by the children were still present.

Most of the children using a constructivist approach were able to identify groupings and

relations between them.

Researchers have recommended different teaching strategies to promote students'

meaningful learning (Anderson, 1987; Anthony, 1996; Confrey, 1990; Minstrell, 1989;

Novak & Gowin, 1984). Constructivism commits one to teaching students how to create

more powerful constructions (Confrey, 1990). Confrey wanted to make a model of the

practices of a teacher committed to constructivist beliefs. A Fundamental Mathematics

Concepts class was chosen for the study. The study took place at a SummerMath

program, an experimental summer program for young girls in high school. The focus of

the study was on the teacher-student interactions. There were 11 students in the study

ranging from 9th to 11 th grades. In order to be in this program, students had to score less

than 45% on a multiple choice placement test consisting of 25 items from the high school








curriculum. The students in the study worked in pairs on the curriculum materials

provided each day. The purpose of the work was to suggest that alternative forms of

instruction can exist in mathematics that differ in their basic assumptions from the

tradition of "direct instruction" (p. 122). Data were taken from videotapes of the

interactions. The method used was the idea of reflection in action. From the results,

students were more persistent, more confident, and asked more questions in class.

Follow-up evaluations of the program also indicated that students' scores on the

mathematics part of the Standardized Achievement Test (SAT) improved as a result of

the program.

An important tenet of constructivism is that learning is an idiosyncratic, active,

and evolving process (Anthony, 1996). Anthony presented two case studies to

understand the nature and consequences of passive and active learning behaviors in the

classroom. He believed that "the nature of a student's metacognitive knowledge and the

quality of learning strategies are critical factors in successful learning outcomes" (p. 349).

Many believe that mathematics is most effectively learned through students' active

participation. "Learning activities commonly identified in this manner include

investigational work, problem solving, small group work, collaborative learning and

experiential learning" (p. 350). Passive learning activities included "listening to the

teacher's exposition, being asked a series of closed questions, and practice and

application of information already presented" (p. 350). The purpose of the case studies

was to provide a detailed description of how learning strategies are used in the learning

context and to examine the appropriateness and effectiveness of each of the students'

strategic learning behaviors. Data were collected throughout the school year by the








researcher using nonparticipant classroom observations, interviews, students' diaries,

students' work, and questionnaires. For each case study, three lessons were recorded

using two video cameras, creating a split-screen image of the teacher and student on a

single tape. Each student was requested to view segments of the lesson and relive, as

fully as possible, the learning situation. Although the case studies only illustrated

contrasting learning approaches and acknowledged that much of the data reflected

students' abilities to discuss their learning, the studies offered "an interesting perspective

of learning processes used in the classroom and homework context" (p. 363). In each

case study, the students believed they were pursuing learning in mathematics. The

student in the first case study employed learning strategies appropriate for task

completion rather than the cognitive objective for which the task was designed. The

resulting mathematical knowledge and skills that the student acquired tended to be inert

and available only when clearly marked by context. The student's learning was

constrained by a limited application of metacognitive strategies. When faced with

difficulties, the student provided little evidence that he was able to analyze, evaluate, or

direct his thinking.

"Ineffective use of metacognitive control strategies such as planning, reviewing,

reflection, selective attention and problem diagnosis meant that the student depended on

the teacher and text to provide information and monitor his progress" (p. 364). The

student in the second case study was able to adapt to the task the learning situation, and to

maximize his learning opportunities. His learning environment was organized around

goals of personal knowledge construction rather than goals of task performance. In both








case studies, the students contrasting learning approaches served "to illustrate the manner

in which each student strives to cope in their world of experience" (p. 365).

Creating a learning environment where students have opportunities to negotiate;

to challenge; and to question their own ideas, others' ideas, or the teacher's ideas can

promote students' epistemological understanding about science (Tsai, 1998). This also

may be the case for mathematics. Opportunities for children to construct mathematical

knowledge arise as children interact with both the teacher and their peers (Davis et al.,

1990). Many agree that meaningful learning for students could produce better cognitive

outcomes, allow for more integrated knowledge, and foster greater learning motivation

(Anthony, 1996; Cobb, Wood, & Yackel, 1991, 1992; Zazkis & Campbell, 1996). From

a constructivist perspective, interactive learning with others is significant for effective

learning (Bishop, 1985; Bruner, 1986; Clement, 1991; Cobb, Wood, & Yackel, 1990;

Jaworski, 1992). Several researchers have emphasized that studies of children's learning

should focus on the effects of children's interactions with other children (Cobb, Wood &

Yackel, 1991; Tzur, 1999; Wiegel, 1998; Yackel, Cobb, Wood, Wheatley, & Merkel,

1990).

Science does not arise from a vacuum; rather, it comes from a complex interaction

between social, technological, and scientific development (Tsai, 1998, p. 486). Tsai

(1998) researched the importance of presenting the constructivist philosophy of science

for students. He conducted a study to acquire a better understanding of the interaction

between scientific epistemological beliefs and learning orientations in a group of

Taiwanese eighth-grade students. Twenty eighth graders in a large urban junior high

school were used in the study. Subjects were chosen using the following criteria: They








were above-average achievers, and they expressed a strong certainty and clear tendency

regarding scientific epistemological beliefs (SEB) based on questionnaire responses. The

questionnaire consisted of bipolar agree-disagree statements on a 5-1 Likert scale,

ranging from empiricist to constructivist views about science. A qualitative analysis

through interviewing the subjects revealed that students holding constructivist

epistemological beliefs about science (knowledge constructivists) tended to learn through

constructivist-oriented strategies when learning science, whereas students having

epistemological beliefs, more aligned with empiricism (knowledge empiricists), tended to

use more rote-like strategies to enhance their understanding. Knowledge constructivist

subjects tended to have a more realistic view about the value of science, and their interest

and curiosity about science mainly motivated them, whereas knowledge empiricist

subjects were mainly motivated by performance on examinations. Although the study

was not conducted using an experimental design, Tsai believed the results strongly

suggested that "students' SEB played a significant role in students' learning orientations

and how they organize scientific information" (pp. 485-486). The growth of science

results from "interaction between science, technology, and society" (p. 486).

Zazkis and Campbell (1996) were concerned with the concept of divisibility and

its relation to division, multiplication, prime and composite numbers, factorization,

divisibility rules, and prime decomposition. They used a constructivist-oriented

theoretical framework for analyzing and interpreting data acquired in clinical interviews

with preservice teachers. The participants were 21 preservice elementary school

teachers. The actions in the study included the following:

1. Examining preservice teachers' understanding of elementary
concepts in number theory.









2. Analyzing and describing cognitive strategies used in solving
unfamiliar problems involving and combining these concepts.

3. Adapting a constructivists-oriented theoretical framework.

Individual clinic interviews were conducted that probed participants' understanding of

number theory concepts. The instrument was designed to reveal the participants' ability

to address problems by recall or construction of connections within their existing content

knowledge. "The results of the study suggested that in the schooling of the participants

involved in this study, insufficient pedagogical emphasis has been placed on developing

an understanding of the most basic and elementary concepts of arithmetic" (p. 562).

Cobb, Wood, and Yackel (1991) conducted a teaching experiment, where their

primary focus was on understanding students cognitive development. The instructional

activities included teacher-directed whole class activities and small group activities. In

1 -hour sessions, the children worked in groups for 25 minutes, whole class instruction for

20 minutes, and 15 minutes for students to discuss with the class solutions and answers.

During these instruction times, the teacher constantly moved about the class observing

and frequently interacting with the students as they engaged in mathematical activities.

The noise level was generally higher when the students were working with their partners.

The teacher used a nonevaluative approach when students gave answers and solutions,

even if their answer or solution was incorrect. Two video cameras were used to record

the mathematics lessons for the school year. Analysis of "whole class dialogues and

small group problem solving interactions focused on the quality of the children's

mathematical activity and learning as they tackled specific instructional activities" (p.

161). The teacher's observations and analysis guided the development of instructional








activities. The teacher's purpose for whole class discussions was to encourage the

children to verbalize their solution attempts. "The obligations and expectations mutually

developed during whole class discussions also provided a framework for the children's

activity as they worked in small groups, in that they were expected to solve problems in a

cooperative manner and to respect each others' efforts" (p. 168). The children's level of

conceptual understanding of mathematics influenced the social relationships that the

children negotiated. Cobb, Wood, and Yackel concluded that the teaching experiment

was reasonably successful. The children were able to solve mathematic problems in

ways that were acceptable to them. "It was also apparent that the children's abilities to

establish productive social relationships and to verbalize their own thinking improved

dramatically as the year progressed" (p. 174). The researchers noticed the enthusiasm

and persistence in the children. They also noticed that when the children were given

challenging problems, they did not become frustrated but experienced joy working with

those problems.

In 1992, Cobb, Wood, and Yackel explored the relationship between individual

learning and group development of three 7-year-old students engaged in collaborative

small group activity. They conducted a quantitative comparison of the control and

treatment of students' arithmetic achievement, beliefs, and personal goals. There was no

individual paper and pencil assignment and no grading of the students' work. Children

began instructional activities by working collaboratively in groups followed by teacher-

initiated discussions of mathematical problems, interpretations, and solutions. The

materials and instruction strategies reflected "the view that mathematical learning is a

constructive, interactive, problem solving process" (p. 99). The students learned as they








interactively designed situations for justification or validation, producing a controlled

solution method.

A follow-up study was conducted with inner-city students with a predominately

minority population. Cobb, Wood and Yackel found it necessary to conduct a

quantitative comparison of arithmetical achievement, beliefs, and personal goals of the

students. They found favorable results particularly with "students' conceptual

development and problem solving capabilities in arithmetic, their perceptions of the

reality of classroom life, and their personal goals as they engaged in mathematical

activity" (p. 100). The children learned in classroom situations "as they interactively

constituted situations for justification or validation" (p. 119).

Tzur (1999) conducted a constructivist teaching experiment with two fourth-grade

students on the relationship of teaching and children' construction of a specific

conception of their fraction knowledge. In a 3-year teaching experiment that began in the

third grade and ended in the fifth grade, the author addressed the fourth-grade year in this

study. Studying of fractions through teaching and learning contributed to the learning

process in children's learning and the author's learning as the researcher-teacher. "The

children's work during the study fostered an important transformation in their thinking

about fractions" (p. 414). Teaching episodes were videotaped during the students' fourth-

grade year. What generally took place in the teaching episode was children completing

task to solve in Sticks. The researcher defined Sticks as a collection of possible actions

that might be used to establish and modify fraction schemes. Students also were given

opportunities to pose problem tasks to each other. The researcher realized that by

allowing students to work this way enhanced their fraction schemes.








Wiegel (1998) conducted a study to investigate collaborative work with pairs of

kindergarten students while they worked on tasks designed to promote early number

development. The participants were 10 students in a public elementary school. The

students in the study were selected based on the following: ability to work with someone,

willingness to work with the researcher, and counting development skills. The 6-month

study consisted of interviews with individual students and of teaching sessions with pairs

of students. Students were interviewed three times during the study. The purpose of the

interviews was to address student's understanding of standard number-word sequences

and their ability to recognize and represent spatial patterns. The interviews were

videotaped and audiotaped in the hallway outside the classroom. The teaching sessions

involved activities focusing on the order of the number-word sequence, counting, and

visual and auditory patterns. The teacher's role was to pose problems and facilitate the

groups of students. As the sessions progressed, all students made some progress toward

forms of organization requiring more coordination and toward increased involvement in

the partner's actions. "Working in pairs supported and enhanced the students' cognitive

development and promoted more sophisticated ways of social interaction" (p. 223).

