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An evaluation of the use of microcomputer-based laboratory instruction on middle school students' concept attainment and attitudes towards computer -based instruction

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An evaluation of the use of microcomputer-based laboratory instruction on middle school students' concept attainment and attitudes towards computer -based instruction

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AN EVALUATION OF THE USE OF MICROCOMPUTER-BASED
LABORATORY INSTRUCTION ON MIDDLE SCHOOL STUDENTS’
CONCEPT ATTAINMENT AND ATTITUDES TOWARDS
COMPUTER-BASED INSTRUCTION
by
Sergio Albina Osio
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the degree
Doctor of Philosophy
(EDUCATION)
May 2002
Copyright 2002 Sergio Albina Osio
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UMI Number: 3073828
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UNIVERSITY OF SOUTHERN CALIFORNIA
The Graduate School
University Park
LOS ANGELES, CALIFORNIA 90089-1695
This dissertation, written by
SERGIO ALBINA OSIO
Under the direction o f his Dissertation
Committee, and approved by all its members,
has been presented to and accepted by The
Graduate School, in partial fulfillment o f
requirements for the degree o f
DOCTOR OF PHILOSOPHY
i ofGraduate Studies
Date May 1 0 , 2002_________
DISSERTATION COMMITTEE
A h
Chairperson
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Sergio Albina Osio William F. McComas, Ph.D.
ABSTRACT
AN EVALUATION OF THE USE OF MICROCOMPUTER-BASED
LABORATORY INSTRUCTION ON MIDDLE SCHOOL STUDENTS’
CONCEPT ATTAINMENT AND ATTITUDES TOWARDS
COMPUTER-BASED INSTRUCTION
The advent of instructional technology has become an integral part of the learning
process, thought by many to be a vital component in the reform of science instruction.
Microcomputer-Based Laboratory (MBL) is an instructional technology environment
in which a computer is connected through its Universal Serial Box (USB) port or an interface
(for older models) with sensors to control the experiment, collect data, and generate and
interpret graphs. In this study, MBL tools and instructions were used to design an
instructional program that integrated ideas in teaching thinking skills so that middle school
students from varying levels of differentiated achievement could categorize and create
concepts grounded on the Concept Attainment Model of teaching. Data were collected and
analyzed based on the procedures of a one-groiq> pretest-posttest experimental design and
two research questions. The study provided a quantitative correlation of variables such as
MBL to students’ pretest-posttest total scores; pretest-posttest total scores to the levels of
differentiated achievement and treatment groups. Likewise, a modified student-computer-
attitude survey was administered to evaluate students’ attitude toward the use of computer
technologies. The research findings revealed a 9.1% increase in test scores in the three
1
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concepts of investigation; multiple increases in test scores in the three levels of differentiated
achievement (22.2% for regular science group, 6.2% for accelerated science group students,
and 1% for sheltered science group); and 11.1% mean difference between the MBL group
and traditional laboratory group. Simultaneously, participants showed a positive significance
of 77% feeling o f comfort and confidence towards the use o f computer technologies.
Eventually, the great potential o f MBL technology could play an important role in the reform
of science education in the schools of the second largest Unified School District in the country
today.
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DEDICATION
To my beloved parents, wife, children, brothers and sister
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ACKNOWLEDGEMENTS
I wish to express my utmost gratitude to my advisor Dr. William F.
McComas and the members of my dissertation committee, Dr. Michele D. Crockett
and Dr. Julie G. Nyquist for their academic guidance. Also, special thanks go to Dr.
Kathryn Alesandrini, Dr. Chogallah Maroufi, and Dr. Webster Cotton, California
State University Los Angeles for their inspiration and enlightenment.
My heartfelt thanks go to my fellow workers at Thomas Starr King Middle
School, Sylmar High School, Wilson High School and Birmingham High School,
Los Angeles Unified School District, Los Angeles, California, for their assistance,
team-support, valuable comments, and constructive criticism.
I am dearly indebted to Ms. Cynthia Nielson, Southwest Sales
Representative, PASCO Scientific, who donated the much needed MBL equipment
to my school. Special thanks go to the late Dr. Offie Garovillo, Romy Morales, and
Sarah G. Novak and Emily Egging of USC for their unselfish efforts on my behalf..
Finally, I will be remiss if I will not mention my wife Annie, daughter Hazel,
son Sergio n, brothers Reynaldo, Alfredo, Bernardo, Rodrigo, and sister Alice, for
their most valuable moral and spiritual support, inspiration, and encouragement.
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TABLE OF CONTENTS
Page No.
DEDICATION ii
ACKNOWLEDGEMENTS iii
LIST OF TABLES viii
LIST OF FIGURES ix
CHAPTER
1 INTRODUCTION 1
Overview I
Purpose of the Study 5
Significance of the Study 6
Research Questions 8
Overview of Experimental Method 9
Assumption, Limitation, and Delimitation 12
Definition of Terms 13
Organization of the Report 15
2 REVIEW OF THE LITERATURE 17
Introduction 17
Uses of Educational Technology in
Science Education 18
Formats of Technologies of Instruction 19
Frameworks Influencing Technologies
of Instruction 26
Cognitive Science and Technologies
of Instruction 26
Constructivist Frameworks and Technologies
of Instruction 29
Concept Attainment and Technolgies
oflnstruction 36
Teaching and Learning Science with Technology 49
A Taxonomy of Instructional Technology 50
iv
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TABLE OF CONTENTS (continued)
CHAPTER Page No.
Historical Development of Computer-Based
Education 54
The Microcomputer-Based Laboratory in Science
Instruction 58
Current Educational Trends in Instructional
Technology 78
3 METHODOLOGY 85
Introduction 85
Research Questions 86
Sample Selection and Procedures 87
Research Design of the Study 90
The Experimental Treatment 78
Instructional Strategies and Investigation 95
Instructions 95
Administration of the Posttest 104
Reliability and Validity Issues 105
Other Statistical Measures 106
Student Computer Attitude Survey 107
Data Analysis 109
Summary of the Methodology 109
4 RESULTS AND ANALYSIS 112
Overview of Statistical Procedures 112
Characteristics of Participants 114
Description of Results for Two Research Questions 115
Summary of Results for Research Question 1 125
Summary of Results for Research Question 2 132
Summary of the Description of Results 133
5 SUMMARY, DISCUSSION, AND
RECOMMENDATION 134
Summary 134
Discussion and Implications 140
Recommendations for Further Research 146
Conclusion 147
REFERENCES 150
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TABLE OF CONTENTS (continued)
APPENDICES Page No.
ONE INFORMED CONSENT FORM 167
TWO LETTER FROM THE RESEARCH AND
EVALUATION BOARD: LAUSD 169
THREE LETTER FROM ADMINISTRATOR 171
FOUR PRETEST FOR THERMODYNAMICS 173
FIVE PRETEST FOR ELECTRICITY 176
SIX PRETEST FOR LIGHT 179
SEVEN TRADITIONAL LAB ACTIVITY 1: HOT AND
COLD WATER THERMODYNAMICS 182
EIGHT TRADITIONAL LAB ACTIVITY 2: INTENSITY OF
LIGHT VS. DISTANCE 184
NINE TRADITIONAL LAB ACTIVITY 3.
ELECTROCHEMICAL CELL 186
TEN MBL ACTIVITY 4: HOT AND COLD WATER
THERMODYNAMICS 188
ELEVEN MBL ACTIVITY 5: FRUIT BATTERY 192
TWELVE MBL ACTIVITY 6: INTENSITY OF LIGHT VS.
DISTANCE 196
THIRTEEN INSTRUCTIONAL PLAN FOR THERMODYNAMICS 199
FOURTEEN INSTRUCTIONAL PLAN FOR ELECTRICITY 201
FIFTEEN INSTRUCTIONAL PLAN FOR LIGHT 203
SIXTEEN MODIFIED STUDENT COMPUTER ATTITUDE
SURVEY 205
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LIST OF TABLES
TABLE Page No.
1. The Taxonomy of Technology as Media 52
2. Summary of Past Reviews on the Effects of CBE
from 1980-1988 56
3. Characteristics of MBL Tools 62
4. Classification of Participants Based on Academic Tests and
Language Status 88
5. Assignment of Subjects to MBL or Traditional Group
in Physical Science 89
6. Measures o f Reliability of the Pretest Total Score 117
7. Measures of the Variability of the Pretest Total Score 118
8. Frequency of Valid Percent of Posttest 120
9. Summary o f Descriptive Statistics for Pretest-Posttest
Comparison (N = 149) 121
10. Summary o f Means: Pretest-Posttest Total Score *
Level of Cognitive Development (N = 149) 122
11. Analysis of Variance of Pretest-Posttest With Level of
Differentiated Achievement 123
12. One-Way Anova Post Hoc Range Test on Level of Differentiated
Achievement 124
13. Summary of Means: Pretest-Posttest Total Score *
Treatment Group 124
14. Analysis o f Variance of Pretest-Posttest With Treatment Group 125
vii
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LIST OF TABLES (continued)
TABLE Page
15 Principal Component Analysis: SPSS Varimax Rotation
for Modified Student Computer Attitude Survey 129
16. One-Way ANOVA Post Hoc Test o f Attitude Survey and Level of
Differentiated Achievement 131
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LIST OF FIGURES
FIGURE Page No.
1. Complementarity o f Two Views of Constructivism 33
2. Syntax of the Concept of Attainment Model 39
3. Instructional Plan to Teach Concept of Measurement 43
4. The General Layout of an MBL System 61
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CHAPTER 1
INTRODUCTION
Overview
Changing demographics, a better understanding of cognitive processes, the
increased need for scientifically literate citizens, and more sophisticated
instructional technologies have created a potential revolution in the teaching and
learning o f science. The advent of instructional technology in particular has
become an integral part of the learning process, thought by many to be a vital
component in the reform of science instruction. Research and development in
instructional technology such as interactive videodisc (IVD), computer assisted
instruction (CAI), calculator-based laboratory (CBL), hypermedia, simulations,
microworld (computer laboratory simulating microworld environment), expert
tutoring systems, telecommunication (Internet, asynchronous learning networks,
HTML), and other relevant microcomputer-based innovations continue to initiate
novel instructional strategies that can be applied in the science classroom.
Microcomputers are transforming our views on what should be included in
meaningful educational plans. They are immensely significant as a medium in raising
achievement levels in various academic areas relative to a traditional teaching
approach (Kulik & Kulik, 1986; Roblyer, Castine, & King, 1988; Wise, 1988;
Leonard, 1992; Windschitl & Andre, 1998). They have shown the power to
1
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motivate students (Johnson, 1982; Cox & Berger, 198S) and enhanced students’
positive attitudes toward subjects in the higher grade levels (Bangert-Drowns,
Kulik, & Kulik, 1985; Delcourt & Kinzie, 1993; Han, 1994).
Computers display positive effects on academic performance when used with
lower ability learners (Bangert-Drowns et al., 1985; Edwards, Norton, Taylor, Weiss,
& Dusseldorp, 1975) and exhibit reduced learning time for subjects in the higher
grades (Kulik, Kulik, & Banguert-Drowns, 1985). They have increased children’s
involvement in the learning process especially those with poor academic performance
(Sheingold, 1991) and have been found to be effective in increasing achievement
levels of treatment groups over those o f control groups in areas of reading,
mathematics, language, and science (Roblyer et al., 1988; Shrum & Adams, 1990;
Leonard, 1992).
The teaching and learning with computer technology depicts a history of
about 35-40 years. In the 1960s, digital computers were applied to support
teaching and learning for instructional computing (programming, hardware and
software concepts, statistical analysis). In the 1970s, they were employed for
computer assisted learning (drill and practice, tutorial, simulation and gaming,
problem solving, exploration). In the 1980s, computer technology was utilized
for its support tools (word-processiqg, spreadsheets, graphics packages,
database management, telecommunication), and for delivery fo r multimedia
(CD-ROM), multimedia programs). Finally, in the 1990s to the present, computer
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technology is utilized for communication links (distance learning, asynchronous
learning network (ALN), Internet) (Gillespie, 1998; Mayadas, 2000; ALN, 2000).
Just as microcomputers play a central role in developing scientific
knowledge, they also facilitate the learning of science. Microcomputers
complement an integral classroom tool for acquiring, manipulating, and
communicating of data in ways which allow students to more actively participate in
research and learning (NSTA, 1990). Manipulating data may be performed when
computers are connected through their Universal Serial Box (USB) or an interface
(for older models) with sensors to control the experiment, collect data, and generate
and interpret graphs. This learning environment is known as the microcomputer-based
laboratory (MBL).
A microcomputer-based laboratory (MBL) is a system that couples a data
gathering device through a USB port in the computer, or for older models through a
converter (usually analog to digital), to a microcomputer. MBL includes the use of
different data collection probes such as temperature probes, pulse-rate probes,
sound detectors, light-sensing probes, biofeedback probes, humidity sensors, pH
probes, and probes that measure magnetic fields (PASCO, 1999; Vernier, 1998).
The computer is then used to record data coming from a probe and provide graphs
for the data and other such visualizations o f data (Friedler & Tamir, 1990; Mokros
& Tinker, 1987; TERC, 1984; Tinker, 1985). Similarly, the collection and
3
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organization o f data can develop in students a new understanding of scientific tools
(Tinker, 1985a) and enable them to visualize dynamic relationships among physical
quantities (Linn, Layman, & Nachmias, 1987; Shram & Adams, 1990; Escalada &
Zollman, 1996).
According to Han (1994), MBL as a learning environment is compatible
with various learning theories because it impacts curriculum (e.g., emphasizes
hands-on problem solving), instructions (e.g., encourages students to analyze,
interpret, and predict information), and assessment ( e.g., students play a larter role
in judging their own progress). Some of these learning theories are student-
centered learning (Wilkinson, 1988; Dixson, 1992), constructivism (von
Glasersfeld, 1981; Wheatley, 1991), and cooperative learning (Johnson &
Johnson, 1976, 1986; Bellamy & Fogarty, 1991; Ernest, 1995). Research studies
(Shrum & Adams, 1990; Escalada & Zollman, 1996) have linked the use of
MBL to conceptual change learning (Beeth, 1998; Griffits & Preston, 1992;
Osborne & Freyberg, 1985; Smith, 1991; Windschitl & Andre, 1998), which is a
derivative of constructivist learning models (Hassard, 1992). As students interact
with technology, they learn through the use of a constructivist learning
framework by integrating visual and verbal thinking (Collins, 1991). Finally,
Kozma (1991) supports an environment in which learners actively work with a
medium (e.g., computer) to construct knowledge, and the medium and the
4
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methods can produce the same or different learning depending on the kind of
medium being used by the learner.
Overall, the past two decades have represented a time of experimentation
with the use of microcomputers in education. Educators who have integrated
instructional technology such as MBL in their classrooms have encountered
divergent learning conditions. Some instructors have been successful with MBL,
while others who have not yet had success abandoned the idea. A few teachers have
just recently started, and have yet to see the results of using MBL (Han, 1994). As
yet, there are no sure paradigms of what constitutes effective instructional strategies
using MBL technology. Hence, research about the effects of MBL and the related
variables is important to formulate recommendations for fundamental change in the
practice of science instruction in the new millenium.
Purpose of the Study
This research study investigates the effect o f microcomputer-based
laboratory (MBL) tools and activities in impacting students’ concept attainment
through hands-on and minds-on activities about the concepts of thermodynamics,
light, and electricity in the setting o f a middle school physical science classroom.
The three concepts will be presented using an information-processing concept
attainment model of teaching (Joyce & Weil, 1996). Concurrently, the study
describes the impact on an attitude variable as related to the use of MBL.
5
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Significance of the Study
The significance of the study revolves around on the potential o f
instructional technology (e.g., MBL tools and activities) to provide demonstration
and laboratory experiences for middle school students with varying levels of
differentiated achievement to enhance their concept attainment and attitude about
computer technology in the science classroom. Visualization of phenomena through
demonstration, simulation, or real-time graphs (Escalada & Zollman, 1996) can
contribute to students’ understanding and reinforcement of physical science
concepts.
During the past two decades, science educators have reflected on the
philosophy and goals that should guide science programs and the science
curriculum into the new century. This period was marked by over 300 documents
by convened committees that have had and will continue to have a powerful impact
on the direction of science education (DeBoer, 1991; Hassard, 1994). These
convened committees have supported the integration of science, technology, and
mathematics in the elementary and secondary science curricula. In the elementary
science curricula, the committees postulated that computing will play a significant role
in its educational programs (BSCS, 1989). Likewise, the convened committees argue
that for technology and mathematics to be integrated, computers must become an
integral part of the instructional process. As such, these committees have strongly
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endorsed the integration o f technology and mathematics in both middle and high
school curriculum (AAAS, 1989; NSTA, 1990; NSTA, 1981).
As a result, the use o f computer technology in physics, chemistry, biology,
and mathematics has increased greatly in the last twenty years. Computers have
provided students with quick and easy access to various forms of information
through the Internet or extensive CD-ROM databases (Fuller & Zollman, 1995).
Computers have also reinforced concepts by various forms of drill, practice, and
tutorial work. When MBL are connected to USB devices or interfacing devices for
older models of microcomputers, they have been useful in laboratory situations for
data analysis and collections. These laboratory situations provide students with the
important experience o f meeting nature as it is rather than in its idealized form and
the opportunity to develop skills in scientific investigation and inquiry (Escalada &
Zollman, 1997).
Synonymously, to develop scientific investigation and inquiry, teachers
should teach students concepts and come to a shared meaning of the concepts.
Without a good understanding o f these concepts, students will have difficulty even
understanding the basics. The process of learning new concepts may be
accomplished in a variety o f ways. One way is to use concept attainment model of
teaching and integrate it with technology. The purpose of using concept attainment
model of teaching is to help students gain an in-depth understanding o f a particular
broad concept by using intellectual processes, including inductive and conceptual
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thinking in order to categorize ideas (Bruner, Goodnow, & Austin, 1967; Joyce &
Weil, 1996).
From this rationale, the concept attainment approach with MBL technology
in science instructions might be an answer (not the answer) to the question of why
students experience conceptual difficulties or misconceptions in understanding
physical science (e.g. physics and chemistry) concepts. Likewise, the use of MBL
instructions and activities in enhancing students’ concept attainment might be a
novel way to expose (externalize), modify or bring closure to a learning situation
(e.g., conceptual difficulties or misconceptions) (Pintrich, Marx, & Boyle, 1993).
Furthermore, the use of integrating concept attainment approach and MBL
instructions and activities in this study might be an avenue of promoting affective
skills (e.g., attitude) towards computer-based technologies among middle school
students from low income families in an urban-inner city school of Los Angeles,
California.
Research Questions
This study will attempt to answer the following research questions:
I. What is the impact of the use of microcomputer-based laboratory activities in the
physical sciences on concept attainment among middle school students o f varying
levels o f differentiated achievement?
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2. What is the impact of the use o f microcomputer-based laboratory activities in the
physical sciences on attitudes towards computer-based technologies among middle
school students of varying levels of differentiated achievement?
Overview of Experimental Method
The participants in the study included of 149 eighth-grade physical science
students from an inner city school in Los Angeles, California. They were classified
into three levels of cognitive development (accelerated, regular, and sheltered
science level) based on the results of 1999-2000 Stanford-9 Test, Aprenda Test
(Spanish only) and language status (e.g., home language survey). Each level was
further subdivided into two sub-grous each which performed three concept
investigations composed of traditional and exploratory activities. See Appendices 7
to 12 for a complete description of this aspect of the investigation.
Various research methods were employed to answer the two research
questions: a one-group pretest-posttest experimental design (Gall, Borg, & Gall,
1996; Isaac & Michael, 199S) and a student computer-attitude survey (Delcourt &
Kinzie, 1993) on the use of computer technologies. Three MBL activities from
DataStudio, a tool for Science Workshop (PASCO Scientific, 1999), were used in
the investigations.
A one group pretest and posttest experimental design (Gall et al., 1996;
Isaac & Michael, 199S) was used to determine the effects of the experimental
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treatment (e.g., MBL). The one-group design involved three steps that included (I)
administration of a pretest measuring the dependent variable, (2) implementation of
the experimental treatment or independent variable (e.g., MBL) to the participants,
and (3) administration o f a posttest that measured the dependent variable again.
The experimental treatment was composed of three MBL activities from the
Science Workshop™ (PASCO Scientific, 1999) series. The activities were Mixing
Hot and Cold Water (using the Temperature Sensor) for thermodynamics', the Fruit
Battery or Wet Cell (using the Voltage Sensor) for electricity-, and the Light Versus
Distance (using the Light Sensor) for light. The effect of the experimental
treatment (use of the MBL instructional strategy) was determined by comparing the
pretest and posttest scores for content acquisition.
Data were analyzed using descriptive and inferential statistical methods
(means, standard deviations, kurtosis, skew, frequency, one-way analysis of
variance (ANOVA), and post hoc range test) applicable to a one-group research
design. The F-test (one-way analysis of variance) was used to measure the
variability of pretest-posttest total scores to the three levels of differentiated
achievement and two treatment groups. That is, in the pretest total score and
posttest total score, the amount of between-groups variance in individual scores
was compared with the amount of within-groups variance in each test. The
variances of the pretest-posttest total scores were compared to the variances of the
three levels of differentiated achievement and two treatment groups.
10
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A Principal Component Analysis (PCA) was performed on the reliability of the
19-item modified student computer attitude survey (m-SCAS). Since there were
19 items in the survey instrument, a search for clusters of variables that were all
correlated with each other were identified. In the survey instrument, Delcourt &
Kinzie (1993) used three structural factors to cluster the 19 variables. Factor I is
identified for Comfort/Anxiety, Factor n for Usefulness (positively phrased,
specific content), and Factor III for Usefulness (negatively phrased, general
content). Likewise, factor loadings (e.g., correlations) were computed using SPSS
10 for Windows.
A modified version of the survey instrument was created by the researcher
to suit the experimental study. That is, the various computer technologies used in
the original survey were changed to MBL technology but the same original clusters
were adopted for facility. Responses to the modified-computer attitude survey
(m-SCAS) were subjected to a principal component analysis (PCA) to find the total
variance. The PCA used Varimax and oblique rotation to display the factor
loadings (e.g., communalities or squared multiple correlations) of each item. SPSS
10 for Windows (1999) statistical software was used in all statistical analyses in
this research study.
11
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Assumption, Limitation, and Delimitation
The study assumed that the participants were enrolled in the physics portion
of the physical science curriculum offered in the eight-grade physical science. It is also
assumed that the students have had some formal computer experience but not in MBL
activities.
The collection of data presented a number of limitations. The target sample
(N=149) is limited to six classes (out of 30 classes totalling 850 eight-grade
students) of science students and teachers from a single school. Only two sets of
MBL equipment were available.
Limitations were that the students belong to lower income families, multi
ethnic, were mixed-gender, were mixed-ability, and were mostly Limited English
Proficient (LEP) or English Language Learners (ELL) in an inner city school o f Los
Angeles County, California. Added to this, the school obtained a low Academic
Performance Index (API) o f 2 out o f 9 based on the results of the 1999-2000
Stanford-9 Test administered to all middle school schools in the Los Angeles
Unified School District (LAUSD, 2000).
By design, the study was limited only to three MBL exploratory activities:
thermodynamics, electricity, and light. The three exploratory activities were
chosen for this particular study because students succumb to many conceptual
difficulties arising from the study of these concepts. Thus, it did not incorporate or
12
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evaluate the impact o f several exploratory MBL tools and activities found in
DataStudio " (PASCO, 1999) and in similar products.
One delimitation o f this study was the level of cognitive development of
students selected, tested, and observed. They ranged in academic standing and
language-ability from accelerated to regular to sheltered science grouping and were
predominantly Latino, Asian, Armenian, and African-American. Consequently, the
research findings cannot be generalized outside the boundaries of the sample.
Likewise, another delimitation was the kind of technology training, knowledge
obtained, and teaching experience o f the teacher.
Definition of Terms
In this section, terms that need special clarification are defined and
explained. These terms are defined specifically for the purpose of this study, and
they may or may not be defined generally in the same manner.
Attitude towards MBL— The students’ attitudes include value, enthusiasm,
enjoyment, and the benefits and advantages they perceive of MBL in science learning
(Delcourt & Kinzie, 1993).
Computer-Based Education (CBE)—/deludes computer-assisted instructions (CAI)
and refers to drill and practice or tutorials; computer-managed instruction (CMI),
generally refers to computer evaluations of students’ test performance, guides
students to appropriate instructional resources and record keeping; computer-
13
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simulated experimentation (CSE); microcromputer-based laboratory (MBL), referring
to an interface between a computer and a data-collecting device (Berger et. al., 1994).
Constructivism—7he view that students construct their knowledge from individual or
interpersonal experiences or both and from reasoning about these experiences (von
Glasersfield, 1995; Staver, 1994).
Educational Technology—Is concerned with the educational application of
technologies and not the myriad o f uses of technologies in modem society. It also
examines those aspects of education which depend on (usually new) technologies.
(Bruce & Hogan, 1998).
Instructional Design— Is the systematic development of instructional specification
using learning and instructional theory to ensure the quality o f instruction (Seels &
Glasgow, 1990).
Instructional Development— The process of implementing instructional design
(Gentry, 1994).
Instructional Technology—Is a field whose basic purpose is to promote the
application of validated practical procedures in the the design and delivery of
instruction. It is often defined either in terms of media and other technology (e.g.,
audiovisual media and equipment, computers, etc., or in terms of a systematic process
which encompasses instructional design, development delivery, and evaluation.
14
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Microcomputer-based laboratory (MBL)— Is a system that couples a data gathering
device—an analog-to-digital converter—with a microcomputer. The microcomputer
is used to record the incoming information from a sensor to produce graphs (TERC,
1984).
Sheltered Classes—Are academic classes where students attend a bilingual program
before they are mainstreamed. The programs vary from beginning to advanced level
of English as a Second Language (ESL) or First Language instruction. The
instruction is made through Specially Designed Academic Instruction in English
(SDAIE).
Organization of this Report
Five chapters comprise the whole research study.
Chapter 1 (Introduction) includes the introduction itself, the significance of
the study, purpose of the study, research questions, methods of study, definition of
terms, and organization o f the report.
Chapter 2 (Review of the Literature) reviews relevant literature which
provides a more detailed description o f the rationale of the study, a background for
the study design and a theoretical framework for the interpretation of results. The
review of the literature includes the following topics: uses o f educational technology
in science education, frameworks influencing technologies of instruction, teaching and
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learning science with technology, the microcomputer-based laboratory in science
instruction, and recent trends in instructional technology.
Chapter 3 (Methodology) describes the procedures for the collection and
analysis of data. These included the sample selection and procedures, procedures of a
one-group pretest-posttest design, reliability test of written documents, and the
administration o f a modified-student computer attitude survey (m-SCAS).
Chapter 4 (Results and Analysis) presents the results of the internal
consistencies o f the written documents (pilot and pretest), identification o f
conceptual difficulties, pretest-posttest comparisons, and pretest-posttest total score
comparison to levels of differentiated achievement and treatment group. Descriptive
and inferential statistical procedures applicable to a one-group pretest-posttest
research design (frequency, means, standard deviation, skewness, kurtosis, one way
ANOVA, and post hoc range test) were used. Also, a Principal Component Analysis
(PCA) was utilized to extract factor loading of the survey instrument using an SPSS
10 for Windows (1999) statistical software.
Chapter 5 (Summary, Discussion, and Recommendation) provides a
summary of results, a synthesis of research findings, discussion and implications,
and recommendations for further research on the potential o f MBL in the active
process of learning physical science concepts.
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CHAPTER 2
REVIEW OF THE LITERATURE
Introduction
Some educators see instructional technology-based science activities as
distracting and confusing to students, while others view these sorts of activities as
enhancing student learning and motivation (Weller, 1996). Weller contends that many
educators, regardless o f where their beliefs lie on the continuum, hope that such
instructional technology-based activities would substantially help teachers provide
students with efficient and effective opportunities to learn both science’s products and
processes. As educators we always seek strategies to improve students’ education.
This chapter will discuss investigations and opinions related to the research
of microcomputer-based laboratory (MBL), a form of instructional technology,
with respect to science teaching and learning. Instructional technology is defined
as a genera o f computer-based education (CBE) in the form o f computer assisted
instruction (CAI), interactive videodisc (IVD), computer simulation experiments
(CSE), microcomputer- based laboratory (MBL), microworld, hypermedia,
multimedia, expert tutoring systems (artificial intelligence), and synchronous and
asynchronous communication. Likewise, instructional technology is defined as the
systemic and systematic application o f strategies and techniques derived from learning
and instructional theories to the solution o f instructional problems.
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Furthermore, instructional technology is often defined either in terms of
media and other technologies (e.g., audiovisual media and equipment, computers,
etc., or in terms of a systematic process which encompasses instructional design,
development delivery, and evaluation. Instructional design is the systematic
development of instructional specification using learning and instructional theory to
ensure the quality of instruction. Instructional development is the process of
implementing instructional design (Anglin, 1994; Seels & Glasgow, 1990; Gentry,
1994).
The following topics are presented in this review o f the literature that include
the uses of educational technology in science education; frameworks influencing
technologies o f instruction; teaching and learning science with technology; the
microcomputer-based laboratory in science instruction; and current educational
trends in instructional technology.
Uses of Educational Technology in Science Education
Educational technology refers to a discipline of study and practice that is
conventionally conceived in light of its two constituent terms. First, it is concerned
with the educational application of technologies and not the myriad uses of
technologies in modem society. Second, it examines those aspects of education that
are crucially dependent on (usually new) technologies (Bruce & Hogan, 1998).
The use of the term educational technology is obscure at best. Prior to the
advent o f the World Wide Web (WWW) it meant stand-alone computer systems or
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programmed instructions, but today, the technology in educational technology is
usually assumed to connote new communication and information technologies (Bruce
& Levin, 1997). Alternately, technology is not a separable component o f educational
practice, but rather a perspective, or set of perspectives, one may adopt on all
educational activity ((Bruce & Levin, 1997; MacKenzie & Wajcman, 1999).
In the classroom, the microcomputer ofiers the teacher more flexibility in
presentation, easier record keeping and better management of instructional
techniques. It offers students a very important resource for learning the concepts and
processes o f science by using various formats of technologies o f instruction such as
drill and practice, simulations, programmed instruction (PI), personalized system of
instruction (PSI), computer assisted instruction (CAI), computer managed instruction
(CMI), computer-based instructional design (CBI), model building (microworld), data
manipulation (MBL) and telecommunication.
These formats of technologies o f instruction can improve scientific learning
and facilitate communication of ideas and concepts. Lest the following emphasis on
computers be misunderstood, it asserts at the outset that microcomputers should
enhance, but not replaced essential hands-on laboratory activities. (NSTA, 1992;
Heinich, Molenda, & Russel, 1989; Fitzpatrick, 1998).
Formats of Technologies of Instruction
A technology o f instruction is a teaching or learning pattern designed to
provide reliable, effective instruction to each learner through application of scientific
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principles of human learning (Dorin, Demmin, & Gabel, 1990; Berger & Thompson,
1998; Gagne, Yekovich, & Yekovich, 1993). Various formats of technologies o f
instruction are discussed but do not by any means constitute a full listing of all
technologies of instruction. The earlier formats of technologies of instruction were
designed from the basics of behaviorism (Lemlech, 1998; Fleming & Levie, 1993;
Good & Brophy, 1990; Gagne, Yekovich, & Yekovich, 1993).
In Paul Saettler’s book The Evolution o f American Technology (Saettler,
1990), he states that behaviorism did not have an impact on educational technology
until the 1960s, the time that behaviorism began to decrease in popularity in American
psychology. Saettler associates six formats or fields of technologies of instruction that
show the affect of behaviorism on the use o f educational technology in U.S. education
which include 1) the behavioral objectives movement and the contribution of task
analysis in behavioral objectives (Burton, Moore & Magliaro, 1996); 2) the teaching
machine phase; 3) the programmed instruction movement; 4) individualized
instructional approaches; S) system approach to instruction and 6) computer-assisted
instruction. Saettler (1990) describes the six formats or fields as follows:
1. Task Analysis and Behavioral Objectives
Task analysis is about the identification and specification of observable
behaviors to be performed by the learner. Behaviorist theories provide many
rational bases for using behavioral objectives by 1) assisting in evaluating learners’
performance, 2) designing and arranging sequences of instruction, 3) communicating
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requirements and expectations, and 4) providing and communicating levels of
performance prior to instruction. Writing behavioral outcomes continues with
Outcomes Based Education (OBE) today. A principle of OBE is that education
should be clearly planned before delivering instruction.