Wiegel also noted that the interactive working condition where students were paired

homogeneously instead of heterogeneously provided learning opportunities different

from whole-class, one-to-one teaching situations, or in small heterogeneous group

settings. "For students who were able to reflect on and anticipate their actions, working

in pairs led to cooperative ventures in which they solved counting tasks they were unable

to solve alone" (p. 223).








Leikin and Zaslavsky (1997) discussed effects on different types of students'

interactions while learning mathematics in a cooperative small-group setting. The study

was conducted in four low-level ninth-grade classes consisting of 98 students. The main

mathematics topic in the study was quadratic functions and equations, which is part of the

Israeli secondary school mathematics curriculum. The topic was divided into six units.

Each unit began with a whole class introductory lesson. The unit ended with a seventh

lesson in which a unit test was given to all students. The four classes were divided into

two groups (group la & lb and group 2a & 2b) for comparison:

1. Students in group 1 a learned all the material according to the experimental
cooperative learning method.

2. Students in lb served as a control group, learning all the material the
conventional way.

3. In the second group, students in 2a and 2b learned by both methods,
changing from one to another by the end of each unit.

The researchers collected data by observation, students' written self-reports, and an

attitude questionnaire. Leikin and Zaslavsky noticed the following: (a) an increase in

students' activeness, (b) a shift toward students' on task verbal interaction, (c) various

opportunities for students to receive help, and (d) positive attitudes toward the

cooperative experimental method. The results favored the experimental small-group

cooperative method.

A number of studies also have documented that students can draw on their

informal knowledge to give meaning to mathematical symbols when problems

represented symbolically are closely matched to problems that draw on students informal

knowledge (Fennema, Franke, Carpenter, & Carey, 1993; Lampert, 1986, 1990; Mack,

1990, 1995; Saenz-Ludlow & Walgamuth, 1998; Streefland, 1991). Saenz-Ludlow and








Walgamuth (1998) analyzed the interpretations of equality and the equal symbol of third-

grade children who participated in a year-long, whole-class socio-constructivist teaching

experiment. A socio-constructivist classroom teaching experiment was conducted

primarily to understand students' conceptual constructions and interpretations. One of the

major goals of this teaching experiment was to analyze the influence of social interaction

on children's progressive understanding of arithmetical concepts. All of the students at

the elementary school in which the teaching experiment took place were considered at-

risk students. Twelve third-grade students participated in the study. Lessons were taped

daily, and field notes of children's solutions were kept to analyze progressive activity and

changes in the children's cognition. "The researcher's daily presence in the classroom

allowed first-hand observation of the unfolding interaction and meaning-making

processes of the students and teacher" (p. 158). The instruction these students received in

first and second grades was characterized as traditional. The researchers believed that the

teachers in the first and second grades emphasized performance and gave little

consideration to the idiosyncratic ways in which children thought about numbers and

mathematical symbols. "At the beginning of the third grade, even when the students

could perform the addition algorithm correctly, they could give no justification for any of

its steps indicating their lack of understanding of place value" (p. 162). The researchers'

first objective was to remove students' procedural learning of operating with numbers by

supporting their understanding of place value through activities with money exchanges

and the packing of beans. When students were given the freedom to operate with

algorithms, they were able to develop strategies different from conventional ones. At the

completion of the study, Saenz-Ludlow and Walgamuth observed the following:








1. The children symbolized equality in different ways.

2. The dialogues indicate that these children initially interpreted
the equal symbol as a command to act on numbers.

3. The dialogues also indicate the crucial role of classroom
discussions on the children's progressive interpretations of the
equal symbol.

4. The dialogues and the arithmetical tasks on equality indicate
these children's intellectual commitment, logical coherence,
and persistence to defend their thinking unless they were
convinced otherwise.

5. The teacher's adaptation to the children's current knowledge
and understandings was a determining factor in sustaining the
children's dialogical interactions.

6. These children's dialogues raise our awareness of the cognitive
effort entailed in the interpretation of and the construction of
mathematical meanings from the conventional symbols, in
particular the equal symbol. (pp. 184-186)

The children in the study expanded their conceptualizations of equality due to their active

role in class discussions, the arithmetical tasks that took into account children's

difficulties, and the teacher's ability to obtain a balance between teaching and the freedom

to learn.

Mack (1995) conducted a study to examine the development of students'

understanding of fractions during instruction. Four third-grade and three fourth-grade

students received individualized instruction on addition and subtraction of fractions in a

one-to-one setting for 3 weeks. The researcher, as teacher, gave instructions on a one-to-

one setting. Each session lasted 30 minutes during regular school hours. Mack met with

the students six times each for the 3-week period. The sessions, which included clinical

interviews, were audiotaped and videotaped. The author was interested in helping

students make connections mainly by posing problems and asking questions. Students








were given problems verbally and encouraged to think aloud while solving the problems.

The results of the study led to the following conclusions:

1. Although students can draw on their informal knowledge to
give meaning to mathematical symbols, they may not readily
relate symbolic representations to informal knowledge on their
own, even when problems presented in different contexts are
closely matched.

2. Students' ability to relate symbols to their informal knowledge
is influenced by their prior knowledge of other symbols.

3. As students moved from working with symbolic
representations involving only proper fractions to working
with representations involving both whole numbers and
fractions, students explained the whole numbers represented
symbolically in terms of inappropriate fractional quantities.
(p. 438)

As students attempted to construct meaning for symbolic representations of fractions,

they overgeneralized the meanings of symbolic representations for whole numbers to

fractions, and they overgeneralized the meanings of symbolic representations for

fractions to whole numbers.

Language and symbols play a role in constructivist accounts of mathematical

development (Bednarz, Janvier, Poirier, & Bacon, 1993; Confrey, 1991; Kaput, 1991;

Pirie & Kieren, 1994; Siegel, Borasi, & Fonzi, 1998). Siegel and others (1998) addressed

the specific function that reading in conjunction with writing and talking can serve in

mathematical inquiries to understand how inquiry experiences can be used in

mathematics education. The participants in the study included a reading and mathematics

educator and a group of secondary mathematics teachers. Data collection included

teachers' written plans, photocopies of instruction materials and students' work, teachers'

anecdotal records of what occurred during selected lessons, and audiotapes and








videotapes of classroom experiences. Based on the results, the researchers suggested that

reading can serve multiple roles in inquiry-based mathematics classes and provide

students with unique opportunities for learning mathematics. They also concluded that

reading can make an important contribution to students' engagement in mathematical

inquiries.

Constructivist perspectives on learning have been central to an empirical and

theoretical work in mathematics education (Cobb, 1995; Simon, 1995; Steffe & Gale,

1995; von Glassersfeld, 1991) and, consequently, have contributed to shaping

mathematics reform efforts (NCTM, 1989, 1991). Understanding learning through

individual and social construction gives teachers a conceptual framework to understand

the learning of their students (Cobb, Wood, Yackel, Nicholls, Wheatley, Trigatti, &

Perlwitz, 1991; Simon, 1991).

Cobb (1995) investigated the role that four second graders' use of particular

instructional devices (e.g., hundreds boards) played in supporting their conceptual

development based on reform recommendations of NCTM (1989). Cobb looked at

students' use of the hundreds board for two reasons: (a) to observe the role instructional

devices might have in supporting children's construction of numerical concepts and (b) to

look at theoretical differences between constructivist and socialcultural conceptual

development. "The investigation of children's use of the hundreds board provides an

opportunity to explore the relationship between psychological constructivist and

sociocultural accounts of mathematical development" (p. 363). The investigation took

place in a second-grade classroom in the course of a year-long teaching experiment.

Modes of children's early number learning were used to develop instructional activities.








The instructional strategy used was "small-group problem solving followed by a teacher-

orchestrated whole-class discussion for children's interpretations and solutions" (p. 365).

Videotapes of the mathematics lessons were done for an entire year. Manipulatives (e.g.,

hundreds boards) were made available to the children in all lessons involving arithmetical

computation and problem solving. Students generally decided what available materials

might help in solving a problem. The analysis Cobb presented focused on "individual

children's cognitive construction of increasingly sophisticated place-value numeration

concepts" (p. 379). The analysis indicated that the children's use of the hundreds board

did not support the construction of increasingly sophisticated concepts of 10. "Children's

use of the hundreds board did appear to support their ability to reflect on their

mathematical activity once they had made this conceptual advance" (p. 377).

Simon (1995) presented data on a whole-class, constructivist teaching experiment

in which problems of teaching practice required the teacher/researcher to explore the

pedagogical implications of his theoretical (constructivist) perspectives. The 3-year

study of the mathematical and pedagogical development of potential elementary teachers

was part of a Construction of Elementary Mathematics (CEM) Project teaching

experiment. Simon was interested in ways to increase these potential elementary

teachers' mathematical knowledge and aid in their development of views of mathematics,

learning, and teaching. There were 26 potential elementary school teachers involved in

the study. Data were collected from a mathematics teaching and learning course, a 5-

week prestudent-teaching practicum, and a 15-week student-teaching practicum taken by

the participants in the study. The researcher took notes and videotaped the classes.

There were no lecture class lessons, only small-group problem solving and teacher-led








whole-class discussions. The mathematical content of the mathematics teaching and

learning course began with exploration of the multiplicative relationship involved in

evaluating the area of rectangles. Simon believed that an advantage of the teaching

experiment design was that time was built into the project to reflect on the understandings

of the students. This teaching episode seemed to emphasize that the teacher did not

create disequilibrium.

The success of such efforts is in part determined by the adequacy
of his model [meaning the teacher] of students' understanding. It
also seems to support the notion that learning does not proceed
linearly. Rather, there seem to be multiple sites in one's web of
understandings on which learning can build. (p. 140)

Cobb, Wood, Yackel, Nicholls, Wheatley, Trigatti, and Perlwitz (1991) also

conducted a study on students' response to constructivism in the classroom. They were

interested in students' computational proficiency, conceptual development in arithmetic,

personal goals in mathematics, and beliefs about reasons for success in mathematics. The

instructional approach used in this teaching experiment was compatible with the

constructivist view. The year-long study consisted of 338 second-grade students. The

treatment group consisted of 187 students, and the remainder of the students were in the

control group. The instructional activities designed to facilitate constructions of thinking

strategies included activities from What's My Rule (Wirtz, 1977) and War (Kamii, 1985).

At the end of the school year, two tests were administered to the study groups. One test

was a state-mandated multiple choice standardized achievement test (ISTEP) and the

second was a Project Arithmetic Test developed by the researchers. An ANOVA was

conducted to determine the achievement of the treatment and control groups on the

ISTEP and Project Arithmetic Test. "The comparison of students' performance on both








the ISTEP and Project Arithmetic Test indicated that the treatment students developed a

higher level of reasoning in arithmetic than the control group" (p. 21). There was an

indication that the teachers in the treatment group were successful partly because of their

"development of classroom mathematics traditions in which their students could publicly

express their thinking without risk of embarrassment" (p. 23). Students in the treatment

group believed that success in mathematics stemmed from developing their own methods

for solving problems. In contrast, students in the control group believed success was

determined by using the same solution methods as the teacher or other students.