2. Teaching Machines
These are self-scoring test devices (punchboard) which may not have been
sufficient for learning, but were useful adjuncts to other teaching techniques. To
solve problems of delayed reinforcement (due to lack of time, large student group,
etc.) and negative reinforcers in schools, Skinner tinned to the teaching machine
concept.
3. Programmed Instruction
These teaching texts or programmed books essentially had the same
characteristics as the teaching machines as to its logical presentation of content,
requirement of overt responses, and presentation of immediate knowledge of
correctness. Skinner used programming to refer to the design of carefully arranged
sequences of contingencies leading to the terminal performances which are the
object of education.
There are two kinds of programming. The first kind, called linear
programming, is a series of learning frames presented in a set of sequence.
However, this had a pall effect (i.e., boredom) due to numerous mini-steps and the
pattern of being correct all the time. The second kind, called intrinsic (branching)
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programming, is a correction of misconceptions and deferring the instruction o f new
matter until there has been clarification and education. These programs were
essentially naturalistic and kept students working at the maximum practical rate.
Programmed instructions may be delivered through a variety of instruments ranging
from workbooks to computers. It is widely used in schools, industry, and the military
for many academic subjects at all grade levels.
4. Individualized Approaches to Instruction
Individualized instruction began in 1900 and was revived in the 1960s. Some
examples of the individualized instructions in the U.S. are the Keller Plan,
Individually Prescribed Instruction, Program for Learning in Accordance with Needs,
and Individually Guided Education (Saettler, 1990).
Keller and his associates developed PSI, or the Keller Plan, for the university
college classes. This system used the principles of behaviorism and mastery
earning that contains five defining characteristics which are use o f proctors,
mastery learning, self-pacing, teacher as motivator, and use of written word.
5. Systems Approach to Instruction
The systems approach to instructions developed out of the 1950s and 1960s
focus on language laboratories, teaching machines, programmed instructions,
multimedia presentations, and the use of computers in instruction. Most systems
approaches are similar to computer flow charts with steps that the designer moves
through during the development o f instructions. Rooted in the military and
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business world, the systems approach involved setting goals and objectives,
analyzing resources, and devising a plan of action and continuous evaluation and
modification.
6. Computer Assisted Instruction
Computer-assisted instruction (CAI) was first used in educational training
during the 1950 by IBM, Gordon Pask, and O.M. Moore. CAI grew rapidly in the
1960s with federal funding for research and development in education and
industrial laboratory. Control Data Corporation (e.g., PLATO) and Mitre Corporation
(e.g., TICCIT) were two companies funded by the government to tests the
effectiveness of CAI. PLATO (University of Illinois) and TICCIT (Brigham Young
University) were CAIs composed of a computerized instruction, an authoring system,
and a learning management system. The courseware cover a span from kindergarten
through graduate school levels in every conceivable area including business and
industry training (Heinich, Molenda, & Russel, 1989; Arends, 1994).
However, in the mid-seventies CAI was not the success that people believed.
Some of the reasons include lack of support from certain sectors, technical
problems in implementation, lack of quality software, and high cost. Today, computer
assisted instruction (CAI) programs facilitate student learning through tutorials, drill
and practice, simulation, and problem solving. These formats of technologies of
instruction are discussed below.
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Computer-Based Education Formats
Other formats of technologies of instruction for CAIs include tutorials,
drill-and-practice, simulation, and for computer-based education (CBE) includes
interactive videodisc (IVD), microcomputer-based laboratory (MBL), and
synchronous and asynchronous telecommunication. The use of computer in MBL
technology is investigated in detail in the next section.
1. Tutorials
In the tutorial role, the computer acts as the teacher. All interaction is between
the computer and the learner. One example of tutorial method is Problem-Solving
Strategies, which guide learners through the application of three strategies, provide
instruction, practice, and feedback based upon student response.
Problem Solving Strategies is a package o f four programs for teaching
problem solving strategies in middle school math classes. The three strategies are trial
and error, exhaustive listing, and simplifying the problem. The first two programs,
Diagonals and Squares are highly interactive tutorials designed for an individual or
pair of students. The programs provide group work experience in applying all three
problem-solving strategies in a (Heinich et al., 1989; Rudell, 1993; Sharan, 1990).
2. Drill and Practice
The program leads the learner through a series of examples to increase
dexterity and fluency in the skill The computer cannot display impatience and goes
ahead only when mastery is shown. Drill-and practice is predominantly used for math
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drills, foreign language translating practice, vocabulary building exercises, and the
like. Other drill-and-practice such as Sentences, let the learners practice sentence
construction. Use of drill and practice is an efficient way to assist students in
committing information to memory but contributes very little to higher-order
thinking skills (White, 1992; Linn, 1988).
3. Simulation
In this method, the learner confronts a scaled-down approximation of a real-
life situation. It allows realistic practice without the expense or risks otherwise
involved. The computer-based simulation Operation: Frog allows a student to dissect
and reconstruct a frog using the same instrument that would be used in a biology
laboratory. The student must remove the twenty-three organs in sequence as in an
actual dissection. Likewise, a large number of civilian and military occupations
involve the operation or maintenance of complex equipment such as aircraft,
manufacturing machines, weapons systems, nuclear power plants, and oil rigs. Major
airlines and the military use computer-based simulation to reduce the amount of flying
time required for training (Leonard, 1989; Heinich et al., 1989; Goldstein, 1995).
4. Interactive Videodisk
A videodisc is an auxiliary storage device that employs laser technology to
present audio and video display. Interactive videodiscs (IVD, the interface o f a
computer and laser videodisc), have been used for interactive instruction. This
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technology makes it possible to access various combinations o f moving pictures, still
pictures, text, and sound almost instantaneously (Han, 1994).
5. Synchronous and Asynchronous Telecommunication
Finally, synchronous and asynchronous telecommunication refer to
communicating across distances, which expands the traditional boundaries of the
classroom. With a computer, communication software, modem, and a telephone line,
students and teachers can access information using databases and information
services, and share information through bulletin boards, electronic conferences,
electronic mail, and collaborative research projects. With telecommunications,
information from students and data sources outside of the classroom are accessible.
Using this mode, students and teachers can share programs and information in a
asynchronous exchange (Han, 1994; McLester, 1997).
Frameworks Influencing Technologies of Instruction
Cognitive Science and Technologies of Instruction
The technologies of instruction described so far were influenced primarily
by behaviorist frameworks, particularly reinforcement theory. But this branch of
psychology is no longer the major contributor to new developments. Because of
the limitations of behaviorism in dealing with higher mental processes and its failure
to recognize cognitive developmental (mental growth) influences on learning, other
instructional theories and development are shaping contemporary trends in various
technologies of instruction.
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Cognitive science includes the study of mental processes such as perceiving,
remembering and reasoning. Functions and processes of the mind are studied in an
attempt to ascertain what learners know and how they know it. The general
assumptions o f cognitivism includes 1) the mind is the agent o f learning, 2) the
role o f mental activities is to represent the real world, and 3) the mind regards
thinking as effective only if it adequately describes some “objective reality”.
Cognitive theory came into its own as an “extension of behavioral theory” (Winn &
Snyder, 1996, p. 112).
Experimental psychology started out in 1879 with a cognitive orientation,
but behaviorism soon became the dominant framework in American psychology.
After World War II, there was a rebirth of interest in cognitive psychology,
stimulated by both pressure from applied settings and the developments in
information science. An event spurred by the growth of information science was
the development o f the computer. That is, the computer became an important tool
in the objective measurement of mental process indicators (Gagne, Yekovich, &
Yekovich, 1993; Berger & Thompson, 1998).
Cognitive psychology has become the more dominant science. The name
derives from its emphasis on cognitive processes—the mental operation we engage in
when thinking. Because o f the many advances in understanding the physiology of the
brain (e.g., study o f artificial intelligence through computer simulations), cognitive
psychologists are more willing than behaviorists to theorize about the kind of
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mental operation that may be involved in learning (Gagne,Yekovich, & Yekovich,
1993; Heinich et aL, 1989; Berger & Thompson, 1998). Cognitive frameworks came
into its existence as an “extension of behavioral theory” ( Winn & Snyder, 1996, p.
12; Rumelhart, Hinton, & McClelland, 1986).
Application of cognitive science in technologies of instruction
Although cognitive psychology emerged in the late 19S0s and began to take
over as the dominant theory of learning, it was not until the late 1970s that cognitive
science began to influence technologies of instruction. Cognitive science began a shift
from behavioristic practices which emphasized external behavior to a concern with the
internal mental processes of the mind and how they could be utilized in promoting
effective learning. The influence of cognitive science in technologies of instruction is
evidenced by the use of advanced organizers, mnemonic devices, metaphors,
chunking into meaningful parts, and careful organization of instructional materials
from simple to complex (Anglin, 1995; Marshall, 1990).
Likewise, cognitivism is evidenced in computer-based instructions.
Computers process information in a similar fashion to how cognitive scientist
believe humans process information (receive, store, and retrieve). This analogy
makes the possibility of programming a computer to “think” like a person
conceivable (i.e., artificial intelligence). Artificial intelligence involves the
computer working to supply appropriate responses to student input from the
computer data base. A trouble-shooting program is one example of these data-base
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programs. Some o f these trouble-shooting programs are: 1) BUGGY (allows
teachers to diagnose causes for student mathematical errors), 2) LOGO (designed
to help children learn to program a computer), 3) SOPIE (helps engineers
troubleshoot electronic equipment problems), 4) MYCIN (diagnoses blood
infections and prescribes possible treatment), 5) PUFF (diagnoses medical patients for
possible pulmonary disorders), 6) SCHOLAR (teaches facts about South
American geography in a Socratic method), and 7) DENDRAL (analyze molecular
structure of unknown compounds) (Saettler, 1990).
Constructivist Frameworks and Technologies of Instruction
Cognitive psychology lends theoretical support to a movement in education
and training referred to as constructivist learning environment (CLE). Recent
research has suggested that students construct their own ideas about the world—
including concepts— and any attempt to teach new concepts to students must take
this into consideration. Furthermore, researchers have recommended that linking new
concepts to a students’ prior knowledge is an integral part o f learning. Researchers
who have supported this view have been labeled constructivists because of their
belief that students construct their own knowledge structures—concepts, beliefs,
theories (Hassard, 1994; Jonassen, 1991; von Glasersfeld, 1996).
Constructivism is a framework of knowledge with roots in philosophy,
psychology, and cybernetics (von Glasersfeld, 1989). While constructivism is
clearly gaining popularity as a new paradigm of learning, many question how
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the philosophy can be operationalized. They argue that it does not provide a
method, approach or particular pedagogy. However, at the same time, numerous
researchers and educators are busy designing what they refer to as a "constructivist
learning environment” (Fosnot, 1996; Jonassen, 1991; Salomon, 1997).
Moreover, agreement on constructivism as a learning theory is not
widespread due largely to what Derry (1996) terms ethnocentrism within various
constructivisms. At the same time, Ernest (1995) figures that, of the seven paradigms
of constructivism, the positions are all variants of radical constructivism The concern
as Ernest sees it is “to accommodate the complementarity between individual
construction and social interaction” (p. 483). Whether knowledge is seen as socially
situated or an individual construction has implications for the ways in which
learning is conceptualized. Even so, as Ernest claims in relation to the varying
constructivist perspectives “there is the risk of wasting time by worrying over
the minutiae o f difference” (p. 459). Maybe then, the best gambit for understanding
the constructivist perspective to teaching and learning is to considerwhat
constructivism is not.
There are two main opposing principles of constructivism One principle
relates constructivism as a “sound theory” (Driver, Asoko, Leach, & Mortimer,
1994; von Glasersfeld, 1995; Staver, 1994; Tobin, 1993) and the other as
a “flawed theory” (e.g., Mathews, 1992, 1994; Osborne, 1996; Philips, 1995).
Constructivism as a flawed theory will not be discussed in this study. First and
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foremost, my purpose in this study is to discuss and support my own and others’
affirmations that constructivism is a sound theory.
Bartlet (1932) pioneered what became the constructivist approach (Good &
Brophy, 1990). Constructivists believe that learners construct their own reality, or at
least interpret it based upon their perceptions o f experiences, so an individual’s
knowledge is a function of one’s prior experiences, mental structures, and beliefs
that are used to interpret objects and events. What someone knows is grounded in
perception of the physical and social experiences comprehended by the mind
(Jonassen, 1991).
Central to constructivism is its conception o f learning. Von Glasersfeld
(199S) argues that “from the constructivist perspective, learning is not a stimulus-
response (S-R) phenomenon” (p. 12). It requires self-regulation and the building of
conceptual structures through reflection and abstraction. Fosnot (1996) adds that
“rather than behaviors or skills as the goal of instruction, concept development and
deep understanding are the foci of learning” (p. 10). For educators, the challenge is
to be able to build a hypothetical model of the conceptual worlds of students since the
worlds could be very different from what is intended by the educator (von
Glasersfeld, 1996).
Dufly & Cunningham (1996) suggest that constructivism has become an
umbrella term with general views that learning is an active process of constructing,
not acquiring knowledge and that instruction is a process of supporting that
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construction. Salomon (1997) further explores assumptions underlying constructivist
framework and practice by examining two current views of constructivism.
According to Salomon (1997), the first view of constructivism is cognitivist
constructivism. Salomon describes cognitivism an approach to learning influenced by
Piagetian cognitive development. Knowledge is believed to be actively constructed,
tightly connected to the individual’s cognitive repertoire and to the context within
which this activity takes place. Hence it is situated. Learning activities are designed
to increase an individual’s skill and knowledge and emphasize the solo effects of
constructivist learning activities (Salomon, 1997; Papert, 1992).
Similarly, Salomon claims that the second view of constructivism is
socio-cultural constructivism. It is an approach to learning influenced by the
Soviet school of thought (Vygotsky). The concern focuses on the social process
of interaction and participation, the socially-based appropriation of meaning.
Learning activities are designed to enhance and explore the social process of
participation and changes that take place while students are engaged in collaboration,
problem solving, and team-based activities (Salomon, 1997). Salomon suggests that
the two views o f constructivism are complementary and represent two sides of an
ongoing dynamic process of reciprocal influences.
Figure 2 shows the complementarity o f the process o f reciprocal influence.
The reciprocal influence is marked by two opposing vectors for enhanced cognitive
enhanced construction (shared meaning) and social participation.
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FIGURE 1. Complementarity of Two Views o f Constructivism
Enhanced
cognitive
^ --------
(shared meaning) and
repertoire
< --------
) social participation
From Fitzpatrick, 1998, p. 2
While this inquiry considered only two dichotomies, no doubt there are many
others that are inspired by a constructivist philosophy either overtly or indirectly.
No doubt there are many teachers who, without knowing of the term, or without
having been informed of the theory, are providing the students in their care with
opportunities for constructivist learning. As von Glaserfield (1995, p. 3-16)
observes:
Constructivism does not claim to have made earth-shaking inventions
in the area o f education; it merely claims to provide a solid
conceptual basis fo r some o f the things that, until now, inspired
teachers had to do without theoretical foundation.
Application of Constructivist Frameworks in Technologies of Instruction
Novel teaching and learning constructivist frameworks espousing student-
centered learning, paired with technological developments, create exciting possibilities
for the transformation o f learning environments (Hannafin, Land, & Oliver, 1999).
The use o f constructivism in educational technology challenges the problem of inert
knowledge when people are specially prompted to remember it, but that is not
spontaneously used to solve problems, even though it is relevant (CTGV, 1997).
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Salomon (1997) describes technology as a provocateur that is providing novel
and very tempting tools though without, in many cases, having instructional rationales
and psychological underpinnings to justify its employment. Sarason (1984) reiterates
this need for purposeful and goal-directed use o f technology in learning environments
(Lawson, Abraham, & Renner, 1989).
Such new technologies, coupled with constructivist principles, redefine the
role o f information in learning environments. Does increased access to information,
afforded by new technologies improve the essence of learning? Perkins (1991)
suggests that independently, constructivism and information processing technologies
have much to offer contemporary procedures to instructions, and characterizes their
relationship as synergistic. He notes their potential for transformation of facets of the
learning environment.
Jonassen (1991) declares that the technological advances o f the 1980s and
1990s have enabled instructional designers to move toward a more constructivist
approach to instructional design. Some of the most useful tools for the constructivist
designer are hypertext and hypermedia because they allow for a branched design of
instruction rather than a linear format of instruction. Hyperlinks allow for learner
control, which is crucial to constructivist learning. However, there is some concern
for the novice learner “lost” in a sea of hypermedia. To address this concern,
knowledge acquisition should be served by classical instruction, instructional
interaction and criterion-referenced evaluation (Duffy & Jonassen, 1991).
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Moreover, in the science classroom, constructivist frameworks impact
learning (curriculum, instruction, assessment). In curriculum, constructivism calls for
the elimination o f a standard curriculum. Instead, it promotes using curricula
customized to the students’ prior knowledge and emphasizes hands-on problem
solving. In instruction, educators focus on making connections between facts and
fostering new understanding in students. Hence, educators tailor their teaching
strategies to student responses and encourage students to analyze, interpret, and
predict information. Teachers also rely heavily on open-ended questions and promote
extensive dialogue among students. Lastly, in assessment, constructivism calls for the
elimination of grades and standardized testing. Instead, assessment becomes part of
the learning process so that study plays a larger role in judging their own progress.
(Ertmer & Newby, 1991; Duffy & Jonassen, 1991; Lebow, 1993; Dick, 1991).
Summing up, novel teaching and learning constructivist frameworks create
exciting possibilities for the transformation of learning environments. Salomon (1997)
describes technology as a“provocateur” that provides novel and very tempting tools.
Sarason (1984) reiterates the purposeful and goal-directed use of technology in
learning environments. Such new technologies coupled with constructivist principles,
redefine the role of these learning environments. Does access to information, afforded
by new technologies, improve the essence o f learning? Perkins (1991) suggests that
constructivist frameworks and information processsing technologies have much to
offer technologies of instruction, and characterizes their relationship as “synergistic”.
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Concept Attainment and Technologies of Instruction
Constructivist frameworks lends theoretical support to models of instruction
which are used in the design of technologies o f instruction. These models are referred
to as information-processing models. They provide the learner with information and
concepts, some emphasize concept formation and hypothesis testing, and still others
generate creative thinking. The model that provides creative thinking is referred to as
the concept attainment model (Joyce, & Weil, 1996)
Concept attainment model, built around the studies o f thinking conducted by
Bruner (Bruner, Goodnow, & Austin, 1967), is a close relative of the inductive model
composed of concept formation, interpretation o f data, and application o f
principles. Designed both to teach concepts and to help students become more
effective at learning concepts, it provides an efficient method for presenting organized
information from a wide range of topics to students at every stage of development.
The model is discussed because it provides a way of delivering, clarifying, and
training students at developing concepts (Lemlech, 1998; Joyce & Weil, 1996).
Orientation to the Model
Concept attainment is the “search for and listing o f attributes that can be used
to distinguish exemplars from nonexemplars of various categories (Bruner et. al.,
1967, p. 233). In this model, students are required to figure out the attributes of a
category that is already formed in another person’s mind by comparing and
contrasting examples (called exemplars) that contain the characteristics (called
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attributes) o f the concept with examples that do not contain those attributes (called
negative exemplars). Exemplars are a subset o f a collection of data or a data set. The
category is the subset or collection of samples that share one or more characteristics
that are missing in the others. It is by comparing the positive
exemplars and contrasting them with the negative ones that the concept or category is
learned. On the other hand, attributes are the features of all items o f a set of data. To
create such lessons we need to have our category clearly in mind (Lemlech, 1998;
Joyce & Weil, 1996; Rudell, 1993).
Pritchard (1994) describes concept attainment as a teaching model that helps
students develop skills for inductive and deductive thinking while learning subject
matter in any field in a constructive and meaningful way. The model is an instructional
approach in which teachers guide students to derive an abstract, generic idea
inductively using pattern recognition and categorizing skills, and then help them
deductively apply the concept in new situations.
Johnson, Carlson, Kastl, & Kastl (1992) assert that one way for students and
their teachers to come to a shared meaning of a concept is by using the concept
attainment teaching model. The purpose o f the model is to help students gain an
in-depth knowledge and understanding of a particular broad concept. This model uses
a variety of intellectual processes, including inductive and conceptual thinking in
order to categorize ideas (Wilson, 1987). Pandey (1993) suggests that proportional
reasoning, combinatorial reasoning and general intelligence are very important
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intellectual processes for predicting the attainment of physics concepts. Likewise,
Nelson & Pan (1997) indicate that using interactive videodisk images, a form of
instructional technology, students utilize inferences and line drawings to construct
pattern recognition to attain biology concepts.
Strategies for Concept Attainment
Have we ever wondered whether the our students hold the same meaning as
we do about a concept? One method of coming to a shared meaning with our
students is the of use the concept attainment model o f teaching (Joyce & Weil,
1996). Joyce & Weil describe three factors to solve the above problem. First, we
design concept attainment activities or exercises in order to study how students think.
Second, make students learn to be more efficient by altering their strategies and
teaching them new ones. Third, change the way information is presented and modify
the model slightly to affect how students process information.
The purpose of concept attainment model is to help students gain an in-depth
understanding of a broad concept available in the exemplars. Do they concentrate on
just certain aspects of the information (partistic strategies), or do they keep all or
most o f the information in mind (holistic strategies) (Joyce & Weil, 1996). The model
uses a variety of intellectual processes, including inductive (see discovery learning)
and conceptual thinking in order to categorize ideas.
Conceptual thinking is a way of organizing and categorizing ideas in your
mind. The process o f learning a new concept may be accomplished in many ways.
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To categorize ideas when learning a new concept depends on past experiences and
knowledge. That is, new ideas are hooked onto or related to what we already know.
Then, concepts are sorted into categories according to their basic characteristics.
These characteristics must be present for the idea to fit into a particular concept
(Johnson et. aL, 1992; Norris, 1992).
Syntax o f the Concept Attainment Model
Figure 2 outlines the three phases of the concept attainment model.
FIGURE 2. Syntax of the Concept Attainment Model
Phase One:
Presentation of Data and
Identification of Concept
Phase Two:
Testing Attainment of the Concept
Teacher presents labeled examples.
Students compare attributes in positive
or negative examples.
Students generate and test hypothesis.
Students state a definition according
to the essential attributes.
Students identify addtional unlabeled
examples as yes or no.
Teacher confirms hypothesis, names
concept, and restates definitions
according to essential attributes.
Students generate examples.
Phase Three:
Analysis of Thinking Strategies
Students describe thoughts.
Students discuss role of hypothesis and attributes.
Students discuss type and number of hypothesis.
From Joyce and Weil, 1996, p. 173.
Instructional Plans with Concept Attainment Model
To design a concept-attainment instructional sequence for use in a particular
subject area, teachers carry our three steps that include 1) identifying a significant and
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definable concept in the areas, 2) analyzing the concept’ essential and defining
features, and 3) designing exemplars from which the concept can be derived. By
carrying out these steps, teachers strengthen their own understanding o f the
concept, its critical attributes and manifestations (Pritchard, 1994). Pritchard
provides a detailed description of these three steps in the design of a concept
attainment instruction.
Identifying Significant and Definable Concepts
Significant and definable concepts help students gain important footholds
in a subject matter and can be discretely defined in terms o f critical attributes
that they can understand. For example, in middle school science, students need to
learn the fundamental concept of life. They can do this with concept attainment
because life is a concept definable in terms that middle school students can
understand. Similarly, in high school chemistry or physics, the concept of energy
transformation is important for juniors and seniors and is clearly understood in
that age to recognize the critical attributes of the chemical and physical changes in
energy transformation.
In effect, teachers with more education and experience can often
instinctively identify and list core concepts their students need to learn. To do this,
they can “map” the subject area they teach in a more formal way by laying out a
semantic diagram or mapping o f the concepts they need to understand. For teachers
working in fields in which they have less education and experience, they can turn to
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text-book tables of contents and curriculum guides to find subject-area outlines of
significant concepts. Similarly, district or state Standards and Frameworks are
good sources to find significant and definable concepts. Whichever approach
used, teachers are potentially able and qualified to identify important core
concepts for their students to categorize, define and understand.
Analyzing the Concept’ s Essential and Definine Features
The critical attributes of a concept are the sole basis for understanding
and communication. Because a concept is an abstraction of objects, ideas, or
experiences that share critical attributes, people can attain a concept— abstract it—by
examining instances that reflect its attributes. This abstraction process, as explained
earlier, is the basis of concept-attainment instruction. When teachers use this model,
they present examples and non-examples of the concept and guide students in
discerning the critical attributes contained in the positive exemplars and missing
in the negative exemplars. Students then link these attributes into a hypothesis
of the concept. For this to occur successfully in the classroom, teachers must analyze
each concept and must discern the critical attributes of each. An example of analyzing
concepts essential and defining features is found in the instructional plan in teaching
the concept of measurement. (See Figure 3).
Teachers can carry out concept-analysis steps in any order, identifying the
concept name, rule or critical attributes, second or last. As they work to develop and
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integrate these three components, teachers typically find that they need to move back
and forth several times among them, checking one against another until they arrive at
a succinct concept name, a complete rule statement and all critical attributes of the
concept presented individually in the list.
D esisnins Exemplars to Derive the Concept
Teachers can visualize real-world scenarios to design positive and negative
exemplars of an active concept. Complete sentences are used to describe these
scenarios briefly. There are four advantages in this complete sentence-scenario
guideline. First, exemplars expressed in complete sentence-scenarios give intellectual
dimension to the concept. Second, sentence-scenario exemplars help students actually
envision the concept at work on the stage of their minds. This is helpful for visual
learners. Third, sentence-scenario exemplars present students with opportunities to
strengthen critical reading skills as they seek to abstract the evident critical attributes
of a concept. Fourth, sentence-scenario exemplars serve as guides for students’ own
construction of exemplars and for their expository writing in general.
Figure 3 presents an instructional plan designed by an eight-grade physical
science teacher. The plan includes a set of sentence-scenario exemplars divided into
two subsets for use in the first and second phases of the model. The plan also includes
the teacher’s complete analysis of the concept of interest—measurement—so that the
presence o f the concept’s critical attributes in the positive exemplar can be
determined.
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FIGURE 3. Instructional Plan to Teach Concept o f Measurement
Concept; Measurement
R ule: Measurement is the act of using a measuring instrument to
numerically describe the characteristics of physical phenomenon
in terms of recognized and accepted standard units.
C ritic a l A ttributes:
a. uses measuring instrument
b. describes numerically
c. uses standard units
d. describes physical phenomenon
Exemplars fo r D eriving the Concept:
1+ Celeste sells blue crabs to restaurants on the East Coast. She grades them as medium,
large and jumbo using a gap gauge that ranges from 5 to 7 inches.
2+ Paulo wants to gain weight for wrestling. He is using exercise and a high-protein diet
and recording his weight weekly as it is shown on the training-room scale.
3- Jim went deep sea fishing for half a day last Saturday. He caught 2
yellow-fin tuna and rated the experience as a “ 10”.
4- In the regional figure skating trials, Nadia’s did very well in creativity.
She received scores of 6.2,6.0, 6.4,6.S, and 6.1 from the judges.
5+ Tony is training for a 50K bicycle race. Each day he rides a 75K course and times
himself with a chronometer to determine his improvement.
6- The baseball coach emphasized that he uses focus and dedication as the most
important criteria in his selection of first string players.
From Pritchard, 1994, p 9
Likewise, Figure 3 shows that in planning a concept-attainment lesson,
teachers develop two sets of positive and negative exemplars to help students derive
the concept, then test and confirm it. The first set (number 1 to 6) will be presented to
students with labels so that they will have identifiable examples of the concept and can
analyze these for its critical attributes. The second set (number 7 to 12) will be
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presented without labels, and the students will be asked to assign positive and
negative labels to them on the basis o f the concept they have hypothesized using the
first set.
FIGURE 3. (Continued).
Exemplars fo r Testing and C onfirm ing the Concept Hypothesis:
7. Sara raises rare ferns in her small greenhouse. She has attached an alarm to the
thermometer to signal if the temperature changes more than 5 degrees. (+)
8. Chen was surprised to learn that it is possible to use a voltmeter to prove that some
kinds of fish generate 100 or more volts of electric current. (+)
9. Student research project proposals will be evaluated on a 10-point scale and those that
receive 7 or above have a good chance of receiving some funding. (-)
10. LaDon and Celia conducted a survey to find out how strongly the school’s math
teachers believe in giving assignments every day. (-)
11. Ty Ling demonstrated how to use a sextant to determine the elevation in degrees of a
known star, and then how to use that to determine boat position. (+)
12. When she used the school telescope, Debra was able to discriminate 3 moons of Jupiter
and to see 2 different shades in the rings of Saturn. (-)
From Pritchard, 1994, p. 10.
This figure shows the teacher has made sure that positive exemplars reflect all
four critical attributes of the measurement concepts. The negative exemplars contain
some numerical information and this can be a red herring for students in that
numerical information is also a critical attribute of the positive exemplars of
measurement. Close comparison of the two kinds o f exemplars, however, reveals that
the negative ones do not describe a measuring instrument to record physical
phenomenon in standard units.
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Finally, Figure 3 shows that the negative exemplars are not scenarios of
people measuring incorrectly. Rather, non-examples show people engaged in
something other than measurements—in rating subjective experiences and in
counting. The point here is that in the Concept Attainment Model, exemplars teach a
concept by having students compare scenarios that depict valid, concept-reflecting
activity (positive exemplars) with scenarios that reflect valid activity that is, however,
not reflective of the concept of interest. Negative exemplars are not incorrect
examples (Pritchard, 1994).
Guidelines Concept Attainment Instruction
In conclusion, Figure 3 reveals that planning a concept attainment instruction
sequence takes time. It involves teachers in thinking holistically through the subject
matter they teach, in doing research that may be necessary to develop clear and
specified descriptions of the concepts they intend their students learn, and crafting
creative and life-based positive and negative exemplars of these concepts. To
accomplish these tasks, it is helpful for teachers to have basic guidelines (Lemlech,
1998; Joyce & Weil, 1996; Pritchard, 1994; Johnson et. al., 1992)
Application of Concept Attainment Model to Technologies of Instruction
As stated earlier, the rudiments of Concept Attainment Model (CAM) of
teaching is utilized to teach students how to learn concepts by determining the
essential difference between positive and negative exemplars through an inductive
thinking process (e.g., pattern recognition and categorizing).
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Research in integrating the concept attainment model (CAM) with the
technologies of instruction in computer-based education is minimal. However, one
research study integrating CAM with computer-based education was published in
1995. Nelson and Pan (1995) constructed an instructional program that explored the
responses and perceptions of preservice elementary teachers school teachers while
finding and using characteristics to construct categories or concept. HyperCard and
videodisk images were used to develop a program so that students could organize and
explore concepts based on the concept attainment model and integrate ideas about
teaching thinking skills using computers.
Nelson & Pan (1995) used the ideas from Tennyson & Cocchiarella (1986)
and Winn (1982) to construct an instructional program to create categories or
concepts. They used the variables best examples and expository examples to show the
image o f an object that both is typical of the concept and familiar to the students.
Likewise, they used line drawings from video clips of an IVD to show best examples
and expository examples of insects They found that the use o f line drawings influence
the type o f information the participants recorded. The participants thought that the
integration of the concept attainment model with an interactive videodisk was
appropriate for the elementary school curriculum instruction.
Several studies have used microcomputer-based laboratory instruction in an
effort to improve students’ graph interpretation skills. They used sensors, to measure
force, current, voltage, light intensity, temperature, sound and pressure to study the
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the effects of MBL instruction in contexts other than kinematics (Adams &
Shrum 1990; Linn, et al., 1987). However, no one used any conceptual model of
instruction, but instead a model similar to the discovery approach to instruction using
MBL technology.
Svec (1995) studied university students enrolled in two physics courses.
One class used MBL equipment with motion sensors, while the other used more
traditional motion laboratories. Two instruments, the Motion Concept Test and
the Graphing Interpretation Skills Test, were administered at the beginning and
end of the semester. The study showed that the (MBL treatment group) students
learned more about graphing interpretation skill, more about motion graphs and more
about conceptual understanding of motion than did the traditionally instructed control
group students. He concluded that learning was made possible by the effective use of
MBL activities.
Beichner (1990) developed a system, called a VideoGraph, which produces
the graphs of the motion shown in the videotape. Beichner’s study used a two-by-two
design. One factor was the type of instruction, VideoGraph or traditional
methodology. The other was whether the students viewed an actual motion event or
not. Beichner found no significant differences among the groups in his study but used
a longer intervention lasting one period. He concluded that the ability to make
changes—and then instantly see the effect— is vital to the efficacy of microcomputer-
based kinematics labs. The feedback appeals to the visual and kinesthetic senses.