Nicholls, Cobb, Wood, Yackell, and Patashnick (1990) conducted a study on

children's response to constructivism in the classroom. They were interested in second-

grade children's reasons for success in mathematics. A series of questionnaires were

administered to six second-grade classes to establish whether children's beliefs about the

causes of success in mathematics were meaningfully related to their personal goals in

mathematics. In conjunction with student's theories of success in school mathematics,

the researchers were interested in measures that would determine how motivational

influences of different mathematics teaching practices influenced students' success in

mathematics. Only one class used teaching practices compatible with the NCTM (1989)

recommendation that emphasis should be on establishing a climate that places critical

thinking at the core of instruction. All statements should be subject to question, and both

teacher and children should be open to reaction and elaboration from others in the

classroom. The teaching practice consistent with a constructivist tradition in education

was observed in class one. Class one was the only group consistent with either the

NCTM recommendation or an approach to mathematics teaching compatible with








constructivism. In class one, after tasks were posed, students worked cooperatively in

pairs to complete the tasks.

"One implication of constructivism is that mathematics, including arithmetical

computation, should be taught through problem solving" (Cobb, Wood, & Yackel, 1991,

p. 158). Franke and Carey (1997) conducted a study to investigate first-grade children's

perceptions of mathematics in problem-solving environments. Two questions were

addressed in the study:

1. What are children's perspectives on what it means to do mathematics in
problem solving classrooms?

2. Do children in problem-solving classrooms with different
demographic characteristics hold similar perspectives about what it means
to do mathematics?

Individual interviews were conducted with 36 first-grade children. Six children,

three girls and three boys, were randomly selected from each of six classrooms. The

researchers were interested in determining children's perceptions about mathematics in

problem-solving environments. Open-ended questions were asked to determine

children's perceptions of what doing mathematics entailed. "The combination of open-

ended, context-driven questions and children's ability to talk about their thinking allowed

us more insights into young children's perceptions about mathematics" (p. 22). When

asked about the use of manipulatives when doing mathematics, children responded that

manipulatives were helpful in solving problems. The children recognized and accepted a

variety of solution strategies, with many of the children valuing all solutions equally and

assuming shared responsibility with the teacher and their peers for their mathematics

learning. Children had varying perceptions of what it meant to succeed in mathematics.








The children in the study have helped the researchers to "see how critical it is to begin to

understand children's perceptions of what it means to engage in mathematics" (p. 24).

Wood and Sellers (1996) conducted a class-by-instruction factorial design used to

compare students in problem-centered classes for 2 years with students in problem-

centered classrooms for 1 year, and with students in textbook classes for 2 years on a

standardized achievement test. Also, the authors studied classes using problem-centered

instruction for 2 years and compared them with students in problem-centered classes for

1 year on an instrument designed to assess students' conceptual development in

arithmetic. Six classes received problem-centered mathematics instruction for 2 years in

second and third grades. This instruction was generally reflective of a socioconstructivist

theory of knowing and compatible with recommendations for reform in mathematics

education. The researchers used an instrument designed to examine personal goals and

beliefs about reasons for success in mathematics. Two tests were given to students to

evaluate their arithmetic learning, the Arithmetic Test and the Personal Goals and Beliefs

questionnaire. The results indicated that after 2 years of instruction in reform-based

classes, students scored significantly higher on standardized measures of computational

proficiency as well as conceptual understanding and they understood arithmetic concepts

better than those in textbook-instructed classes. The students in the study believed "that

it is important to find their own or different ways to solve problems, rather than conform

to the method shown by the teacher" (p. 351).

One's representation of space is not a perceptual "reading off' of the spatial

environment but is built up from prior active manipulation of the environment (Battista,

Clements, Amoff, Battista, & Van Auken Borrow, 1998; Clements & Battista, 1992;








Clements, Swaminathan, Hannibal & Sarama, 1999). Battista and others (1998)

examined students' structuring and enumeration of two-dimensional (2D) rectangular

arrays of squares. The authors interviewed 12 second graders during the fall. Neither the

research team nor the students' classroom teacher instructed students on enumerating

rectangular arrays during the school year. The interviews were videotaped. After

demonstrating how a plastic square inch tile fit exactly on a graphically indicated square

given in a rectangle drawing, students were asked to do the following: (a) predict how

many squares were in the rectangle, (b) draw where they thought the squares would be

located on the rectangle, (c) then predict again how many squares would be needed, and

(d) cover the rectangle with square tiles and determine again the number of squares

needed. The results indicated that many students' understanding of the row-by-column

arrays was unclear.

In the traditional view of learning, it is assumed that row-by-
column structure resides in two-dimensional rectangular arrays
of squares and can be automatically apprehended by all.
However, as we have seen in the present study, and consistent
with a constructivist view of the operation of the mind, such
structuring is not in the arrays--it must be personally constructed
by each individual. (p. 531)

In a previous study focusing on three-dimensional arrays, Battista and Clements

(1996) were only able to see consequences of a student's ability to perform coordinating

actions. In their study, the researchers not only were able to see the consequences of a

student's ability or inability to perform sequence of coordination actions used to organize

a set of objects but they also were able to observe these actions, which gave them a better

understanding of their characteristics.








Clements, Swaminathan, Hannibal, and Sarama (1999) investigated criteria

preschool children used to distinguish members of a class of shapes from other figures.

The participants in the study were 97 middle-class children ages 3 to 6 from two

preschools and one elementary school with two kindergarten classes. The researchers

investigated young children's methods used to distinguish geometric shapes common in

the social-cultural environment. Data were collected using clinical interviews and paper-

and-pencil tasks in a one-to-one setting. Children identified circles with a high degree of

accuracy. Six-year-olds performed significantly better than did the younger children.

Battista (1999) used psychological and sociocultural components of a

constructivist paradigm to provide a detailed analysis of how the students cognitive

constructions are used to enumerate three-dimensional arrays of cubes developed and

changed in an inquiry-based problem-centered mathematics classroom. There were six

fifth-grade students in the study. The study took place over 4 weeks for 1 hour per day

covering a unit on volume. The students had to find a way to predict correctly the

number of cubes that would fill boxes described by pictures, patterns, or words. Each

student in the study was given individual activity sheets to record answers. The teacher

walked around the classroom, observing students interacting in pairs and encouraging

collaborative communication. Students made predictions and checked their predictions

by making paper boxes and filling them with cubes. "Having students first predict then

check their predictions with cubes was an essential component in their establishing the

viability of their mental models and enumeration schemes" (p. 442). This study showed

how powerful learning can occur in problem-centered inquiry-based instruction. Cobb

noted the following implications:








Using the constructivist theory of abstraction permits an
elucidation of students' need for repeated and varied opportunities
to properly construct difficult concepts. Use of this theory also
provides those insights into students' learning required for
effective curriculum development and instruction because it
describes precise constructive itineraries students follow in
acquiring particular mathematical ideas. Second, instructional
materials designed to promote intrapersonal (vs. interpersonal)
cognitive conflict can be very effective in engendering
accommodations and the resulting mathematics learning.
(Battista, 1999, pp. 447-448)


Concrete Manipulatives for Teaching Mathematics

Many mathematics and science teachers in elementary and middle schools use

manipulatives with varying degrees of success. Educators have argued that concrete

manipulatives can help develop and enhance math problem solving and critical thinking

skills, decrease anxiety, and increase concentration (Cuisenaire, 1992; Davidson, 1990;

Langbort, 1988; Marzola, 1987; Ohanian, 1992; Scheer, 1985; Stone, 1987; Taylor &

Brooks, 1986).

"One does not come to know a tool through a description of it, but only through its

activity" (Confrey, 1990, p. 110). Reys (1971) gave the following suggestions on how

concrete manipulatives can enhance the learning of students at all levels:

1. To vary instructional activities

2. To provide experiences in actual problem solving
situations

3. To provide a basis for analyzing sensory data, necessary in
concept formation

4. To provide an opportunity for students to discover
relationships and formulate generalizations

5. To provide active participation by pupils








6. To provide for individual differences

7. To increase motivation related, not to a single mathematics
topic, but to learning in general. (p. 555)

Burns (1996) suggested other common uses of concrete manipulatives:

1. Manipulatives help make abstract ideas concrete.

2. Manipulatives lift math off textbook pages.

3. Manipulatives build students' confidence by giving them a
way to test and confirm their reasoning.

4. Manipulatives are useful tools for solving problems.

5. Manipulatives make learning math interesting and
enjoyable. (p. 46)

Reys (1971) also suggested the following basic tenets for using manipulative materials in

learning mathematics:

1. Concept formation is the essence of learning mathematics.

2. Learning is based on experience.

3. Sensory learning is the foundation of all experiences and
thus the heart of learning.

4. Learning is a growth process and is developmental in
nature.

5. Learning is characterized by distinct, developmental
stages.

6. Learning is enhanced by motivation.

7. Learning proceeds from the concrete to the abstract.

8. Learning requires active participation by the learner.

9. Formulation of a mathematical abstraction is a long
process. (p. 552)








In addition to Reys and Bums' list of ways to use manipulatives. Yeatts (1991)

suggested other ways manipulative materials could help:

1. To introduce a new mathematical concept

2. To reinforce previous learning

3. To provide concrete representations of abstract ideas

4. To provide for individual learning styles

5. To foster creative thinking processes

6. To provide experiences in problem solving situations

7. To provide opportunities for students to become active
participants in their own learning experiences

8. To provide opportunities for students to exchange
viewpoints with their classmates

9. To diversify the educational activities

10. To enhance interest and motivation for learning new
concepts. (p. 7)

In a meta-analysis of 60 studies from kindergartners to college-age students,

Sowell (1989) found that long-term use of concrete manipulative material increased

mathematics achievement and improved students' attitudes toward mathematics, when

instruction using concrete material was presented by teachers knowledgeable about their

use.

Many of the studies favoring the use of concrete material took place more than 20

years ago (Allen, 1978; Babb, 1976; Bledsoe, Purser, & Frantz, 1974; Bolduc, 1970;

Bring, 1972; Brown, 1973; Callahan & Jacobson, 1967; Derderian, 1980; Earhart, 1964;

Ekman, 1967; Fuson, 1975; Johnson, 1971; Letteri, 1980; Nichols, 1972; Nickel, 1971;

Purser, 1974; Robinson, 1978; Romero, 1979; Stockdale, 1980; Tobin, 1974; Toney,








1968; Trueblood, 1968; Wallace, 1974; Wheatley, 1979). There have only been a few

current studies found on the use of concrete manipulatives (Baxter, Shavelson, Herman,

Brown, & Valadez, 1993; Burton, 1992; Canny, 1984; Chester, Davis, & Reglin, 1991;

Gardner, Simons, & Simpson, 1992; McClung, 1998). It seems apparent that there is a

gap in the literature on studies on the use of manipulatives and their effects on students'

learning. This may be because manipulatives used to teach mathematics have been

stressed many times and in different contexts, where it is assumed that using

manipulatives in teaching mathematics is apparent (Behr, 1976).