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Summary
The Concept Attainment Model, integrated with technologies of instruction,
makes it possible to teach students significant content in a subject area and teach them
specific thinking skills o f observing, analyzing, hypothesizing and hypothesis tests,
and engaging them in metacognition. Students test the concept deductively and using
the general hypothesis of the concept to determine which exemplars do and do not
“belong” to it and use it to derive the concept o f interest (Pritchard, 1994; Pandey,
1993).
The use of a three-phase instructional plan in the Concept Attainment Model
presents teachers with very real challenges. Teachers select significant and
appropriate concepts; develop positive and negative exemplars for them; guide
students through careful observation, analysis, and work with hypothesis; and create
challenging lab activities or case-study guides to apply the concepts learned. This
results to a comprehensive, systematic planning, thoughtful and focused teaching
(Lemlech, 1998; Pritchard, 1994; Platten, 1991).
On the other hand, the influence of cognitive science and constructivist
framework in the technology of instruction shares the analogy of comparing the
processes o f the mind to that of a computer. Perkins (1991) highlights the information
processing models have spawned the computer, a model o f the mind, as an
information processor. Constructivism has added that this information processor must
be seen during learning as in making hypothesis and testing tentative interpretations.
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Jonassen (1992) states that most designers and theorists of instructional
technology assume positions that fall somewhere between constructivism and
objectivism. For example, programmed instruction (PI) and instructional design (ID)
possess more objectivist assumptions while Piagetian and discovery learning tasks
tend to be more constructivistic.
Moreover, Hunt and Ellis (1999) contend that the computer is used as an
analogy for cognitive activity as in cognitive neuroscience exploring functional brain
imaging techniques (e.g., positron tomography, PET) to relate cognitive phenomena.
Duffy & Cunningham (1996) suggest that any learning situation may be approached
from either constructivism and objectivism perspective.
Finally, cognitive science, constructivist framework, and concept attainment
model influence the technologies of instruction . They reflect the diversity of the
theoretical foundations in the design and development of instructional technology
systems.
Teaching and Learning Science with Technology
Teaching and learning science with computer technology has a history of
only four decades. Since about 1960 digital computers have been used to support
teaching and learning in several ways. Computers have been used as a school
subject (e.g., instructional computing), as a means to enhance learning (e.g., CAIs),
as a support tool (e.g., wordprocessing), as a vehicle fo r multimedia delivery
(authoring tools), and finally as a communication link (e.g., web-based education).
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However, computer technology has not impacted instruction to a great
degree. Until recently, the computer had been used mainly as an aid to
facilitate productivity and to support or enhance normal teaching activities. There
have been very few examples where computers have really changed how and what
educators teach. The promises of the past have not yet been realized, but progress has
been made in the instructional design and instructional development of new digital
tools and other forms of instructional technology (e.g., MBL, wireless
wearable-computer, etc.) to support learning and teaching (King & Behnke, 1999;
Gillespie, 1998; Winn, 1997; Wepner, 1991).
A Taxonomy of Instructional Technology
Instructional technology (IT) is defined as a type of computer-based
education (CBE) in the forms of MBL, CAI, IVD, simulation, telecommunication such
as web-baseddistance learning (WVDL), expert tutoring systems and others.
Instructional technology is a field whose basic purpose is to promote the application
o f validated, practical procedures in the design and delivery of instruction (Heinich,
Molenda, & Russell, 1991). It is often defined either in terms o f media and other
technology used (e.g., audiovisual media and equipment, computers, etc.), or in
terms of systematic process which encompasses instructional design, development,
and evaluation (Berger & Kam, 1996).
According to Berger & Kam (1996), instructional design is the systematic
development of instructional specifications using learning and instructional theory
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to ensure the quality instruction and may have nothing to do with computers. It is
the entire process o f analysis of learning needs and goals and the development o f a
delivery system to meet those needs. It includes development of instructional
materials and activities and tryout and evaluation o f all instruction and learner
activities. Instructional development is the process o f implementing the design
plan. Thus, instructional technology is equivalent to instructional design plus
instructional development. Moreover, instructional technology is the systemic and
systematic application of strategies and techniques from behavioral, cognitive,
social, and constructivist theories to the solution o f instructional problem
(Mayadas, 2000).
Using the mechanics o f instructional design and development coupled with
the application o f the theories and models of learning (behavioral, cognitive,
constructivism, social), Bruce & Levin (1997) devised a new way of classifying
the uses o f educational technologies. Bruce and Levin based their taxonomy on a
four-part division suggested years ago by John Dewey: inquiry, expression,
construction, communication. They have tested the utility of this taxonomy by
using it to classify a set of advanced applications o f educational technologies. The
continued success in using the taxonomy paved the way to get the support of the
National Science Foundation (NSF) and use the taxonomy to point to new potential
uses of technologies to support learning (Bruce & Levin, 1997). The taxonomy is
shown m Table 1.
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TABLE 1. The Taxonomy of Technology as Media
CATEGORIES
SUB-CATEGORIES AND APPLICATIONS
A . M edia fo r In q u iry
1. Theory building— technology as media fo r
th in kin g
* Model exploration and simulation toolkits
* Visualization software & Virtual reality
environments
* Data modeling—defining categories, relatiof1 5 ’
representation
* Procedural & Mathematical Models
* Knowledge representation: semantic network
on-online tools; Knowledge integration
2. Data access-— connecting to texts, video, data ***•
* Hypertext and hypermedia environment
* Library access and ordering
3. Data C ollection—using technology to extend S*®***
* Remote scientific instrument
* Microcomputer-based laboratories
(sensors for temperature, motion, light etc-)
* Video and sound recording
4. Data Analysis
* Exploratory Data and Statistical analysis
* Environments for inquiry & image processing
* Spreadsheets & Programs to make tables/graP^s
* Problem-solving programs
B. M edia fo r Communication
1. Document preparation
* Word-processing, Outlining, Graphics
* Spelling, grammar, usage, and style aids
* Symbolic expression; Desktop publishing
2. Communication —w ith other students, teac>*rs’
experts in various fields, and people around
w orld
* Electronic mail, Asynchronous computer
conferencing (text, audio, video, etc.); WWW
3. C ollaborative M edia
* Collaborative data environment; Group Dec!8 * 0 1 1
Support System, Shared Document Prepare!*0 1 1
4. Teaching M edia
* Tutoring systems; Instructional simulations;
and practice systems: Telementoring -
C . M edia fo r C onstruction
* Control systems; Robotics; Control of Equip**1 ®1 * !
* Computer Aided Design; Construction of graphs/
charts
D. M edia fo r Expression
•Drawing and printing programs
* Musing making, accompaniment, composing
* Animation software; Multimedia software
* Interactive Video and hypermedia __ _ . _
from Bruce & Levin (1997), p. 29
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Table 1 indicates the taxonomy emphasizing the mediative aspect o f
technologies. That is, to view technology as media as it operates to a large extent,
through the ways as it alters the environment in thinking, communicating, and
acting in the world. Thus, technologies provide new media for learning. For
example, learning science entails learning how to use computers as media for
collecting and analyzing data (MBL), for modeling phenomena (microworlds, data
modeling), and for communicating results (Internet or WWW, web-based
instruction). For these activities, science students need experience with the
technological media scientists use; and they need to learn how to think through new
media. At the same time, there is a growing body of research showing that these
media uses are effective at supporting learning of concepts, attitudes, and process.
Thus, it is not mere coincidence that the categories of media for learning listed
within the taxonomy above reflect the uses o f computers by professionals in various
fields (Means, 1994; Hawisher, 1994; Barker & Kemps, 1990).
From the rationale of the above taxonomy of technology as media, questions
were asked by teachers such as: (i) What has been the impact of instructional
technology (e.g., CBE) on teaching and learning in physical science? and (ii) What
should be done to encourage and facilitate the wise use of CBE in science education?
To provide answers to the questions the work of researchers who have investigated
the effects o f computer-based education (e.g., MBL) on learning for the past three
decades will be unraveled to understand its historical perspective.
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Historical Development of Computer-Based Education
Studies investigating the effects of CBE on learning (usually with regard to
student achievement) have been reviewed periodically since the 1970s. These studies
typically include computer-based education reviews on the effects of computer-
assisted instruction (CAI), generally referring to drill or practice and tutorials;
computer-managed instruction (CMI), generally referring to computer evaluation of
student test performance, guiding students to appropriate instructional resources,
and record keeping; interactive videodisc (IVD); computer-simulated
experimentation (CSE); microworlds, referring to a simulation o f micro-world
environment; expert tutoring (artificial intelligence), referring to a third type of CAI;
and more recently microcomputer-based laboratory (MBL), generally referring to an
interface (or Universal Serial Box port) between a computer and a data-collecting
device (Berger, Lu, Beizer, & Voss, 1994).
The first comprehensive review
The first comprehensive review on CBE was conducted by Roblyer, Castine,
and King (1988) using 26 past reviews from 1972. They added their own meta
analysis on the effects of microcomputer-based education on learning from 1980
to 1987. Their reviews examined studies that were descriptive type reviews that
used meta-analysis techniques and reported its effect sizes (ES). The descriptive
type reviews were called “box scores” and generally provided a narrative review
o f studies, counting and computing the number of studies with positive effects
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versus negative effects versus no effects. Box score type reviews provided qualitative
insights into the impact of a given type of treatment. On the other hand, effect sizes
(ES) are quantitative measures o f the impact which a new method would have over an
old one. Effect sizes are calculated by subtracting the mean score achieved by the
control group from the mean score achieved by the treatment
group. The result is divided by the measure of the standard deviation of the control
group or the pooled standard deviation (Gall, Borg, & Gall, 1996; Glass, 1976;
Pedhazur & Schmelkin, 1991). To measure effect sizes appropriately, Cohen
(1977) has suggested that the magnitude of ES should be as follows according to
this table. For ES of 0.2 or less - small effect, ES of 0.5 to 0.6 = medium effect, and
ES of 0.80 or more = large effect.
Table 2 shows the first summary of past reviews compiled on the effects
of CBE on learning from 1980 to 1988. Information from the table reveals
that only four reviews have been reported specifically from the science domain by
many researchers. However, Aiello and Wolfe (1980) conducted a meta-analysis
on individualized instructions in science including audio-tutorial instruction, CAI,
programmed instructions, personalized system of instructions, programmed
instructions, and on a combination category that did not fit into any of the first four
categories. Their effect sizes were ES of 0.36 in specific subject areas (N=l), ES of
0.52 in chemistry (N=8) and ES o f 0.23 for physics (N=4). In contrast, Willet et al.,
(1983) meta-analyzed the effects of instructional systems in science teaching
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including audio-tutorial, computer linked, contracts for learning, media-based
instruction, departmentalized elementary school, team teaching, and individualized
instruction. They found out that CBE produced an overall ES of 0.13 (N=14); CATs
effect size was only 0.01 (N=5); CMI’s average ES of 0.05 (N=8); CSE average ES of
1.45 (N=l), heavily biasing the overall effect size in the positive direction.
Personalized system o f instruction (PSI) was the most effective instructional system,
with an average ES of 0.60 (N=13), which can be considered as a medium effect
(Cohen, 1977).
TABLE 2. Summary of Past Reviews on the Effects of CBE from 1980-1988
Reviewer/s Year Grade Level Number o f
Studies
Type o f CBE Average
E ffect Sizes
Aiello & Wolfe 1980
Secondary &
College Science
1 1 CAI 0.42
Willet et al., 1983 Elementary
& Secondary
science
14 CAI.CMI,CSE 0.13
Wise & Okey 1983 Elementary &
secondary
math & science
12 0.82
Kulik et al. 1983 Secondary
science
1 1 0.31
Kulik et al 1984 Elementary
science
1 CMI 0.36
Kulik & Kulik 1986 College (hard
sciences**
44 CAI,CMI,CEI 0.15
Roblyer et al. 1988 Secondary &
college science
4(3) CSE 0.40
(0.64)
Wise
1988 Elementary
secondary
26 mbl. CAI. CMI. CSE,
VDBL***
0.34
* Hard sciences = tbe bard sciences plus engineering, mathematics, and agriculture
** Computer-enhanced instruction; computer serving as a tool (word processing, graphing data in science classes, etc)
and simulation device.
*** Number in parentheses are for analysis with outliers removed.
**** Microcomputer-based laboratories, tutorials, diagnostic testing, simulations, and video desk-based lessons
Source: Berger e t al., (1994), In D. Gabel (Ed.), “Research on the uses o f technology in science education”
New York, NY: M acmillan Publishing
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Assessment of the First Comprehensive Review
The results of the first comprehensive review related to CBE by Roblyer et
al., (1988) using box-score type review and meta-analysis techniques reported the
following trends across the twenty sue earlier reviews. First, they found that most
reviews reported that CBE provided benefits over other instructional methods and
reported an average time saving of 32 percent in higher subjects. Second, they
established that students’ attitudes were positively affected by CBE with an effect
size of 0.62 in attitude towards computers and an effect size of 0.22 was reported
towards school subjects and school learning. Third, they predicated that better
results were reported in tutorials than in drill-and-practice in areas of secondary level
mathematics, reading and language. Fourth, they established that supplemental use
of CBE was more effective than total replacement o f the classroom teacher. Fifth,
they spotted that there were some signs that CBE may be especially effective for use
with lower ability learners. Roblyer et al., (1988) cautioned, however, that
effectiveness may have been due to the specific software and not CBE in general.
Sixth, they predicated that computer-assisted instruction (CAI) was more effective
at lower grade levels and computer-managed instruction (CMI) and computer-
simulation experimentation (CSE) were more effective at higher grade levels.
Moreover, many researchers like Wise and Okey (1983), Kulik et al., (1983,
1985), and Wise (1988) have conducted a science-domain-specific meta-analysis.
In the study of Wise (1988), he reported an overall average effect size of ES =34
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(N=51) for CBE and found microcomputer-based laboratory (MBL) to have
produced the highest average effect size of 0.76 (N=6). MBL was followed by video
disk-based lessons that produced an average effect size of ES=0.40 (N=l 1).
Finally, Wise noted, that computer-based tutorials were one form of CBE that
have consistently high positive average effects in reviews. However, based from the
findings of CBE, meta-analysis provided an overall average measure
of how computers affect learning in science, but they cannot provide some of the
specific information indicating how certain programs like MBL have affected the
learning process and the specific content in which CBE programs were successful.
To provide information on the effect of a microcomputer-based laboratory,
this study is designed to highlight a case study which will investigate the impact on
the use of microcomputer-based laboratory on concept attainment among students of
varying levels of differentiated achievement. Subsequently, the impact on the use of
MBL on students’ attitudes towards computer technologies will be investigated.
The Microcomputer-Based Laboratory in Science Instruction
The “crisis” of ineffectiveness in US science education is recognized as
a major concern, and technological innovation is heralded as at least a partial
solution. Prescriptive plans to integrate technology into the science curriculum
have mentioned possible improvements as due to the fact that a) scientists are using
these tools and students might also be helped by them, b) technology has already
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domiciled in many US schools, c) technology has transformed the workplace and
students will require more extensive learning skills and technological skills, and
d) the experience of scientists using technology to solve complex problems can be
used to instruct technological problem-solving skills to students (Linn, 1988;
Lieberman & Linn, 1991; Maclsaac, 1992).
Linn (1988) and Thornton (1993) go on to suggest that the implementation
of technology for instructional purposes moves through three major stages of
acceptance which are technology in the service o f established goal, adapting science
education to technological innovation, and, finally, the integration of technology and
learning. This would suggest that MBL adoption (Amend & Furstenau, 1992; Nakhleh
& Krajcik, 1992; Linn 1988) will catalyze significant changes in science curricula by
making apparent present procedural shortcomings
in instructional delivery, causing change in curriculum content to surmount these
limitations and finally supporting reforms in the curricular paradigms of science
pedagogy. Such reforms are already apparent in the constructivist movement in
science pedagogy, which embraces many of the characteristics of free investigation
and student empowerment ascribed to technological innovation (Linn, 1988;
Beichner, 1995; Heck, 1990; Thornton & SokolofT 1993).
MBL instruction: An overview
Microcomputer-based laboratory (MBL) is an environment which uses a data-
gathering device, an analog to digital converter for older models or a USB link for
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newer models, with a microcomputer. It includes the use of different sensors or
probes such as temperature probes, pulse-rate probes, sound detectors, light-sensing
probes, magnetic probes, biofeedback probes, humidity sensors, or pH probes,
connected to a computer and serving as measuring devices to collect experimental
data in the laboratory. The collection and organization of data will develop new
understanding of scientific tools (Tinker, 1985a; Krajcik, 1992) and conceptualize
the relative correlation of physical quantities (Linn, Layman, & Nachmias, 1987;
Weller, 1996).
General Layout of MBL
Figure 4 shows the general layout of a typical MBL system that includes a)
Computer (Graphic Representation), b) Interface Box (Transformation) (Note:
newer versions uses USB link for direct connection), c) Sensor or probes
(Experimental Environment), and d) Printer (Graphic Station). Graphic
representation shows how the program/software displays the resulting graph from
the data collected via the sensor. Likewise, the software loads the pre-configured
experiment files and performs advanced analysis of the result. A real-time graph is
drawn on the monitor screen as data are collected. Transformation occurs at the
Interface Box (analog/digital converter); or directly via a USB link), when data are
sent to the computer in real-time for analysis. At the Experimental Environment,
the probes or sensors read the data from the experiment being performed. There are
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about 40 sensors (Pasco, 1999; Vernier, 1999) that measure motion, temperature,
voltage, pH, dissolved oxygen, light, gaseous carbon dioxide, and other variables. The
PAS PORT Science Lab I is a new version of MBL that includes three sensors, one
Universal Serial Box (USB) link, software, and Quick Start Cards (Pasco, 2002).
Lastly, at the Graphic Station, the resulting graphs, tables, and reports of the
experiments are printed on paper for analysis and display in two or three
dimensional images.
FIGURE 4. The General Layout o f an MBL System
Graphic Station Microcomputer Interface B ox Probes
(To produce graphs) (Monitor & Software) (A/D Converter (pH. motion,etc)
or USB Link)
Characteristics of Using MBL Tools
One unexpected characteristic o f MBL activities is a simplified
experimental environment, due to fewer repetitive chores such as data collection
and graphing. More of the student’s concentration can be spent on the
phenomena and relationship, reducing cognitive overhead. Cognitive overhead
refers to the constraints of human short term memory (Gagne & Glaser, 1987;
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Maclsaac, 1992). Table 3 presents a summary of the characteristics of using
microcomputer-based laboratory tools in the science classroom.
TABLE 3. Characteristics of MBL Tools
1. Actively engages students in constructing science concepts.
2. Enhance and complements science teaching instructions.
3. Produces real-time graphs. Allows for exploration not normally performed in
science classroom.
4. Allows for collection, graphing, interpretation, and analysis of data.
5. Allows students to use more powerful and sophisticated techniques that are
more realistically reflect methods used in research and analytical laboratories.
6. Provides opportunities to predict, question, and apply science concepts.
7. Enhances hands-on interactive learning.
8. Makes more efficient use of limited class time.
FromKrajcik, 1999, p. 2
The MBL learning environment
One of the most powerful uses of MBL in science teaching is to have
students use it as a laboratory tool to collect and analyze data. Microcomputers
used as a laboratory tool may offer a fundamentally new way of aiding students to
construct or create science concepts (Linn, Songer, Lewis & Stem, 1991; Mokros &
Tinker, 1987). They also allow students to experience what it is like to do scientific
research (Tinker & Papert, 1989).
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Due to the interactive nature of MBL and because MBL links the concrete
experience o f data gathering with a symbolic representation of real-time, many
science educators support the use of MBL to enhance the learning of science
concepts, science process skills, graphing skills, and problem solving abilities for a
broad range of science students in the classroom ( Linn et al., 1991; Nahkleh &
Krajcik, 1991).
Technology-Enhanced Science Laboratory
A student’s success in learning is associated with the environment in which the
learning occurs. A technology-enhanced science laboratory (TESL) is a learning
environment that provides cutting-edge instructional technology instructions to
students and teachers. These cutting- edge instructional technology instructions
include interactive videodisk technology (IVD), telecommunication, computer
simulations; authoring tools; microworlds; expert tutoring system (artificial
intelligence), calculator based-laboratory (CBL), calculator based- ranging (CBR),
and microcomputer based-laboratory (MBL).
In the technology-enhanced science laboratory where lessons are much less
formal than nonlaboratory lessons, students are allowed to talk and move about the
room. At this exploratory phase, they are free to do what they see fit, and they often
have the chances to interact individually or in small groups or have cooperative
learning with the teacher and their peers. Hence, TESL as a learning environment
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becomes the nucleus of science that can be used to make sense of new experiences
(Appleton, 1997).
TESL activities can be seen as a means of allowing learners to pursue
learning autonomously. TESL activities or exercises affirm a fertile ground in terms
of learners being able to solve problems and construct knowledge that will
enchance metaconceptual and metacognitive change conditions (Thorley, 1990).
Thorley defines metaconceptual as students’ comments explicitly referring to the
content of a conception while metacognitive for comments referring to, for
example, the status of a conception. That is, negotiating the status of a conception
needs the inclusion of planned instruction by the teacher in which students speak
about their conceptions (Beeth, 1998).
Speaking of scientific conceptions, the technology-enhanced science lab is a
place in which science students have experiences that interact with their existing
conceptions and at the same time develop new concepts. Hence, it can be used as a
means o f identifying students’ preconceptions, as well as a vehicle for extending or
modifying the conceptions. The effectiveness of the TESL in fulfilling both task—
diagnosing and affecting conceptual change—depends on how it is used. This
includes the nature of the exercises and investigations, the way students interact
with the teacher and each other, and the role played by pre- and postlab discussion
(Friedler & Tamir,1990; Hegarty-HazeL, 1990).
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Accordingly, Larowitz and Tamir (1994) elaborated that the technology-
enhanced science laboratory may help in identifying student beliefs and
misconceptions in two ways: observing, listening, and questioning students during
their work reveals their thinking and conducting structured or unstructured interviews
based on their observation and manipulation of objects and apparatus. Furthermore,
reflecting on the history of research related to the use of the technology-enhanced
science laboratory as a learning environment, Larowitz & Tamir (1994) noted four
goals that were congruent to constructivist ideas that emerged from the review of
literature. Technology enhanced-science laboratories should provide: (1) concrete
experiences and ways to help students confront their misconceptions; (2 opportunities
for data manipulation through the use o f microcomputers; (3) opportunities for
developing skills in logical thinking and organization, especially with respect to
science, technology, and society (STS) issues; (4) opportunities for building values,
especially as they relate to the nature of science
Based on the rationales o f the above four goals, it is justified to link the
instructional potential of the technology-enhanced science laboratory (TESL)
to a constructivist learning environment (CLE) (Hassard, 1994; Ernest, 1995;
Fosnot (1996). The basis of this linkage can be explained using the following
attributes. First, the TESL is a place in which science students have experiences that
interact with their existing conceptions and at the same time develop new concepts.
Hence, the TESL can be used as a means o f identifying preconceptions as well as
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modifying the conceptions (Friedler & Tamir, 1990; Hegarty-Hazel, 1990a).
Second, the attributes of the computer to store information, process information,
display information, and carry on an interactive dialogue with the user (Walker,
1983; Joyce & Weil, 1996) will make learning with microcomputers active and
interactive. Third, the science, technology, society (STS) movement that gained
momentum in the 1980s seems to continue until the present time to promote
scientific literacy (AAAS, 1989; California State Department, 1990). Fourth,
writing laboratory reports and doing projects will make significant contribution
to the development of personal knowledge and the enhancement of positive
scientific attitudes (Bliss, 1990; Tamir, 1989b; Delcourt & Kinzie, 1993).
Finally, supporting a constructivist teaching, researchers (Tamir & Lunetta,
1978; Perkins, 1991; von Glaserfeld, 1996) convey that TESL has promoted
student inquiry and allowed them to understand investigations. They contend that
TESL exercises can be effective in promoting intellectual development, inquiry, and
problem solving skills. Also, they claim that TESL work had been justified in
science education primarily because of its ability to promote observational and
inductive reasoning skills. That is, direct contact with the physical world is a unique
characteristic of study of science.
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Students and Teachers in MBL Environments
Students and teachers interact while doing MBL activities in the science
classroom and laboratory. Students like computers, and teachers like results. The
role of students and teachers is an important factor in the successful use and
implementation of MBL tools and activities in a technology-enhanced science
laboratory (TESL).
School age children are usually very comfortable using computers for almost
any purpose including education. According to Bennett and Brennan (1996), the
majority of students (80%) rated a computer-based physics course from good to
outstanding. Bennett and Brennan show that students of many different learning levels
and styles are supported by computer based approaches to learning physics. Since the
price of technology has dropped so markedly, a good market for used computers has
developed. Hence, people of many socio-economic levels have been able to afford
computer technology.
MBL effects upon student learning and conceptual development in
undergraduate physics have been studied by researchers like Beichner (1990),
Thornton & Sokoloff (1990), Heck (1990), Leiberman & Linn (1991), to name a
few, and they claim curricular activities and expectations play a preeminent role in
student science laboratories where MBL technology is used (Leiberman & Linn,
1991). Beichner (1995), says that 84% of physics teachers surveyed suggest that
MBL removed tedium from lab activities and 92% say that students found MBL
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activities engaging. This sounds very encouraging, but the novelty effect of
technology in the classroom should be taken into consideration.
On the other hand, The teacher plays a pivotal role in creating an atmosphere
that allows students to investigate. The overall effectiveness of MBL will depend
upon the teachers’ school of thoughts of how to use the new technology, their
personal knowledge o f the concepts involved, and their knowledge of how to help
students link their experiences with the concepts (Krajcik, Layman, Star &
Magnusson, 1991). For instance, MBL provide opportunities for students to collect
real-time data, and ask “what if’ questions and use electronic sensors to test their
predictions and view the results of these experiments in various forms like graphs
and charts (Thornton & Sokoloff 1990).
Microcomputer-based laboratory: What research says
The development of MBL was a by-product from computer-based education
research to improve students’ graphing skills and enhance their mastery of science
concepts (Weiss, 1987; Shrum & Adams, 1990). Likewise, the use of MBL has
produced large average positive effect sizes (Wise, 1988). The development of
graphing skills (e.g., graph construction and graph interpretation) is an essential
component in understanding the relationship of fundamental and derived physical
quantities. The National Assessment of Educational Progress (NAEP) reported that
students’ inadequate graphing skills do not allow them to perform well on graphical
items in mathematics and science (NAEP, 1999, 2000). Hence, the recent shift to
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use more CBEs has caused science educators like Mokros, Tinker and other science
educators to research and develop MBL.
First Maior Research Programs
There were two major research programs that led to the development of MBL
over the past two decades. The first research program was in 1982 at the
Technological Education Research Center (TERC) in Cambridge, Massachusetts.
TERC’s scientists actively designed the research program and tested the hardware
and software needed to create MBL equipment (Mokros & Tinker, 1987; Tinker,
1985a). The research program was proliferated by a group of motivated science
educators including Tinker, Mokros, Thornton, SokolofF, and Brassell who began
working on a five-year project to develop MBL curriculum materials. On three
separate but related studies, the group investigated sixth, seventh, and eight grade
students’ knowledge of graphing skills and their application of that knowledge to
interpretation of motion graphs.
Based on their research, Mokros and Tinker (1987) of TERC, disclosed
that the generation and analysis of real data by the students provided by MBL
caused visualization of science concepts that could not be achieved in any other form.
They articulated further that the ability to reproduce a displacement-time graph
while moving in front of a motion sensor was a profound motivation among
students, whether they are in first grade or at the university.
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Likewise, the research component of the TERC project was about graphing
misconceptions and how MBL can help students improve their graphing skills. The
project planners found out that a typical misconception made by the students was
confusing the graph of an event with a picture of the event. Results from
research suggested that MBL instructional approaches do help in improving
graphing skills. Alternately, Barclay (1986) added that the inherent attributes of
MBL science laboratories include the grounding of the graphical representation
in the concrete action of the student, the inclusion of different ways of
experiencing the materials (visual, kinesthetic, and analytic), and the fast
feedback that allows students to relate the graph to the event immediately.
Second Maior Research Program
Alternatively, the second research program, the Computer as Lab Partner,
evolved from a series o f studies with middle school students using MBL heat and
temperature units (Friendler, Nachmias & Linn, 1990). The studies involved whole
classes and eventually tested curriculum units for MBL. Their research indicated
that curricular activities and expectations play a pre-eminent role in the science
laboratories where MBL is used that and it can achieve profound conceptual changes
among students.
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Additional Research Programs
Additional developments o f MBL include the creation of a number of
interface probes (USB links for newer models) that include pulse-rate monitors,
skin-resistance probes, magnetic field probes sound detectors, motion sensors,
temperature probes, and light sensing probes. Two promising MBL developers are
Vernier Caliper of Portland, Oregon and PASCO Scientific Instruments of
Roseville, California. Both companies adapted their software to Apple, IBM, and
Macintosh computers with accompanying manuals for students and teachers
(Vernier, 1999; PASCO, 1999).
Most MBL programs are user-friendly and permit students to watch real
time graphs being formed as the experiment progresses. Graphics are saved and
recalled for later analysis and discussion. Students can change the scales of the graph
before data are collected and enter the text describing the experiment being
conducted. The resulting printout contains a caption of the activity, as well as the
graph. The students pick various time scales ranging from seconds to hours. For
example, students use long time periods by placing the probes outdoors to record
weather related changes (Vernier, 1999).
Shram and Adam (1990) studied the effects of MBL and conventional
laboratory graphing exercises on acquisition of graph construction and graph
interpretation skills (N=20). They found that conventional laboratory graphing
exercises led to high success on graph construction ( p = 0.05) while MBL appears
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to lead to higher achievement on graph interpretation (not statistically significant with
the small group). The researchers recommend that conventional graphing exercises be
coupled with MBL exercises.
Beichner (1990) studied the effect of VideoGraph software (simulating an
MBL exercise) compared with traditional laboratory. The subjects consisted of 165
high school physics students and 72 college physics students randomly assigned to
four different treatments. The treatments consisted of (1) a traditional laboratory
with students viewing an object thrown from one end o f the room to another
forming an arc; (2) a traditional laboratory with students examining and making
graphs from stroboscopic photographic representations of the projectile motion; (3)
students working with VideoGraph animation of the projectile motion when
students were able to start, stop, and relay the motion; and (4) students working with
static computer representations o f projectile motions paralleling the content of the
static stroboscopic laboratory experiment. Beichner did not find any significant
achievement differences across the four treatment groups. The gain from pretest to
posttest were very small, on the order o f+1 point out o f 24 total points. Beichner
believes that these results indicate that a real motion event must be experienced by
learners coupled with simultaneous graphing associated with MBL experiments.
Nakhleh and Krajcik (1992) investigated how different levels of information
presented by different technologies affected secondary students’ understanding of
acid, base, and pH concepts. They found that students using MBL had a large
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positive shift in their concept map scores, and provided evidence that MBL affected
their understanding of chemical concepts (e.g., acid, base, and pH).
Thornton (1993) and his associates studied MBL effects upon student
learning and conceptual development in undergraduate physics at Tufts University
and Dickinson College. They developed quantitative instruments designed to
measure changes in the physics-related graph interpretation skills and kinematics
conceptual understanding of undergraduate students. Their large scale testing at
various sites indicated that their own laboratory curricula incorporating MBL and
the instructional strategies proposed by Arons and McDermott were considerably
more effective in teaching basic kinematics (mechanics) concepts than standard
lectures. Also, Maclsaac (1993) designed and implemented MBL instrumentation
in high school curricula in British Columbia and found positive effects on
student perceptions of a computer-based introductory mechanics laboratory
curriculum.
Han (1994) formulated a research agenda using MBL and reported research-
based outcomes of using MBL for teaching and learning science. The list of
documented outcomes formulated by Han (1994) are:
1. Effect on real-time graph: After an MBL experiment is carried out, a real-time
graph is formed. Students view the graph or graphs as a representation of a
dynamic relationship of physical quantities rather than a static picture on the
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computer screen. Thus, MBL links the concrete experience with more
abstract and symbolic representations of physical quantities.
2. Effect on graphing skills: MBL helped students improve their graphing skills
and learn to construct and interpret the resulting graphs. The students
distinguished the dependent horn the independent variable.
3. Impact on students’ science conceptions: MBL activities stimulate higher
energy discussions among students about their understanding of the world
through personal experience. The computer plays a vital role in motivating
learners and keeps them on task.
4. Impact on students’ problem solving: Higher level thinking skills are
encouraged and developed by the use of MBL. Computers provide speed in
analysis o f laboratory data. Through MBL, students learn and use the scientific
method in selecting a hypothesis, conducting observation, collecting data, and
testing or rejecting hypothesis.