Baxter and others (1993) were interested in the performance assessment and their

effects on students in diverse groups. They developed a study with 105 Latino and Anglo

sixth graders in two types of mathematics curriculum--hands-on and traditional. In the

hands-on group of 40 students (13 Latino and 27 Anglo), the curriculum consisted of

problem solving using manipulatives.

The instruction in the traditional group of 65 students (43 Latino and 22 Anglo)

consisted of a textbook and worksheet curriculum. Performance assessment was

comprised of several tasks, including measurement, place value, and probability. Due to

time constraints, the hands-on group received only the measurement and place value

assessment. The traditional group received all three assessments. The performance

assessment consisted of describing objects, measuring length, and finding the area. The

researchers concluded that the mean differences on the two performance assessments,

where students with varying instructional histories could be compared, showed that

students who had received hands-on mathematics instruction scored higher, on average.

than students in the traditional curriculum. When comparing the groups by ethnicity, the








average scores for the Anglo students were higher than Latino students on all

achievement measures. The extent of the difference varied, based on the instructional

history of each student. The researchers concluded that assessments on mathematics

performance relating closely to hands-on instructional activities provided reliable

measures of mathematics achievement.

The manipulatives and strategies that young children would use if given free

choice were of interest to Burton (1992). In the study, Burton explored young children's

understanding of division. The subjects used in the study were 117 second-graders from

five second-grade classrooms, three in one school and two in another school. The testing

environment for the students in the study consisted of individual interviews conducted in

the hallway outside the classroom. Each student was given an opportunity to explore the

testing kit (manipulatives) before the interview began. The researcher was interested in

how students would solve unfamiliar mathematical problems. Twelve problems

presented one at a time were read and shown to the child during interview. The child

decided which manipulative and strategy he or she wanted to use. The child's strategy

and choice of manipulative were recorded for each problem. Each interview, on the

average, lasted approximately 23 minutes. Some students took longer and some students

took less time to answer the questions. All of the manipulatives in the kit were used at

least once by one child, in spite of the 38 children that did not use any manipulatives on

any of the 12 questions. Burton concluded that "second-grade children are capable of

solving some division word problems if manipulatives are supplied. They are especially

successful in this if the objects match the problem of context" (p. 13).








Many classroom teachers focus on covering the material in the textbook from an

abstract approach despite the support in the literature for the use of manipulative

materials. Canny (1984) was interested in determining if manipulative materials had a

greater impact when used to introduce or reinforce a concept or both. The researcher

investigated the role of manipulative materials in improving achievement in computation,

concept-formation, and problem solving in fourth-grade mathematics. There were 123

fourth graders included in the study. The students were divided into four groups. Group

A teachers used manipulative material to introduce the concepts. Group B teachers used

manipulatives for reinforcement after the traditional teaching method. Group C teachers

used manipulatives to introduce the concept, practiced, and reinforced the concept using

the textbook and manipulatives. Group D teachers only used the textbook; they did not

use manipulative materials for the mathematics lessons. The classroom teachers

implemented the lessons to avoid as much disruption of the classes as possible. The

students in the study were given The Science Research Associates (SRA) achievement

test, a researcher-designed written achievement test and retention test that matched the

lessons and the textbook. The researcher conducted in-service sessions and demonstrated

teaching lessons because the teachers in the study had little experience with using

concrete objects to aid in learning. An ANCOVA model with multiple contrasts was

used in the study.

Canny concluded that "the group using manipulative materials for only

reinforcement scored the lowest while the group using manipulatives for only

introduction scored the highest" (p. 71). Most of the test results in this study provided

additional support for the research favoring the use of manipulative materials in teaching








and learning mathematics. The overall findings of this study would influence the

researcher to use manipulative materials to introduce concepts because it produced the

highest scores on the SRA test and the retention test.

"Using math manipulatives is one method of teaching that makes learning real to

students" (Chester and others, 1991, p. 4). Manipulatives help students to become

actively involved in doing mathematics instead of just listening to lectures and

completing paper and pencil activities. Chester and others investigated the effect of

manipulatives on increasing mathematics achievement of third-grade students. The

students in the study consisted of two randomly selected third-grade classes with 26

students in each class. Before the start of the study on a geometry unit, the students in the

study were given a multiple choice unit test. The researchers used an ANCOVA and t-

test to analyze the data. Manipulatives were used to teach concepts in the geometry unit

during the 2-week hourly teaching sessions. Drawings and diagrams were the only things

used to teach the concepts in the control group. There was a significant difference

between the pretest and posttest scores of both groups. Chester and others concluded that

"the experimental group, which used the math manipulatives, received higher adjusted

posttest scores than the control group, which used only the textbook" (p. 17).

McClung (1998) conducted a study to determine if the use of manipulatives in an

Algebra I class would make a difference in the achievement of the students. The

researcher used two Algebra I classes mixed with 10th and 11 th graders. One group had

24 students and the other group had 25 students. Group A was the control group, and the

students in this group were taught using traditional teaching methods consisting of

lecture, homework, and in-class worksheets. Experimental Group B was also taught








using the traditional teaching method but used manipulative Algeblocks instead of the

worksheets. A pretest was given prior to the beginning of the study to assure

homogeneity. A posttest, identical to the pretest, was given at the end of the study. A

two-sample t-test was used to analyze the data. There was a significant difference

between the two groups. The control group's mean score, using no manipulatives, was

higher than the experimental group's mean score, using manipulatives. This implied that

students using the traditional teaching methods outperformed the students using the

manipulatives. According to Piaget (1970), the concrete operational stage (7 to 12), is

the foundation for the use of manipulatives. One reason students using manipulatives

may have scored lower than students not using manipulatives could be because students

used manipulatives in the class lessons but were not permitted to use them on the posttest.

Although no information was given about the instructor's knowledge on manipulatives, a

second reason may be that the instructor had limited knowledge of the concept of using

manipulatives and did not obtain this information before the study began.

Hinzman (1997) conducted a study to determine the differences in mathematic

scores/grades of middle school students after the use of hands-on manipulatives and

group activities. Thirty-four eighth-grade students in two prealgebra classes participated

in the study. The instruments used in the 18-week study included a pretest, observation

of the students participating in the lessons, and a student attitude survey. Chapter tests

were given to the study groups to determine the effects of manipulative use on test

performance. Notebook tests were also given to determine the effects of homework on

class performance. A T-test was used to compare the test results of the two prealgebra

classes. There was no control group used in the study.








Hinzman concluded that students' performance during in class instruction

improved with the use of manipulatives. The study did not show any significant changes

in grades for students who used manipulatives and students who did not use

manipulatives. However, based on the results of the student attitude survey, there was a

significant improvement of students' attitude towards mathematics.

There was significant interest in determining whether combining computer-aided

instruction with hands-on science activities would increase students' cognitive and

affective assessment outcome (Gardner and others, 1992). Gardner and others used a

"weatherschool" meteorology program to determine the effect of computer-aided

instruction in the elementary school classroom. The instruments used to measure

students' cognitive and affective domains were paper and pencil tests. The first test

contained 13 questions and measured the knowledge and application level. The second

test contained 15 questions and measured students' attitudes toward science and

computers. There were three treatment groups. The first treatment group with 47

students included hands-on activities. The second treatment group with 46 students

included a combination of hands-on activities and the weatherschool program. The third

treatment group with 21 students used text based learning only. All three treatment

groups met for 10 days for two class periods each day. The results of the study indicated

that students' attitude and knowledge toward science increased with hands-on activities.

The pretest and posttest scores from Treatment Group II showed higher gains than

Treatment Group I. Treatment Group III scored higher on the pretest for conceptual

assessments. However, they scored lower than both groups on the posttest. "Hands-on








activities, CAI, or both apparently lead to increased understanding and more positive

attitudes" (p. 336).

Not all researchers would agree that concrete manipulatives have significant

effects on students' learning (Labinowicz, 1985; Resnick & Omanson, 1987; Thompson,

1992). Thompson was interested in the effects of concrete manipulatives on student's

understanding of whole number concepts. He investigated students' engagement in tasks

involving the use of base-ten blocks and their contributions to students' construction of

meaning of decimals. The study consisted of 20 fourth-grade students, 10 boys and 10

girls. Students were assigned to one of two treatment groups based on result of their

pretest scores. Students with similar scores were put in opposite groups. One treatment

group, taught by their regular classroom teacher, used Blocks Microworld in instruction.

The other treatment group, taught by a research assistant, used wooden base-ten blocks.

The microworld instruction was videotaped. The wooden base-ten blocks instruction

could not be videotaped because the class had two special education students in

attendance who were not part of the study. The teacher in the microworld experiment

group used a computer connected to a large screen projector for whole-class discussion.

The teacher in the wooden base-ten blocks experiment group used the overhead projector

and plastic blocks for whole-class instruction. Both teachers in the treatment groups used

a highly detailed script for instruction, which restricted their actions. This type of

instruction was used to get more responses from the students. At the end of the 9-day

study, students were administered a posttest containing items from the pretest and

additional items on decimal concepts. Thompson concluded that there were no

significant changes in either group regarding whole number concepts.








Tanbanjong (1983) conducted a study on the effects of using manipulative

material in teaching addition and subtraction to first-grade students. The 6-week study

consisted of 350 first-grade students. Half of the students were assigned to the treatment

group and the other half were assigned to the control group. The instructional activities

covering content in addition and subtraction of one-digit and two-digit numbers included

activities from a textbook provided by the Ministry of Education of Bangkok, Thailand,

for first-grade students. Students in both groups received mathematics instruction for 40

minutes per day for 6 weeks. In the treatment group, the instructor used the following

manipulatives: a pocket chart, a set of trading chips, a bundle of sticks, and an abacus.

In the control group, there were no manipulative materials used, only traditional

teaching methods. The researcher observed each group every week. A 20-item posttest,

used to measure students' achievement, was given at the conclusion of the study. A one-

way ANOVA was used to analyze the data. The children in the treatment group scored

significantly higher on the achievement test than the children in the control group. The

researcher concluded that the manipulative approach was more effective than the

traditional teaching method in facilitating achievement gains.

Kanemoto (1998) also conducted a study on the effects of manipulatives on

students' learning. The researcher was interested in the effects of using manipulatives

on fourth-grade students in learning place value. The 4-week study consisted of 51

fourth-grade students from two different classes. The treatment group contained 26

students, and the control group contained 25 students. A pretest was given to both

groups to make sure there were no significant differences in the groups. The treatment

group was taught place value using a combination of manipulatives and textbook








instruction. The control group was taught place value using textbook instruction only

but no manipulative material. There were 11 one-hour mathematics lessons conducted

during the study. A 20-item posttest was given at the completion of the study. A chi-

square test was used to analyze the data. The researcher concluded that the results of the

posttest showed that the use of manipulatives did not provide a difference in

mathematics achievement between the treatment and control groups when studying

place-value.

Harris (1993) also conducted a study to determine the effects of the use of

manipulative materials on the development of mathematical concepts and skills. The

12-day study consisted of 331 seventh-grade students. The classrooms were already

intact; therefore, random assignment was not possible. The control group consisted of

160 students, and the remainder of students were in the treatment group. Both groups

were given The California Achievement Test as a pretest to measure broad concepts of a

specific content area. All classes in the study used the same mathematics textbook.