5. Impact on student’s prior knowledge: Prior knowledge is quickly and easily
challenged and possibly corrected if graphs are produced and displayed
immediately for students’ thinking and learning.
6. Effects of computer as a tool: MBL environments allow for more control and
collaboration. As a tool, it increases students’ success and is accompanied by a
change in their attitude toward science. Likewise, the computer as a tool
increases student and teacher productivity.
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7. Effect as a Learning Environment: The use of MBL makes it possible to create
learning environments in which students engage in activities that they find
interesting and exciting for personal reasons. As a result, less lecturing from the
teacher enhances classroom management and simplifies the methods of evaluating
students.
8. Impact on student-teacher relationship: The teacher becomes a facilitator and
coordinates problematic lessons for students to study, analyze, and solve.
9. Impact on traditionally underrepresented groups: MBL reduces the science
anxiety and negative experiences of underrepresented ethnic or socioeconomic
groups by increasing the participation of students in exploring the MBL sensors
and watching real-time graphs.
10. Impact of levels o f student ability and experience: MBL probeware helps
disadvantaged and learning handicapped (LH) students experience
experiment-based activities. Computer probes make it easy for students to
gather data without the stumbling blocks of language, reading, tedious
calculation, and graphing.
Trumper (1997) investigated the effect of V-Scope activities on the
performance of 11th grade students in analyzing kinematics graphs. Students were
challenged to construct different kinds of graphs using their own movements as
well as the motion of a dynamics cart. They found that the V-Scope kinematics
laboratory activities can promote kinematics concepts and graphing skills.
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Likewise, Redish, Saul, & Steinberg (1997) of the Department o f Physics,
University of Maryland, used one-hour active-engagement tutorials using MBL
equipment and substituted it for traditional problem-solving recitations in
introductory calculus-based mechanics classes. They found that a comparison
of the results of eleven lecture classes taught by six different teachers with and
without tutorials showed that MBL tutorials resulted in a significant improvement
compared to the traditional recitations when measured by carefully designed
multiple choice problems. However, the fiee-response question showed that
although the tutorials students did somewhat better in recognizing and applying
concepts, there was still room for improvement.
Summary of MBL research in science instruction
Research has shown that MBL provides opportunities for the following
reasons: students like using computers, designing plans and or experiments, efficient
collection and analysis of data, and MBL provides real world experiences and
supports many learning styles. Similarly, Leonard (1992) summarizes MBL benefits
that include, reducing costs, improving effectiveness, saving student time (and thus
preventing boredom), learning to use state-of- the-art-scientific instrumention,
simplifying data analysis, making instrumental results more meaningful by allowing
students to perceive relationships between independent and dependent variables as the
experiment is completed, allowing students more effectively comprehend abstract
concepts, and providing an opportunity for developing problem solving skills.
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Research in MBL technology is an ongoing educational undertaking. While
positive results were mostly evident in the undergraduate science-math instruction,
there were fewer studies cited in the elementary, middle and high school science
by prominent MBL science educators and researchers. In the K-12 level, there
are gray areas that need further assessment, development, and research. Some
of these gray areas are the development o f integrated science process skills,
content knowledge, and learning how to use technology for instruction (Horizon
Research, 2001). Data from the 2000 National Survey of Science and Mathematics
Education (Horizon Research, 2001) indicate that elementary teachers feel less
well qualified than their middle school and high school counterparts to teach both
science and mathematics. Also, professional development activities in science and
mathematics and deepening content knowledge in science and math are of great
concern. Moreover, the lack of funds for science equipment and supplies is a serious
problem. Notably, these problem areas call for a timely and ongoing investment in
instructional technology (e.g., MBL) to enhance technical and professional
development among K-12 teachers. Only then, will factors affecting student attitudes
toward active learning, students’ math-science skills, and changes in teachers’ role as
facilitator or organizer in adopting technology in a technology-enhanced science
laboratory hasten the adoption and implementation of science reforms in US
schools in the new millennium.
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Current Educational Trends in Intructional Technology
Since the 1980s, novel and intriguing technology projects in CBEs have
been marketed, but due to financial constraints in many institutions of
learning they have not reached many classrooms. In this subsection, an array of
instructional technology projects are described such as telecommunication
(synchronous/asynchronous technology, web-based distance learning and
instruction; information superhighway), authoring tools (multimedia/hypermedia),
microworld (simulations), and expert tutoring system (artificial intelligence).
Telecommunication
Telecommunication is a form of CBE used to globalize the classroom. By
means of on-line communication via computer networks, classrooms can exchange
data or information about social problems in their respective regions. On-line
communications via computer networks are also called Educational
telecommunications. Educational telecommunications is a broad, catchall term for
any form of education which relies on telecommunications for delivery. Synchronous
or Asynchronous Technology, Telecourses, Teleconferencing, Computer-mediated
communication (CMC), Internet, and any use of the Web, all fit under this category.
Synchronous and Asynchronous Technology
The level of interaction in educational telecommunication is affected by the
choice o f synchronous or asynchronous delivery. Synchronous is the transfer of
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information without delay like the traditional stand up teacher. Distance education
examples would be audio and or video transmitted “live” among instructors and
students via TV, Internet or radio (Hoffinan, 2001).
A example of synchronous technology is Distance Education (Distance
Learning). Distance education is becoming well known among people who cannot
come in person to attend classes in a university. The university uses an organized
instructional program using telecommunication to offer college courses to learners
who are at home. An example of Distance Education projects include the The Star
Schools program funded by the U.S. Department of Education which provides
instruction via satellite in German, Japanese, mathematics, and science. At present
time, there are enormous opportunities for teachers and students in joining distance
learning through continuing education to supplement their educational goals
(Lemlech, 1998).
Asynchronous delivery includes all archived and stored materials like CD-
ROM, web-pages, email, fax, videotape, and so forth. Asynchronous technology is
widely used in institutions of higher learning. With funding from the Alfred P. Sloan
Foundation, universities receive grants by developing asynchronous learning network
(ALN) projects about current fields, concepts, and trends in education and training.
On November 17-18, 2001, The Seventh Sloan-C International Conference on
Online Learning was held at the University of Central Florida, Orlando, Florida.
Its central theme was “Emerging Standards of Excellence in Asynchronous
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Learning.” The two-day confab, participated in by numerous universities, focused on
issues like Institutional Best Practices’ , Emerging Standards o f Excellence in
Online Technology, Emerging Standards o f Excellence in Assessment and
Evaluation, Emerging Standards o f Excellence fo r Learning and Pedagogy; and
Emerging Standards o f Excellence fo r Faculty Development and Participation (ALN,
2001).
Earlier telecommunications projects (Watemet, McGraw-Hill Information
Exchange (MIX), Kids Network) used in globalizing the classroom around the
country allow learners to collect and analyze data on local and global phenomena
and share their data and findings with students around the world. The challenge is to
relate science concepts to global themes such as environment, pollution, natural
resources, energy, food, population, war technology, and human health and defense
(Lenk, 1988; Hassard, 1992).
Information Superhighway
Another technological innovation using telecommunication is the use of the
Internet commonly called the Information Superhighway. President Clinton’s
Technology Literacy Challenge calls for an effort to connect all U.S. public schools
and every institutional room, that is classroom, computer lab, and library/media
center, to the Internet. In just 3 years, the percentage of U.S. public schools with
Internet access increased from 35 percent in fell 1994 to 78 percent in fell 1997.
On the whole, schools are on track toward achieving the goal of connecting all of
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the nation’s public schools to the Internet. In a speech by Secretary Irving of the
United States Department of Commerce, he stated “Connecting classrooms to the
Information Superhighway will help us achieve both goals. We can bring the
resources o f the nation’s and world’s best libraries, museums, and research labs to
any classroom, whether located in our inner cities, or rural towns. And learning
how to navigate on the Information Superhighway will develop critical computer
and technology-related skills that have become essential skills for virtually every
vocation” (NTIA, 1997, p.2).
Authoring tools: Multimedia/Hypermedia
Authoring tools are another form of CBE that holds so much promise in
multi-media or hypermedia (e.g., interactive computer presentation). An authoring
tool is a software program that allows non-programmers to create a computer-based
instruction (CBI) without using a programming language. An authoring tool is used
to produce a stack of cards (the window) that contains text, graphics, and buttons.
The buttons are used to navigate from one stack to another or from one card to
another. Sound, animation, quicktime movie and other visual effects can be added
to enhance a newly designed software or hypermedia. The most common authoring
tools are HyperCard™, ToolBook™, MediaText™, HyperStudio™, and
Powerpoint™.
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Microworld: Simulations
Microworld is a another kind of computer-based instruction (CBI) that
simulates a microworld environment. The microworld environment is a computer
laboratory simulating real world phenomena. These environments allow students to
discover scientific principles on their own, to ask questions, interpret results, and
draw conclusions. Experiments can be scaffolded so that the increase in complexity
is gradual, and a number of interactive variables can be incorporated to reflect a
real-world problem. In scaffolding, students are given the opportunity to explore and
discover scientific principles by actively constructing meaning (e.g., putting and
linking knowledge together). Although microworlds should not replace
laboratories, they can help students think critically and evaluate, not merely accept
theory and methods of scientific investigation (Peterson, Jungck, Sharpe, & Finzer,
1987; Hassard, 1994, Roth, et. al., 1996).
A particular example of a microworld environment is Science Quest (1991). It
was developed by the Florida State University, where middle school students studied
the processing power of the computer and the visual capabilities of IVD.
The students were able to explore a wide variety of micro world environments
and problems to solve. There are numerous micro world projects on the Internet like
Educational Space Simulations Projects, Molecular Dynamics on a Whole New
Scale, and Plant Studio to name a few.
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Expert Tutoring Systems (Artificial Intelligence)
Expert Tutoring System (Artificial Intelligence) is another formofCBE used
in tutoring and drill and practice. An example is intelligent tutoring system (ITS),
which is a third type of computer-assisted learning. It provides a workable and
supportive learning environment to facilitate active learning. An expert tutoring
environment is structured so that students construct new knowledge from their
existing knowledge. From this premise, the notion of misconception or “bug” plays a
central role. Ideally, a student’s bug will cause an erroneous result which he will
notice. Hence, the bug is corrected from a nonconstructive bug into a constructive
bug (Burton & Brown, 1982).
Another example of an expert tutoring system is a prototype designed by
Sleeman & Brown (1982). The goal is to improve a student’s inquiry skills within a
micro world environment in the domains of microeconomics, light refraction, and
electrical circuits where students interactively learn through observation and
discovery. After the activities, inquiry strategies of students are compared to
expert and nonexpert learners. To use the program to its fullest, it is
recommended that an “inquiry coach” be partially designed to intervene so that
students’ use of strategies can be more systematic and effective.
The current capabilities of many artificial intelligence (AI) systems closely
match some of the specialized needs of disabled people. There is a growing interest
in applying the scientific knowledge and engineering experience developed by AI
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researchers to the domain of assistive technology. Assistive technology is a
growing field within the AI community. Some work in assistive technology can be
viewed as an intermediate step to a full AI system. Also, AI is the inclusion of a
human in the cognitive loop that can allow solutions to be found for unsolved
problems. Current research in many areas o f assistive technology includes sign
language translators, robotic wheelchairs, eye-tracking interfaces, and assistive robots
for the elderly and mobility impaired (Mittal, Yanco, Aronis, & Simpson, 1998;
AAAI, 1996).
Summing-up, the capabilities and applications of instructional technology
such as telecommunication (synchronous/asynchronous technology, web-based
distance learning and instruction, information superhighway), authoring tools
(multimedia/hypermedia); microworld (simulations), and expert tutoring systems
(artificial intelligence) have been briefly cited as they relate to teaching and learning
science. From the rationale o f the capabilities and applications of instructional
technology come questions such as: Are there better research methods that could
understand the new directions of instructional technology? If the answer is in the
affirmative, then the revolutionary potential of instructional technology might act
as a catalyst to accomplish the technological goals of science education for the 21st
century.
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CHAPTER 3
METHODOLOGY
Introduction
“One of the most important task that teachers undertake is to teach
concepts” (Platten, 1991, p. 2). The lack of firm understanding of concepts
subjects the learner to experience conceptual difficulties in analyzing information
or grasping even the basics in performing the task in academic areas such as
physical science. For example, not knowing the concept of chemical symbols and
oxidation numbers in chemistry severely limits the learner to write and balance
chemical compounds in an equation and solve the various stoichiometric
relationships o f reacting substances and products.
The ability to create concepts and analyze information is generally regarded
as the fundamental thinking skill. Various kinds of thinking are enhanced by
particular models of teaching. Some models, for instance, are designed to teach
students to attack problems inductively (concept formation), attain concepts
and analyze thinking strategies (concept attainment), analyze social issues and
problems {jurisprudential and role playing), break set and think divergently
{synectics and group investigation), work together to generate and test
hypotheses (group investigation and scientific inquiry), reason causally
{inquiry training, scientific inquiry, synectics, group investigation, simulation); and
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master complex bodies o f information (memory, scientific inquiry, group
investigation) (Joyce & Weil, 1996).
The purpose of this study is to investigate the effect o f microcomputer
based-laboratory instructions in impacting student concept attainment and attitude
towards the use of technologies in the science classroom. The study offers a
quantitative correlation of variables such as MBL to students’ pretest-posttest total
scores, treatment groups, levels of differentiated achievement, and attitudes.
Likewise, the study attempts to illustrate how concept attainment model (Joyce &
Weil, 1996) and instructional technology can be integrated so that learning to think is
an important component of the activity performed in a technology-enhanced science
laboratory (TESL). That is, a learning environment where students are given
appropriate instructional strategies to construct knowledge from their experiences
by visualizing, exploring, and understanding physical science concepts. The
construction o f knowledge is the overarching theme to attaining concepts and analyze
thinking strategies.
Research Questions
1. What is the impact of the use of microcomputer-based laboratory activities in the
physical sciences on concept attainment among middle school students of varying
levels of differentiated development?
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2. What is the impact of the use of microcomputer-based laboratory activities in the
physical sciences on attitude towards computer-based technologies among students of
varying levels o f differentiated achievement?
Sample Selection and Procedures
The sample consisted of six eighth grade physical science classes (N=149
students) of both genders, mixed-ability, and from low income families in an
urban-inner city school of Los Angeles, California. The school is classified with an
Academic Performance Index (API) of 2 out of 9 based on the 1999-2000 Stanford-
9 Test and Aprenda 2 Test (Spanish only) (LAUSD, 2000). The participation in the
study was voluntary but the intervention would be seen by all as a regular
instructional technique. Student’s involvement was solicited by the researcher and
those who were interested were provided with an informed consent form . (See
Appendix One). District’s policy to conduct research is granted by the local
administrator if data are obtained solely from one single school. (See Appendix
Three).
The sample was grouped into three levels o f differentiated achievement
namely accelerated-science students (AS), regular-science students (RS), and
sheltered-science students (SS) based on the results o f the 1999-2000 Stanford-9
Test, Apprenda Test (Spanish only), and Home Language Survey administered by the
school to English Language Learners (ELL) as per District’s Master Plan (LAUSD,
1995). Students grouped in the accelerated-science group belong to the upper 50-
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percentile of the Stanford 9 test; the regular-science group are students
redesignated (passing score in math, reading, and language of the Stanford 9 Test);
and the sheltered-student group belong to the Master Plan (LAUSD, 1995).
Table 4 shows the classification o f participants based on Stanford 9 and
Aprenda 2 (Spanish only) Tests and language status (e.g., home language survey &
assessment).
TABLE 4. Classification of Participants Based on Academic Tests and
Language Status
Level of Differentiated
Achievement
Academic Tests & Language Status
Accelerated-Science Students (ASC)
Students who passed the Stanford 9 Test and classified
as English-Only (EO) or Fluent English (FP).
Regular-Science Students (RSS)
Students who garnered below 50 percentile of the
Stanford 9 Test and classified as Limited English
Proficient (LEP) or Preparation for Redesignated
Program (PRP).
Sheltered-Science Students (SSS)
Students who took the Aprenda 2 (Spanish only) Test
and/or garnered below 50 percentile in the Stanford 9
test and classified as LEP. Teaching is provided using
SDAIE instruction.
7 rom 1999 School-Based Management Plan, Appendix XIII.
The Master Plan is a program provided by the District to enhance English
language training to English Language Learners (ELL). Sheltered-science students
(SS) belong to the Preparation for Redesignation Program (PRP) who have
completed Model A (Spanish instruction and increasing instruction in English) or
Model B (speakers of any other languages except Spanish). The instructions in
content areas, such as science and mathematics, are performed using the Specially
Designed Academic Instructions in English (SDAIE). Teachers with bilingual
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credentials from the California Commission on Teacher Credentialing, are the only
teachers authorized to teach sheltered classes. Likewise, each subgroup was
assigned to one of the two instructional strategies namely, MBL group or traditional
laboratory (TRL) group.