Eleven lessons designed by the researcher, were developed for both groups. The lessons

covered objectives relating to perimeter and area. The control group received traditional

lecture, demonstration, and paper and pencil instruction. The treatment group received

the same instructional lessons except their instruction included the use of the following

manipulatives: geoboards, rubber bands, rulers, tape measures, trundle wheel, overhead

tiles, unifix cubes, square tiles, dot paper, and grid paper. At the end of the study, two

tests were administered to the two groups. One test was the California Achievement

Test (CAT), and the second test, consisting of multiple-choice and open-ended

questions, was a teacher-made unit test developed by the researcher. An ANOVA was








conducted to determine the achievement of the treatment and control group on the CAT

and the teacher-made unit test. The comparison of students' performance on both the

CAT and the teacher-made unit test indicated that the students in the treatment group

developed a better understanding of perimeter and area than the control group. Students

in the treatment group posited that manipulatives helped them to understand perimeter

and area and to apply the formulas correctly. No response was given for the control

group.


Technology and Simulated Manipulatives in the Classroom

An instructional model for teaching mathematics to young children should

emphasize active learning and problem solving with concrete materials (NCTM, 1989).

"Technology must support and enhance this model" (Campbell & Stewart, 1993, p. 252).

Technology education at the elementary level begins with three things in mind: the child,

the elementary school curriculum, and the appropriate technology activity (Kirkwood,

1992). Implementing technology in the mathematics education classroom helps "promote

high level thinking skills and support concept development" (Harvey & Chamitski, 1998,

p. 157). Campbell and Stewart (1993) stated that "perspective for using technology with

young children is that these tools permit teachers and children to investigate the richness

of mathematics" (p. 252).

An important role for computers is to assist students with exploring and

discovering concepts, with the transition from concrete experiences to abstract

mathematical ideas (NCTM, 1989). "In computer environments, students cannot overlook

the consequences of their actions, which is possible to do with physical manipulatives"








(Clements & McMillan, 1996, p. 274). Clements and McMillan gave the following

advantages to using the computer instead of physical manipulatives:

1. They avoid distractions often present when students use physical

manipulatives.

2. Some computer manipulatives offer more flexibility then do their non-

computer counterparts.

3. Another aspect of the flexibility afforded by many computer manipulatives is

the ability to change an arrangement of the data. Primary Graphing and Probability

Workshop (Clements, Crown, & Kantowski, 1991) allows the user to convert a picture

graph to a bar graph with a single keystroke.

4. Students and teachers can save and later retrieve any arrangements of

computer manipulatives. Students who had partially solved a problem can pick up

immediately where they left off.

5. Once a series of actions is finished, it is often difficult to reflect on it. But,

computers have the power to record and replay sequences of actions on manipulatives.

This ability encourages real mathematical exploration.

6. The computer connects manipulatives that students make, move, and change

with numbers and words.

7. Computer manipulatives dynamically link multiple representations. For

example, many programs allow students to see immediately the changes in a graph as

they change data in a table.

8. Students can do things that they cannot do with physical manipulatives. For

example, students can expand computer geoboards to any size or shape.








Computers also serve as a catalyst for social interaction (Clements, 1998). Social

interaction constitutes a crucial source of opportunities to learn mathematics (Piaget,

1970).

Connell (1998) conducted a study to determine the impact for technology use in a

constructivist elementary mathematics classroom. Students were randomly assigned to

one of two conditions. Twenty-five of the students were assigned to the technology-

aligned classroom (TAC), where the teacher used the computer as a student tool for

mathematics exploration. Twenty-seven students were assigned to the technology-

misaligned classroom (TMC), where the teacher used the computer as a presentation tool.

Daily teacher instruction was the treatment in the study. "Technology was present in

both classrooms, but only used as an integral portion of the mathematics instruction in the

TAC" (p. 325). Both classrooms used a similar constructivist mathematical pedagogy

with similar set of materials. Technology "was used in a constructivist fashion" (p. 331),

not as an afterthought. Both groups were successful in learning the required grade level

mathematics. "It is clear that the constructivist approaches to instruction as utilized in

this study were effective" (p. 331).

There is an assumption that computer software produces the same cognitive effect

as touching physical mathematics manipulatives (Terry, 1995). Terry conducted a study

to determine the effects that mathematics manipulatives (MM) and mathematics

manipulative software (MS) had on students' computation skills and spatial sense. The 4-

week study consisted of 214 students in Grades 3, 4, and 5. Sixty-eight students were in

the MM group; 68 students were in the MS group; and the remainder of students were in

the mathematics manipulative with mathematics manipulative software (BOTH) group.








Base ten blocks and attribute shapes, both in concrete manipulative and computer

manipulative form, were used for the computation and spatial sense lessons. At the end

of the study, two tests, designed by the researcher, were administered to the three groups.

One test covered computation, and the second test covered spatial sense. A three-way

ANOVA was conducted to determine the achievement of the three groups on the

computation and spatial sense test. The comparison of students in the three groups on the

spatial sense test indicated that there was no significant difference. The comparison of

the three groups on the computation test indicated that the students using both concrete

manipulatives and computer manipulatives developed a better understanding of

computation than the other two groups. The researcher felt that the use of both was the

most effective way to teach computation.

Kim (1993) also was interested in the effects of concrete manipulatives and

computer-simulated manipulatives on students' learning. Kim conducted a study on

young children to determine the effects computer-simulated and concrete manipulatives

would have on their learning of geometric and arithmetic concepts. The 6-week study

consisted of 35 kindergarten children. Seventeen students were in the hands-on group,

and the remainder of students was in the computer simulation group. The instructional

activities for both groups included activities with geoboards, attribute blocks, and

Cuisenaire rods. There was no control group in the study. The researcher was

concerned with possible differences in initial intellectual ability and maturity, because

the two treatment groups were not randomly assigned. There were 12 lessons, with each

lesson lasting 25 minutes. At the beginning of the three units a pretest was

administered, and at the end of each unit, a posttest was given to determine students'








progress in the three units. The first unit covered geometry; the second unit covered

classification; and the third unit covered seriation, counting, and addition. An

ANCOVA was conducted to determine the achievement of the treatment groups on the

unit tests. The results indicated that hands-on and computer-simulated manipulatives

were similar in their effectiveness. Both groups made significant gains from the pretest

to the posttest on all three unit tests. The researcher concluded that using either concrete

or computer-simulated manipulatives helped children learn mathematical concepts

relating to geometry, seriation, counting, and addition. The researcher also believed that

hands-on and computer-simulated manipulatives were equally effective for teaching

mathematics concepts to young children.

Berlin and White (1986) studied the effects of combining interactive

microcomputer simulations and concrete activities on the development of abstract

thinking in elementary school mathematics. The sample of students was randomly

selected from two elementary schools. Three levels of treatment were administered to the

113 students in the study: (a) concrete only activities, (b) combination of concrete and

computer simulation activities, and (c) computer-simulation only activities. Two paper

and pencil exams requiring reflective abstract thought were administered to all the

subjects. The researchers felt it was still unclear what effects concrete manipulation of

objects and computer simulations had on students' understanding. The researchers

concluded that effects of concrete and computer activities produced different results,

depending on the socio-cultural background and gender of the child. Berlin and White

posited more research was needed to determine the effect of concrete and computer

activities in the learning of elementary school mathematics.








Researchers have also found computer simulations useful in the science

curriculum (Bork, 1979; Lunetta & Hofstein, 1981). Choi and Gennaro (1987) conducted

a study that compared the effects of microcomputer-simulated experiences with a parallel

instruction using hands-on laboratory experiences for teaching volume displacement

concepts. The subjects consisted of 128 eighth-grade students in earth science classes.

The method used in the study was an experimental design with a control group. The

students were randomly assigned to one of two treatment groups--the microcomputer-

simulated group (the experimental) and the hands on laboratory group (the control). The

experimental group used a computer program which included graphics, animation, and

color for visualizing concepts; blinking words to help students focus on important

instructions; and immediate feedback to enhance learning. The control and the

experimental groups conducted parallel experiments, but the experimental group used

worksheets and hands-on experiments. The experiments took 55 minutes in the control

group and only 25 minutes in the experimental group. After the 2-day experiment, a 20-

item multiple choice posttest was administered to students in both groups to measure the

students' understanding of concepts related to volume displacement. There was no

pretest given. The results of the study showed that computer-simulated experiments were

as effective as hands-on laboratory experience. Forty-five days later a retention test was

given to the students in both groups. A three-way ANOVA was used to interpret the

data. Choi and Gennaro found that the computer-simulated experiences were as effective

as the hands-on laboratory experiences. Also, there was not a significant difference in

retention scores of the two groups.








One implication of this study was that "computer simulations could be used for

the teaching of some concepts without the extra needed effort and time of the teacher to

prepare make-up material" (p. 550). Another implication was that the computer-

simulated experience "enabled the students to achieve at an equal performance level in

approximately one-fourth the time required for the hands-on laboratory experiences (25

minutes compared to 95 minutes)" (p. 550).


Probability

Because of the emphasis on probability learning in the school curriculum, there

is a need for further on-going research on the learning and teaching of probability

(Shaughnessy, 1992). The study of statistics and probability stress the importance of

questioning, conjecturing, and searching for relationships when formulating and solving

real-world problems (NCTM, 2000). We live in a society where probabilistic skills are

necessary in order to function. Probability describes the world in which we live. Many

everyday functions depend on knowing and understanding probability. Milton (1975)

suggested the following reasons for introducing probability as early as the primary level:

I. The basic role which probability theory plays in modem
society both in the daily lives of the public at large, and
the professional activities of groups within the society, e.g.
in the sciences (natural and social), medicine and
technology.

2. Probability theory calls upon many mathematical ideas
and skills developed in other areas of school coursed, e.g.
set, mapping, number, counting, and graphs.

3. Students are able to work in a branch of mathematics,
which is relevant to current activities in life. (p. 169)








Jones, Langrall, Thornton, and Mogill (1999) conducted a study to evaluate the

thinking of third-grade students in relation to an instructional program in probability.

The focus of the evaluation was on student learning. The design of the instructional

program was consistent with a constructivist orientation to learning. "Opportunities to

construct probability knowledge arise from students attempt to solve problems, to build

on and recognize their informal knowledge, and to resolve conflicting points of view"

(p. 492). There were two third-grade classes involved in the study: one was an early-

instruction group, in the fall, and the other one was a delayed-instruction group, in the

spring. The instructional program consisted of 16 40-minute sessions with two sessions

occurring each week for 8 weeks. The probability problem task was based on the

following essential constructs: "sample space, probability of an event, probability

comparisons, and conditional probability" (p. 494). Problems were chosen so they

would be accessible to students functioning at different levels. The most important

feature of the data was the number of students in both groups that increased their

informal quantitative level of probabilistic thinking following instruction. An important

finding was that 51% of the students who began instruction below an informal

quantitative level reached this level after instruction. A repeated measures ANOVA was

used to evaluate the overall effect. This analysis "demonstrated that both the early and

delayed instruction groups showed significant growth in performance following

instruction" (p. 517).

Analyses of the students' learning during the instructional program, which

revealed a number of patterns that appear to have critical ramifications for children

understanding of probability, are related to the following:









1. children's conception of sample space

2. their use of part-part and part-whole relationships in
comparing and representing probabilities
3. their use of invented or conventional language to describe

their probability thinking. (p. 515)

Many of the students revealed higher level sample-space thinking following instruction.

The results of this study showed that the probabilistic thinking framework could be used

to develop an effective instructional program in probability. A further conclusion of this

study was that the use of part-part reasoning provided a threshold level for dealing with

probability situations that incorporated some need for quantitative reasoning. The

researchers suggested that further research is needed to help classroom teachers use the

probabilistic thinking framework to enhance student learning.

Fishbein and Gazit (1984) analyzed the effect teaching of probability indirectly

has on intuitive probabilistic judgments. The researchers defined intuition as "a global,

synthetic, non-explicitly justified evaluation or prediction" (p. 2). The study evolved

around the indirect effect of a course on probability on typical, intuitively-based

probabilistic misconceptions. The teaching program designed for students in Grades 5, 6,

and 7 was intended to determine the capacity of their ability to assimilate, correctly and

productively, basic concepts and solving procedures in probability. The students were

given various situations that gave them opportunities to be active in the following areas:

I. calculating probabilities

2. predicting outcomes in uncertain situations

3. using operations with dice, coins and marbles for
watching, recording and summing up different sets of
outcomes. (p. 3)








Two questionnaire assessments were used to determine the teaching effects. The first

questionnaire was used to determine to what extent pupils in the experimental class had

assimilated and were able to use concepts and procedures taught. The purpose of the

second questionnaire was to determine the indirect effect of instruction on children's

intuitively based misconceptions. The first questionnaire was too difficult for fifth

graders. Nevertheless, for sixth and seventh graders, progress was evident. Because of

the low negative results for fifth-grade with questionnaire one, the second questionnaire

was not reliable and relevant enough for this grade level. However, "in grades six and

seven, the teaching programme has had an indirect positive effect on their intuitive

biases" (p. 22).

Vahey (1998) was interested in middle school students' understanding of

probability. In his dissertation, he used a Probability Inquiry Environment (PIE)

curriculum. The PIE curriculum consisted of computer-mediated inquiry-based

activities, hands-on activities and whole-class discussion. Vahey used a quasi-

experimental design with four seventh-grade classes. Two of the classes used the PIE

curriculum for 3 weeks and the other two classes used a teacher-designed curriculum for

3 weeks. The same teacher taught the 3 week curriculum for both groups. The students

in both groups were given the same quantitative pretest and posttest. There was no

significant difference between the groups on the pretest. However, a significant

difference was found between the two groups on the posttest. The students using the PIE

curriculum out-performed the students in the comparison group on the posttest. Vahey

posited that students using the PIE curriculum could successfully build on their existing








understandings by engaging in activities before formal introductions of concepts,

definitions, and terms.

Drier (2000) was also interested in children's probabilistic understanding. In her

dissertation, she conducted a 2-month exploratory teaching experiment with three 9-year-

old children at the end of their third-grade year. Each child in the study participated in 10

hours of teaching sessions using a computer microworld program designed by the

researcher. The computer microworld program, Probability Explorer, was designed for

students to explore with probability experiments. A pretest and posttest interview

assessment was given to all three children. All teaching sessions were videotaped and

audiotaped. The children's development of probabilistic reasoning and their interaction

with the computer tools varied during the study. The results of the study indicated that

open-ended computer tools could represent children's development of probability

concepts. Drier posited that further research was needed with Probability Explorer in a

variety of small groups and classroom situations with different task and technological

versus nontechnological learning on students' reasoning and understanding of probability.

She also recommended that further research be done to help students connect their

"understanding of probability with their understandings of other mathematics concepts

and their everyday experiences" (p. 359).

Jiang (1993) also developed a simulated computer program, CHANCE. The

program was used to eliminate the use of physical materials when teaching and learning

probability. Without being able to generate large number experiments, it is difficult for

students to believe that certain events have the same possible chance of occurring (Jiang

& Potter, 1994). Jiang and Potter conducted a teaching experiment to evaluate the








performance of CHANCE. The researchers were interested in knowing if the use of

CHANCE could effectively help students achieve conceptual understanding of

probability. There was no control group due to time constraints. Like the

aforementioned study, the teaching experiment was exploratory, and a small sample was

used to obtain information that was more detailed. Three boys and one girl participated

in the study. They were in 5th, 6th, 8th, and 11 th grades, respectively. The students in

Grades 5 and 6 were in group one and the students in Grades 8 and 11 were in group two.

The teaching experiment consisted of two 1-hour sessions per week for 5 weeks. The

children in the study took the same pretest and posttest. The pretest was administered to

determine students' previous knowledge. The posttest was used to determine both

students' knowledge and students' ability to solve problems using simulations and

modeling. They found CHANCE useful in making probability instruction meaningful,

stimulating and increasing students' learning difficulties, and in helping students

overcome their misconceptions about probability. Generalizability was limited due to the

small sample used in the study. Also, because the students were above average, there

was no way to determine if students below average would benefit or if the positive effect

of CHANCE was due partly because of the beginning academic level of the students.

Fischbein, Nello, and Marino (1991) conducted a study on students' difficulties

with probabilistic concepts. For example, the concept of certain events is more difficult

to comprehend than that of possible events. They used 618 students in three groups. In

the first group, there were 211 students in Grades 4 and 5. In the second group, there

were 278 students in Grades 1, 2, and 3 with no prior instruction in probability, and in the

third group, there were 130 students in Grades 1, 2, and 3 with prior instruction in








probability. Students answered one of two parallel 14-item questionnaires (A or B) of

probability problems. The items on the questionnaires required the subjects to explain

their answers. On many of the problems, the subjects had to determine how the sample

space related to a certain event. The authors indicated that students had difficulty

answering the probability problems on the questionnaire because of students' lack of

skills for rational structures.

Fast (1999) also noticed that students overcame misconceptions in probability

after instruction. Fast investigated the effectiveness of using analogies to effect

conceptual change in students' alternative probability concepts. He wanted to determine

if using analogous anchoring was an effective approach to overcoming probability

misconceptions. Anchors are analogies or examples used to simplify difficult concepts.

It was difficult generating anchoring situations that would be effective for the students.

The investigation centered on high school students' probability concepts. Most of the

students in the study felt analogies were useful in reconstructing their thinking.

Generally, misconceptions are "highly resistant to change" (p. 234). Thus, "the results of

the study showed that the use of analogies was effective for reconstructing high school

students' probability misconceptions" (p. 234). Fast concluded that the use of analogies

could make a valuable contribution to overcoming probability misconceptions.


Summary

There is much support on children learning concepts of probability as early as

elementary school. Many important documents, such as Principle and Standards

(2000), have been revised to include the teaching and learning of probability as early as

kindergarten. Although few studies exist at the elementary level, researchers recognize








the importance of introducing probability at an earlier age (Shaughnessy, 1992; Vahey,

1998).

In the review, many of the studies (e.g., Fishbein & Gazit, 1984; Baxter and

others, 1993; Jiang, 1993; Vahey, 1998) approached the concepts of probability and

statistics starting in the middle grades and higher through computer simulations and

concrete manipulatives. It was evident in a few of the studies (e.g., Choi & Gennaro,

1987; Kim, 1993) that the use of computer simulations was helpful for learning

mathematics and science and providing more learning time for students. Some of the

studies (Jones and others, 1999; Drier, 2000) provided evidence that students may learn

probability concepts earlier than the age predicted by Piaget and others. At this

particular time, there are very few studies reported on the investigation of how computer

simulations and concrete manipulatives can benefit elementary students in learning

probability concepts. It is not clear at this point the effects that concrete manipulatives

and computer simulations have on elementary students' understanding probability

concepts at an early age.

In many of the studies (Mack, 1995; Anthony, 1996; Frank & Carey, 1997;

Wiegel, 1998; Drier, 2000, interviews and case studies were used to obtain information.

Some researchers (e.g., Choi & Gennaro, 1987; Jiang, 1993; Kim, 1993) did not establish

control groups or validate their instruments. Many of the instruments were used to

provide information on students' probabilistic thinking and misconceptions about

probability. In some quantitative studies (e.g., Confrey, 1990), researchers used

nonrandom samples which were often a consequence of school-based research. In

addition, generalizability was not possible due to the small number of students used in








these studies, which may have weakened statistical results. It is still important to

consider the results of these studies because they can serve as a starting point for further

investigation. In some studies, researchers designed their own computer program (e.g.,

Jiang, 1993; Drier, 2000) for teaching probability with computer simulations because

they posited there was inadequate software available. One program found to be the most

promising to use for teaching probability using computer simulations was Probability

Explorer. However, there has been no research conducted on its effect on whole-class

learning.

Some studies revealed that when examining children about specific concepts in

probability, many misconceptions regarding children's understanding of probability were

evident. For example, some students may have misconceptions about a coin toss being

fair if they believed that getting a tail was best.

The constructivist theory has been prominent in recent research on mathematics

and science learning. The constructivist learning and teaching theory has influenced the

instructional environment. Because of this theory, many studies (e.g., Bednarz & Janvier,

1988; Confrey, 1990; Anthony, 1996; Schulte, 1996) have stressed the importance of

students taking an active role in their learning process. The constructivist learning theory

emphasizes the importance of the knowledge, beliefs, and skills an individual brings to

the experience of learning. It also provides a basis for recent mathematics education

reform efforts. There is a need for further research for teaching and learning probability

from a constructivist perspective, especially at the elementary level.














CHAPTER 3
METHODOLOGY


Overview of the Study

This chapter describes the research objectives, the development of the research

instruments, and the participants for the study. It outlines the procedures for the

research design and the data analysis.

The purpose of this study was to investigate the impact of using computer-

simulated manipulatives and concrete manipulatives on elementary students' probability

learning skills and concepts of experimental probability. Of interest to the researcher

was students' ability to predict outcomes of simple experiments and to discuss and

describe the likelihood of events using words such as certain, likely, unlikely, and

impossible. The researcher was also interested in incidental fraction learning that might

occur as participants engage in probability experiences.


Research Objective

The following null hypotheses were tested in the study:

I. There will be no significant difference between students
who use computer-simulated manipulatives and students
who do not use computer simulated manipulatives
regarding students' learning skills and concepts in
experimental probability.

2. There will be no significant difference between students
who use concrete manipulatives and students who do not
use concrete manipulatives regarding students' learning
skills and concepts in experimental probability









3. There will be no interaction between students who use
concrete manipulatives and computer-simulated
manipulatives regarding students' learning skills and
concepts in experimental probability.


Description of the Research Instruments

Experimental Probability Instrument

The researcher designed the Experimental Probability Instrument (EPI) (see

Appendix A) and administered it as a pretest and posttest. The EPI was used to measure

students' probability learning skills and concepts of experimental probability. The

research was conducted to examine the effects of employing computer-simulated

manipulatives and concrete manipulatives on the above factors.

To insure that the content of the instrument reflected content domain of

elementary students regarding the concept of probability, the researcher used several

methods to determine the content validity of the instrument. First, the researcher used

the literature as a guide to locate items specific to probability. Secondly, the researcher's

teaching experience was used to examine the items for appropriate content. In addition,

items used to create the EPI were adapted from Konold, Pollatsek, Lohmeier, and

Lipson (1993), Vahey (1998), and Drier (2000). The objectives of the EPI are given in

Table 3. Fourthly, the researcher submitted the instrument to two mathematics

educators for their review and analysis of content and later received written comments

from them regarding the content and objectives of the instrument. They provided

feedback on the wording and numbering of some of the items and drawings of the

objects the researcher incorporated into the design of the instrument.









Table 3

Objectives of Experimental Probability Instrument


# of items Objective: Test the students understanding, learning skills, concepts
and ability to...


9 decide whether a game is fair or unfair by specifying the estimated
probability of an event, given the data from an experiment

6 discuss the degree of likelihood by distinguishing whether an event is
an instance of certainty, uncertainty, or impossible

3 test predictions and identify estimates of true probability given a set
of data from an experiment, by counting the number of outcomes of
an event

8 predict the probability of outcomes by specifying the probability of
simple events or an impossible event

2 specify the probability of an impossible event

10 interpret that the measure of the likelihood of an event can be
represented by a number from 0 to I by specifying the outcome of an
event between 0 and 1

1 describe events as likely or unlikely by identifying the most likely
event of two unequally likely events

6 find outcomes of probability experiments and decide if they are
equally likely by identifying two equally likely events as being
equally likely



Experimental Fraction Instrument

To measure incidental fraction learning, the researcher also administered a

parallel Experimental Fraction Instrument (EFI) (see Appendix B) as a pretest and

posttest. The role of the EFI was to measure students' incidental fraction learning skills

after working with experimental probability.








To insure that the content of the instrument reflected content domain of

elementary students regarding the concept of fractions, the researcher's teaching

experience and the literature were used as a guide. The researcher incorporated items

for the EFI similar to those found in elementary mathematics textbooks.


Pilot Study

The researcher conducted a pilot study during the Spring 2000 term using two

fifth-grade elementary classes in a school district in the southeastern United States. The

purpose of the pilot study was to obtain reliability data for the instruments and examine

the application of the treatment.

After the implementation of the EPI and EFI in one class, the researcher made

several changes regarding the content of the instrument. First, objects representative of

manipulatives were redrawn so that the values were a true representation of the size.

These objects included marbles and spinners. Second, more specific directions were

given. Third, the researcher eliminated two questions because they were repetitive.

The researcher observed that the success of the EPI for students was probable

only when students were given an opportunity to try experiments using the computer-

simulated probability software. Because students were not familiar with some of the

terminology used in the lessons, the researcher designed the lessons to provide time for

whole group discussion prior to the activities for all groups in the study. This eliminated

many questions except a few that related to the activities. For example, students

discussed the term least and gave examples of least likely events. All groups continued

in this way until all unfamiliar terms were understood.








An internal consistency reliability estimate for the final version of the EPI was

estimated using an EXCEL program. The reliability estimate for the EPI was given as

.89. An item analysis was done to determine if items had a reasonable level of

discrimination. The difficulty and discrimination indices for the individual items of the

EPI are reported in Table 4. Item discrimination for the EPI ranged from 0 to .26 with a

median of .21.

An EXCEL program was also used to estimate the EFI. The reliability estimate

for the EFI was also .89. The difficulty and discrimination indices for the individual

items of the EFI are reported in Table 5. Item discrimination for the EFI ranged from 0

to .27 with a median of. 15.

The discrimination index is used to describe the validity of a test in terms of the

persons in contrasting groups who pass each item. The researcher obtained the

discrimination index by subtracting the proportion of students in the lower half of the

group who answered the item correct from the proportion of students in the upper half of

the group who answered the item correct. The item-total correlation as a measure of

discrimination for EPI and EFI was given as .55 and .32, respectively.

The item difficulty index is used to describe the percentage of persons who

correctly answer a particular test item. The difficulty index was obtained by the number

of subjects who answered the item correctly, divided by the total number of subjects

taking the test.

The first administration of the study was conducted during September 2000. The

study included four classes taught by the classroom teachers. The study began with the

administration of the EPI and the EFI in all classes. After implementation of the two










Table 4

Difficulty and Discrimination Indices for Experimental Probability Instrument



Item Discrimination Difficulty p-value


.250
.239
.247
.156
.234
.198
.234
.250
.247
.215
.207
.156
.229
.222
.250
.117
.253
.198
.198
.156
.179
.131
.215
.215
.144
.168
.168
.102
.131
.254
.244
.244
.215
.222
.234
.222
.222
.234
.198
.250
.244
.168
.179
.000
.000
.255


.291
.242
.370
.434
.311
.121
.468
.375
.429
.477
.602
.546
.448
.448
.273
.162
.473
.373
.104
.439
.399
.297
.712
.217
.490
.431
.545
.406
.453
.430
.678
.291
.375
.561
.784
.801
.801
.559
.508
.340
.658
.090
.505
1
1
.550









Table 5

Difficulty and Discrimination Indices for Experimental Fraction Instrument


Item Discrimination Difficulty p-value


.152
.152
.152
.152
.083
.000
.083
.000
.083
.000
.083
.000
.000
.083
.000
.083
.152
.152
.205
.242
.242
.152
.273
.265
.205
.000
.152
.242


.716
.716
.716
.716
.409
0
.409
0
.409
0
.409
0
0
.409
0
.409
.553
.770
.503
.444
.444
.552
.750
.373
.316
0
.498
.315


.83
.83
.83
.83
.92
1
.92
1
.92
1
.92
1
1
.92
1
.92
.83
.83
.75
.67
.67
.83
.50
.42
.75
1
.83
.67


instruments, the researcher decided to make a change to the format of the instruments.

The researcher attached the EFI onto the EPI to encourage students to complete the


instruments.








The classroom teachers were instructed to teach the probability lessons to

students following the administration of the covariates. Several changes were made to

the lessons based on the researcher's observations. The lengths of the lessons for the

manipulative and the computer/manipulative group were shortened to provide enough

time for discussion. Manipulatives were presorted and labeled before the treatment

lessons for affective use of time. All classes except the control group were assigned to

small groups within the classes prior to the start of the lessons. Another major change

based on the researcher observation was for the researcher to facilitate the lessons

instead of training the teachers to facilitate the lessons. The reason was that although the

teachers were trained and given materials to use for the lessons, they did not follow

through with their roles in the study. One classroom teacher did not use any of the

concrete manipulatives provided. Another classroom teacher did not follow the lessons,

which were designed according to the constructivist theory. The teacher in this

classroom taught using more traditional teaching methods. For example, the teacher

would have the students conduct a simulation using marbles on the computer and then

would ask how many of each did students obtain. There would be no further discussion

of the experience. This continued for 5 minutes, and then the teacher concluded the

lesson. Students were not able to discuss what they learned from their results of the

simulations. The researcher concluded that the study could not be conducted properly

without adherence to the theoretical framework.


Research Population and Sample

The population for this study consisted of students in the fifth grade at public

elementary schools. The research sample consisted of 83 fifth-grade students enrolled in








elementary school in the southeastern United States. The subjects were of various

abilities, mixed gender, and racial background. The students in the classes represented

characteristics of the population of students in these elementary schools. There were

four classes of students: three treatment classes and one control class. Because the

students were already in their assigned classes, true random assignment was not

possible. First, students in one class participated with concrete manipulatives. A second

class experienced instruction with the computer and probability software. A third class

was taught using a combination of concrete manipulatives and the computer. The control

group participated in the traditional setting.


Procedures

Prior to the study, the researcher obtained permission from the University of

Florida Institutional Review Board for the investigation to take place. The students were

informed and had to obtain parental permission to participate in the study (see Appendix

E). The classroom teachers administered the pretest and posttest instruments in class. A

schedule for the procedures is provided in Table 6.


Table 6

Schedule of Administration of Instruments and Treatment


Group Pretests Treatment Posttests


Treatment(c/m)* January 8, 2001 January 9-11, 2001 January 12, 2001
Treatment(m)** January 8, 2001 January 22-25, 2001 January 26, 2001
Treatment (c)*** January 8, 2001 January 22-25, 2001 January 26, 2001
Control January 8, 2001 January 16-18 2001 January 19, 2001

** manipulative group; *computer & manipulative group; ***computer group








Treatment

Treatment consisted of the use of concrete manipulatives and computer-simulated

manipulatives during in-class lessons. There were three preplanned lessons, each lasting

one 60-minute class period. The lessons were presented in a constructivist perspective.

According to information provided in the review of the literature, this included student

discovery, collaboration with other students in groups, and doing activities that relied

heavily on sources of data and manipulatives. Rather than correct students' initial

analysis, the researcher let the experimental results guide and correct students' thinking

(Van de Walle, 1998). This one instructional method gave students an opportunity to

develop and build upon their own mathematical ideas. Rather than the teacher being the

dispenser of knowledge, students were able to use their own inquiry to investigate events.

The concrete and computer-simulated manipulatives used in this study included

coins, dice, spinners, and marbles. These manipulatives were used as simulations of real

situations. These manipulatives can illustrate impossible and certain events. All of these

manipulatives can be used to teach basic concepts of probability. Students need to see

that simulations aid in learning fundamental concepts of probability.


Treatment Lessons

The researcher taught all classes. Each group had its own version of the lessons.

This was due to the type of materials used for each group. Although the lessons were

designed according to the nature of the group, the objectives for the lessons for each

group were parallel. All treatment groups were put into small cooperative groups of four

to six students. The lessons used during the treatment phase are found in Appendix C. A

brief outline follows:








Lesson 1.

Objective: To have students explore concepts of chance and to decide whether a

game is fair or unfair using concrete manipulatives or computer generated manipulatives.

Procedure: Students worked in cooperative groups of four and six. Students in

each group had a role in their group. The roles in the group included two students in

charge of the manipulatives, one student to record the outcomes and a student to report

the information to the whole class on the performance of their group. The instructor

assessed students' prior knowledge through inquiry about chance and luck. Also, through

whole class discussion, students described what fair meant to them. The students spent

the remaining class time working on activities and reflecting on their outcomes through

group and whole class discussion. The activities are described in detail in the

motivational sections of the lessons in Appendix C.

Lesson 2

Objective: To have students work with likely or unlikely events.

Procedure: Students worked in cooperative groups. The instructor checked for

understanding of terms by first having students discuss in their groups what the terms

certain, impossible, likely, or unlikely meant to them. In whole class setting, students

discussed the meaning of these terms. Students worked on activities in cooperative

groups. Following the activities, students responded to questions regarding their

outcomes.

Lesson 3

Objective: To have students make and test predictions for simple experiments.








Procedure: Students spent the first part of class time working on activities in their

cooperative groups and remaining class time reviewing concepts of chance, and simple,

likely and unlikely events with the instructor.

According to the NCTM (2000), the study of chance done with the use of

concrete manipulatives should be followed by computer-simulated manipulatives using

available software. This would gradually lead to a deeper understanding of these ideas

through middle and high school. Therefore, the researcher chose to use a computer

software program, "Probability Explorer," developed by Drier (2000). Drier designed

the program to generate simulations of manipulatives for large trials in a few minutes.

The software displays results as a table that presents the experimental results as a

frequency (count) or relative frequency (fraction, decimal, or percent), a pie graph that

displays the relative frequencies, or as a bar graph that shows the frequency distribution.

These added features of this newly designed software program give teachers an

opportunity to reinforce previously taught skills. According to many researchers (e.g.,

Weibe, 1988; Perl, 1990; Strommen & Lincoln, 1992; Clements, 1998; Greening, 1998),

students' experiences with technology provide deeper and more substantial

understandings.


Data Analysis

A nonequivalent control group design was used for the study. There were four

groups with one class of students in each. Treatment Group I was allowed to use

concrete manipulatives for the lessons. Treatment Group II participated in computer

simulations with the lessons, and Group III used a combination of both concrete

manipulatives and computer simulations for the lessons. The Control Group was not








provided with manipulatives or the use of the computer for use with the lessons. The

quasi-experimental research design involved a 2x2 matrix: the level of concrete

manipulative use and the level of computer simulation. The objective was to determine

the effect of the two independent variables, individually and interactively, on the

dependent variable (posttest scores). The researcher used an analysis of covariance

(ANCOVA). The advantage of using an ANCOVA was to control for any initial

differences that may have existed between the four groups and to determine if there was

a significant treatment effect. The EPI and the EFI were used as pretests (covariates)

and as posttests.

In addition to the quantitative analyses of the instrument, the teachers in the study

classes observed the experimental classes to record students' reaction to the use of

concrete manipulatives and computer-simulated manipulatives. These observations

were used to examine students' problem-solving process.














CHAPTER 4
RESULTS


This chapter contains the descriptive statistics and the results of the analysis

pertinent to the null hypotheses of this study. Participants for the study consisted of 83

fifth-grade students from four classes at three elementary schools in Southeast U.S. The

instructional phase consisted of three lessons that tested students understanding, learning

skills, concepts and ability of experimental probability.

Descriptive statistics for the pretest and posttest results for the probability

instrument are presented in Table 7, which includes the number (n) in each group, mean

(m), and standard deviation (SD).


Table 7

Pretest and Posttest Means and Standard Deviations for EPI


Pretest Posttest
Group n Mean SD Mean SD



Computer & Manipulative 20 24.20 4.88 26.30 3.49
Computer 17 17.71 6.99 22.88 4.62
Manipulative 12 19.17 5.79 20.17 8.22
Control 17 25.47 6.52 26.09 7.67



The researcher employed an ANCOVA to control for initial differences that may

have existed in the pretest results among the four groups. The ANCOVA results revealed








that when considering the dependent variable, posttest scores on the Probability

Instrument, there were no statistically significant difference among students using

concrete manipulatives based on the results of the EPI (see Table 8). Therefore, the

following null hypotheses could not be rejected:

There will be no significant difference between students who use concrete
manipulatives and students who do not use concrete manipulatives regarding
students' learning skills and concepts in experimental probability.

There will be no interaction between students who use computer-simulated
manipulatives and concrete manipulatives regarding students' learning skills
and concepts in experimental probability.

These findings indicated that the interaction terms did not account for a significant

proportion of the variance in posttest scores (adjusted R2 = .3861).


Table 8

Analysis of Covariance: EPI


Source DF Type III SS F p


Pretest 1 374.21 15.07* 0.00
Manipulative 1 14.71 0.59 0.44
Computer 1 134.27 5.41* 0.02
Computer- Manipulative 1 29.28 1.18 0.28
Pretest x Manipulative 1 12.22 0.49 0.49
Pretest x Computer 1 152.90 6.16* 0.02
Pretest x Computer/Manipulative 1 23.54 0.95 0.33
Model 7 1189.44 6.841* 0.00
Error 58 1440.68

Note: *significant for p= .05








After these terms were removed, there was minimum change in the observed

variance (adjusted R2 = .3833). However, there was a statistically significant variable for

students' use of computer-simulated manipulatives on the EPI (see Table 8). Therefore,

the following null hypotheses could be rejected:

There will be no significant difference between students who use computer-
simulated manipulatives and students who do not use computer simulated
manipulatives regarding students' experimental probability learning skills
and concepts.


The parameter estimate (see Table 9) indicates how the treatment groups faired

compared to the control group. Results of the interaction analyses show that achievement

in mathematics can significantly interact with computer instruction. Students with very

low scores on the EPI received higher scores on the EPI when instruction included

computer instruction. On average, students in the computer group scored 13 points

higher on the posttest than students in the control group given their individual pretest

scores. This was possible only when students in the computer group had low pretest


Table 9

EPI: Comparison of the Parameter Estimate for the Treatment and Control Groups


Source Estimate p


Manipulative -5.55 0.44
Computer 14.06* 0.02
Manipulative-Computer 8.30 0.28
Pretest x Manipulative 0.22 0.49
Pretest x Computer -0.64* 0.01
Pretest x Manipulative-Computer -0.29 0.33

Note: *significant for p = .05









scores on the measure of probability skills. However, this advantage disappears when

comparing students who have higher pretest scores (see Figure 1). Low-achieving

students were obviously not benefiting as much from the usual mode of instruction

employed by their teachers, and even though a computer approach was new to them, it

proved to be more beneficial than the more familiar type of instruction. This indicates

that students who have little previous probability knowledge would benefit from

participation in the computer group (see Figure 1). It is also an indication that computer

use shows positive results as indicated by Connell (1998), Kim (1993), and Terry (1995).

In addition, the same interpretation applies when comparing the computer group

to the manipulative group and the manipulative-computer group (see Figures 2 and 3).



Interaction between Computer and
Control Groups

2! 40
0
S30
= 20
a 10
0
CL 0

0 5 10 15 20 25 30
Pretest Probability Scores

-1 Control Computer


Figure 1. Interaction between computer and control groups.









Interaction between Computer and
Manipulative Groups


0
0



.0
0
a-


5 10 15 20
Pretest Probability Scores


25 30


- Manipulative Computer


Figure 2. Interaction between computer and manipulative groups.



Interaction between Computer and
Manipulative-Computer Groups

0
2 40

0
._-20 -

CL
0


5 10 15 20
Protest Probability Scores


25 30


1- Manipulative-Computer Computer


Figure 3. Interaction between computer and manipulative-computer groups.








Students in the computer group who have low pretest scores on probability skills tend to

do better than students in the manipulative group and the manipulative-computer group.

This is an indication that neither the manipulative group, nor the manipulative-computer

group was significantly different from the control group. However, like the control

group, the manipulative group and the manipulative-computer group were similarly

different from the computer group.


Other Findings

Incidental learning, which is not planned, is often overlooked. The study of

probability gives students an opportunity to revisit and practice previously learned

concepts and skills. Therefore, the researcher included an EFI to measure incidental

learning of fractions. Descriptive statistics for the pretest and posttest results for the EFI

are presented in Table 10, which includes the number (n) in each group, mean (in), and

standard deviation (SD).


Table 10

Pretest and Posttest Means and Standard Deviations for EFI


Pretest Posttest
Group n Mean SD Mean SD



Control 16 26.13 4.05 26.94 3.92
Manipulative 12 18.17 7.57 19.75 8.17
Computer 16 14.56 9.64 22.63 5.82
Computer & Manipulative 20 20.25 5.93 22.05 4.84








Significance tests of the parameter estimates for group affiliation indicate that

there was no detectable difference between the mean adjusted posttest scores of students

in the control group and the mean adjusted posttest scores of students in the

computergroup or the computer-manipulative group using a significance level of p = 0.05

given the students fraction pretest scores (see Table 11). The only significant difference

was in the scores of the students in the manipulative group (see Table 11). Even with the

interaction terms removed, there was still a relatively significant change in the scores of

the students in the manipulative group (see Table 12). Controlling for fraction pretest

scores, students in the manipulative group still scored on average, 3.30 points lower on

the fraction posttest than students in the control group (see Table 12). Students in the

computer group and the


Table 11

Analysis of Covariance: Fraction Instrument


Source DF Type III p Parameter F
SS Estimate


Model 7 1222.13 0.00 8.96
Error 56 1090.86
Fraction Pretest 1 51.16 0.11 0.46 2.63
Manipulative 1 34.96 0.19 -10.98 1.79
Computer 1 3.86 0.66 3.43 0.20
Manipulative-Computer 1 4.09 0.65 -3.79 0.21
Pretest x Manipulative 1 29.49 0.22 0.41 1.51
Pretest x Computer 1 6.02 0.58 -0.17 0.31
Pretest x Manipulative-Computer 1 1.09 0.81 0.08 0.06









Table 12

Analysis of Covariance: Fraction Instrument


Source DF Type III Parameter p-value F
SS Estimate


Model 4 1074.63 0.00 12.80
Error 59 1238.35
Fraction Pretest 1 679.53 0.48 0.00 32.38
Manipulative 1 67.42 -3.36 0.06 3.21
Computer 1 9.15 1.25 0.18 0.44
Manipulative-Computer 1 34.21 -2.06 0.17 1.63


manipulative-computer group still did not appear to have different posttest scores than

students in the Control group.

Not all unplanned learning is effective. Incidental learning is generally not

recognized or labeled as learning by the learner or others; therefore, it is difficult to

measure.


Limitations of the Study

Several limitations must be considered when interpreting the results of this study.

Due to the time of year, the classes were intact and nonrandomly assigned to treatment

and control groups. This is not uncommon among studies conducted in school settings.

There may have been preexisting differences in the intact classes to begin with. For

example, the computer/concrete manipulative group was a higher scoring class to begin

with. Because of intact classes, it was not possible to take several classes of students and








include them in each group. Therefore, only one class of students, which included 16-25

students, was involved in each group. This is the reason for the small sample size.

The Probability Explorer was new software that had never been used in whole

class instruction. Therefore, the researcher was unable to compare the results to that of

students who used the Probability Explorer software in whole class instruction. In

addition, the software was only designed for use in IBM computers, which limited where

it could be used in the school district.

The duration of the study was only 4 days. The question might be raised whether

4 days is sufficient time to develop the concepts completely and effectively with

computers and concrete manipulatives. The researcher believes that because traditional

teaching practices are still used in many classrooms, students may not be used to a

constructivist learning environment where students are actively engaged in their learning.

Thus, it may take students a few trials to become adjusted to different teaching and

learning styles. However, because of the schools curriculum, school administrator and

teachers limited outside activities to a few days.

Finally, the researcher has been a mathematics teacher in the southeastern United

States for more than 10 years and was aware of the possibility that the researcher

influencing results may introduce a threat to validity. Using specific preplanned lessons

for students in all the study classes minimized this.














CHAPTER 5
CONCLUSION


Summary

The purpose of this study was to examine the effectiveness of concrete and

computer-simulated manipulatives on elementary students' probability learning skills and

concepts. There were four heterogeneous groups with one class of students in each.

Treatment Group I was allowed to use concrete manipulatives for the lessons; Treatment

Group II participated in computer simulations for the lessons; and Group III participated

in a combination of both concrete manipulatives and computer simulations for the

lessons. The control group was taught the lessons using traditional means of instruction.

The objective was to determine the effect of computer-simulated manipulatives and

concrete manipulatives on elementary students' probability learning skills and concepts

on the dependent variable of posttest scores on the EPI.

The sample consisted of 83 fifth-grade students. The classes remained intact;

hence, random assignment to treatment groups was not possible. The researcher taught

all classes. Following a 2-week break, students in both the treatment and control groups

were administered the EPI and the EFI as pretests. Approximately 2 weeks elapsed

between the first and second testing sessions during which time the instruction took

place.

An ANCOVA was used to examine the posttest scores from the EPI. Based on

the ANCOVA, it was concluded that students experiencing computer instruction, who