Table S presents a detailed description o f the assignment of the six physical
science classes to MBL or traditional treatment groups. Twenty three students were
unable to take the posttest due to illness. Thus, the data collected carne from 149
students.
TABLE 5. Assignment of Subjects to MBL or Traditional Groups in Physical
Science
Characteristic ASC Students RSC Students SSC Students
AS, AS, RS, RS, ss, ss,
Original Class n = 31 n = 31 n = 30 n = 25 n = 30 n = 25
Treatment Group n = 31 n = 31 n = 30 n = 25 n = 30 n = 25
Concept of
Investigation and
Treatment Variable
Thermodynamics
Electricity
Light
MBL TRL
MBL TRL
MBL TRL
MBL TRL
MBL TRL
MBL TRL
MBL TRL
MBL TRL
MBL TRL
Legend:
Level of Differentiated Achievement
ASC = Accelerated Science Class: Group ASi and AS2
RSC = Regular Science Class: Group RSi and RS2
SSC = Sheltered Science Class: Group SSt and SS2
Instructional Strategy (Treatment Group)
MBL = Microcomputer Based Laboratory
TRL = Traditional Laboratory
Investigations: (Traditional and MBL)
Thermodynamics: (Hot and Cold Thermodynamics)
Electricity: (Fruit Battery or Wet Cell)
Light: (Light Intensity)
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Research Design of the Study
The one-group pretest-posttest experimental design (Gall et al., 1996; Isaac
& Michael, 199S; Pedhazur et al., 1991) used in this study is a one-variable
experiment which involved the manipulation o f a single treatment (e.g., MBL). This
was followed by observing the effects of this independent (predictor, input,
manipulated, treatment, intervention, or stimulus) variable (e.g., MBL) on the
dependent (criterion, posttest, output, or outcome) variables (Gall et. al., 1996;
Isaac & Michael, 1995) namely the pretest-posttest total scores, three levels of
differentiated achievement, and two instructional strategies. Finally, this was
followed by administering a modified-student-computer survey (M-SCAS) and
observed the effects of MBL to student attitudes towards the use of computer
technology (Delcourt & Kinzie, 1993). Many researchers encouraged the evaluation
of CBE programs like MBL in terms o f student attitudes towards computers (Kulik et.
al., 1985; Loyd & Gressard, 1984). So it is included in this research investigation..
In this study, the experimental design involved three steps specifically (1)
the administration of a pretest measuring the dependent variable, (2) the
implementation of the experimental treatment or independent variable (e.g., MBL)
to the participants using the Concept Attainment Model, and (3) the adm inistration
of a posttest that measured the dependent variable again. Details of each step are
described in the data collection procedures. The effects of the differentiated
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treatments were determined by comparing the pretest and posttest total scores. The
completed comparative analysis are discussed in Chapter IV (Results and Analysis).
Data Collection Procedures
The following procedures based on a one-group pretest-posttest experimental
design were used in collecting data for this study.
Administration of the Pretest
The first step in the one-group prestest-posttest experimental design is the
administration of the pretest. This step is composed of two parts namely the pilot
study and the pretest which are described below.
Pilot Study
Prior to the administration of the pretest, a pilot study was conducted to
evaluate the procedures and instruments used in the study. There were two parts of
the pilot study namely 1) presentation of various MBL instructional delivery
platforms (e.g., hardware and software) and 2) administration of a pilot test of the
pretest.
Presentation of MBL Platforms
In the first part of the pilot study, the different MBL instructional delivery
platforms and activities were introduced to a sample (N— 25) of eighth-grade
students enrolled in a computer class at the school site. The approach in presenting
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each of the three concepts of investigation (e.g., electricity, thermodynamics, and
light) followed a concept attainment model (exemplars & non-exemplars), and
supplemented by the inductive or process approach (concept formation) (Joyce &
Weil, 1996; Lawson, Abraham, & Renner, 1989; Tennyson & Cocchiarella, 1986).
Administration of Pilot Test
In the second part of the pilot study, a pilot test o f the pretest was
administered to the students during their physical science class. The pilot test
consisted of 45-multiple-choice questions from the concepts of thermodynamics,
electricity, and light. This test was prepared by a group of science instructional
experts including two college physics instructors, two secondary physical science
teachers, a science head of department, and the researcher. The pilot test was
utilized to refine the questions employed in the preparation of the final pretest and
to identify conceptual difficulties or misconceptions about the three concepts of
investigation. The students in the pilot study did not participate in the final research
study. (See Appendices Four, Five and Six).
Reliability and Validity of Pilot Test
Appropriate controls were adopted to enhance the reliability and validity
in the design of the pilot test. The design of the questions in the pilot test was focused
on common misconceptions or conceptual difficulties from a review o f the literature,
misconceptions noted from the experiences o f the science instructional
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experts, and misconceptions noted from students. Sixteen conceptual areas to
formulate the pilot test questions were identified. In thermodynamics, questions
about the nature of heat energy, difference between heat and temperature, plotting
of temperature scales, effects of heat energy on matter, heat capacity, specific heat,
and law o f heat exchange were asked. In light, questions in light measurements,
luminous and illuminated objects, intensity of source, and amount of illumination
were asked. Finally, in the electricity section, questions in energy conversion,
chemical cells, electrolytes, and reactivity series o f metals were queried. The three
topics of investigation are embodied in the current District’s Standards in
Science Instructions for middle schools.
The Final Pretest
The fin a l pretest consisted of 30-multiple choice questions based on the
pilot test and its reliability and validity verified by the same group of science
instructional experts. (See Appendix Four, Five, and Six). Ten questions were
derived from each concept of thermodynamics, light, and electricity. The fin a l
pretest was administered to six eighth grade classes (N=172) during their physical
science classes.
An alternative method to verify the reliability and validity of the fin a l
pretest was performed by using Cronbach’s alpha (SPSS 10 for Windows, 1999) for
internal consistency. The measured value is discussed in Chapter IV (Results).
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Finally, the results of the fin a l pretest were tabulated and analyzed. Also, the list of
conceptual difficulties was identified, compiled, and analyzed. (See Table 11).
The Experimental Treatment
The second part of the one-group pretest-posttest experimental design was
the implementation of the experimental treatment (MBL and traditional lab). The MBL
experimental treatment was composed of three MBL activities from Science
Workshop™ (PASCO Scientific, 1999). The MBL activities were (Mixing Hot and
Cold Water—Temperature Sensor) for thermodynamics, (Fruit Battery or Wet
Cell—Voltage Sensor) for electricity, and finally, (Light Versus Distance—Light
Sensor) for light. The goal of using three different sensors was to provide the
participants ample experience and exposure to scientific products and processes in
creating concepts and analyzing critical thinking skills in a constructivist
environment. Likewise, the traditional laboratory group (TRL) performed the same
concept of investigation (thermodynamics, electricity, and light) and measured
thermal equilibrium, potential difference, and illumination respectively but used
traditional equipment like voltmeters, beakers, metal electrodes, thermometers,
electrical wires, batteries, AC and DC lights, and others. (See Appendices Seven,
Eighth, and Nine).
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Instructional Strategies and Investigations
The instructional strategies and investigations used in teaching each concept
of interest were similar in structure. That is, the instructions for MBL group
followed the three phases o f the Concept Attainment Model by using an
instructional plan depicting the operational rule, critical attributes, positive and
negative exemplars (labeled and unlabeled) of the concept. Additionally, the
investigations were based on MBL instructions using probes or sensors for the
treatment group (MBL). (See Appendices Ten, Eleven, and Twelve). On the other
hand, instructions for the traditional laboratory group (TRL) used traditional
equipment or apparatus to measure the equilibrium temperature, potential drop, and
illumination respectively. (See Appendices Seven, Eighth, and Nine).
Instructions
Three instructors, including the researcher in a total of six classes during
the spring semester, taught classes in the three concepts of investigation. Each
instructor taught two classes each to a total sample of one hundred seventy two
students. These instructors had similar teaching aims for each investigation and
used similar class activities and evaluation. They used teaching strategies
recommended by Joyce & Weil (1996) and Pritchard (1994) as strategies that
promote concept attainment. The instructions followed the three phases of the
Concept Attainment Model and used an instructional plan depicting the operational
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rule, critical attributes, positive and negative exemplars (labeled and unlabeled) of
the concept. (Lesson Plans are found in Appendices Thirteen, Fourteen, and
Fifteen). On the other hand, teaching objectives for the traditional laboratory group
and MBL group were similar and based on the deductive approach for the three
concepts of investigations (see Appendices Seven, Eighth, and Nine).
Each MBL investigation required two periods: each period was 52 minutes
in duration. The traditional lab group required three periods to accommodate
additional time due to equipment malfunction and students’ difficulty to seting-up
the activity.
Teaching with the Concept Attainment Model
The instruction in presenting the concept o f thermodynamics, electricity,
and light followed the Concept Attainment Model (Joyce & Weil, 1996; Pandey,
1993; Nelson & Pan, 1997) for the MBL group. This model is designed to teach
students how to learn concepts by determining the essential difference between
positive and negative exemplars ( Melnick & Gable, 1990) through an inductive
thinking process (e.g., pattern recognition and categorizing). This conceptual thinking
activity was composed o f a concept analysis and preparation of positive and negative
exemplars as shown in the instructional plan of of each concept o f interest. A case-
study guideline and laboratory activity were used in teaching the abstract concepts of
thermodynamics, electricity and light. There were three phases involved in using the
Concept Attainment Model o f teaching (Joyce & Weil, 1996; Pritchard, 1994).
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Phase One: Presentation o f Exemplars
The teacher told the students that they were going to learn a concept.
Then, they were allowed to “mess up” with the equipment they were going to use.
The teacher invited students to derive the concept and presented case-study
guidelines labeled as positive and negative exemplars. Students working in a team
of four compared the features of positive and negative exemplars. The students
working with their group developed and tested hypotheses of the concept of
investigation based on its critical attributes. They were asked to look and analyze at
the critical attributes of the positive exemplars that were not found in the negative
exemplars.
Phase Two: Testing o f the Concept
The teacher presented additional unlabeled positive and negative exemplars
based on the laboratory exercise the students performed. The students tested the
concept hypothesis with the new unlabeled exemplars. Likewise, students modified
and refined the concept hypothesis as necessary using the MBL results on the screen
of the computer. When all or most of the exemplars were correctly identified,
the teacher asked the students to finally “test” the hypothesis against the data they
have so far collected. If their hypothesis is confirmed, the teacher named the
concept followed by a definition or the operational rule o f the concept. If the concept
is not confirmed, the teacher presented additional exemplars and asked students
to compare the patterns in the positive exemplars once again. This was done by
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replicating the MBL activity and observing the real-time graph or numerical values
generated in the computer screen.
Phase Three: Analysis o f Thinking Strategies
After the students attained the concept, the teacher asked the students to
describe their thoughts as they participated in the concept attainment. That is, the
teacher guided student’s analysis of critical thinking (Norris, 1992) strategies they
used by answering questions. Simultaneously, students explained the ways attributes
were found. They also described attribute cues and integration in the hypothesis.
Finally, each group o f students discussed the range o f hypothesis generated with
the instructor acting as facilitator. This was part o f the open forum during the
post-laboratory.
The Laboratory Component of Instruction
Two methods of conducting laboratory activities or investigations, MBL and
traditional (TRL) approach were investigated. The MBL students used a computer
to collect, display, store, and print the graphically presented data from
exercises. The only time they used ordinary paper and pencil was when they
answered questions about their exercises. The traditional students used traditional
equipment (i.e., stop watches, thermometers, paper, voltmeter, electrodes) to collect
their data and construct their graph. Students in both groups conducted simple
experiments in thermodynamics, electricity, and light.
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Laboratory activities or investigation in each concept of investigation were
composed of two sets. In thermodynamics, the group using traditional equipment
performed one set of activities and the MBL group using MBL tools and activities
performed the second set. The instructors for the concepts of
electricity and light used the same procedures. At the onset, the students were
allowed to “mess up” the various MBL delivery platforms (hardware and software)
and traditional equipment. They were grouped in teams of four students and
performed the various activities and a post-laboratory discussion ended each
activity. These activities were performed in Phase 3 of the concept attainment model.
Concept o f Thermodynamics
Instructional Activity
The first instructional activity Mixing Hot and Cold Water (Temperature
Sensor) for thermodynamics; was a study o f thermal energy as it moves from one
substance with a high temperature to a substance of lower temperature until the
system reaches thermal equilibrium. The thermal energy of a substance depends on
the mass, specific heat, and the change in temperature of the substance in question.
Thus, thermal energy is the amount of heat gained or lost, which varies directly to the
change in temperature.
The change in temperature was measured by a thermometer at different
time intervals in the traditional laboratory (TRL) investigation. The students plotted
the temperature-time graph using paper and pencil. Then they interpreted the hand
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made graph. Simultaneously, in the MBL version, student the change in temperature
by a temperature sensor or probe that was connected to a computer through an
interface (new models use USB link).The students observed the generated graph and
made a printout for analysis. (See Appendices Seven and Eight).
The Instructional Plan
The instructional plan in the traditional group included an introduction and
discussion of the concept of investigation presented deductively. The instructional
plan in the MBL group, on the other hand, included a scientific case-study
guidelines exemplars and laboratory activity. (See Appendix 13). It included
an analysis of the concept of hot-cold water thermodynamics as reflected in the
operational rule and critical attributes. Two sets of positive and negative
exemplars were developed to help students derive the concept, then test and
confirm it. The first set of exemplars was presented with labels to have identifiable
examples o f the concept that can be analyzed for its critical attributes. The second
set is presented without labels. After the performance of the class activity, students
were asked to assign positive and negative labels to them on the basis of the
concept they have hypothesized using the first set and their experimental
observations in the laboratory activity.
The positive exemplars reflected almost all of the critical attributes of the
concept of hot-cold-water thermodynamics. The negative exemplars did, in almost
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every case, contain some temperature information which was also a critical attribute
o f the positive exemplars. Close comparison of the two kinds of exemplars, however,
revealed that the negative ones do not describe change in phase or heat transfer and
kinetic energy of atoms and molecules.
The main purpose of this part in the Concept Attainment Model, exemplars
taught a concept by having students compared scientific-case-study guidelines that
depicted valid, concept-reflecting activity with guidelines that were not reflective
activities of the concept o f investigation (Pritchard, 1994). Negative exemplars
were not incorrect examples.
Concept of Electricity
Instructional Activity
The second instructional activity, Fruit Battery or Wet Cell (Voltage Sensor)
for electricity, was a study of the conversion of stored energy in fruits like apples into
electricity, (e.g., wet cell). The dependent variable was the voltage, or potential
difference, in a fruit or tuber. This was measured in the conventional laboratory by a
voltmeter connected to two metallic terminals (e.g., Zn and Cu) inserted into a piece
of fruit or tuber. In the MBL version, a voltage sensor with a pair of alligator clips
was slipped into the end of a fruit. The voltage reading was generated on the
computer screen (see Appendices Nine and Ten).
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Instructional Plan
The instructional plan for the traditional group included an introduction and
discussion of the concept of investigation presented deductively, while, the
instructional plan for the MBL group included scientific-case-study guideline
exemplars and class activity. (See Appendix Fourteen). The MBL group plan included
an analysis of the concept o f electricity (e.g., fruit battery) based on its operational
rule and critical attributes in the instructional plan
Two sets o f positive and negative exemplars were developed to help
students derive the concept, then test and confirm it. The first set of exemplars was
presented with labels to have identifiable examples of the concept, and it can be
analyzed for its critical attributes. The second set is presented without labels. After
the performance of the laboratory activity, students were asked to assign positive
and negative labels to them on the basis o f the concept they have hypothesized
using the first set o f exemplars and their experimental observations in the laboratory
activity.
The positive exemplars reflected almost all the critical attributes of the
electricity concept. The negative exemplars, in almost every case, contained
some temperature information which was also a critical attribute of the of the positive
exemplars. Close comparison of the two kinds of exemplars, however, revealed
that the negative ones did not describe the reactivity series of metals and the
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acidic/basic or conducting properties of substances. Negative exemplars were not
incorrect examples. They were real and active.
Concept o f Light
Instructional Activity
The third instructional activity, Light Intensity Versus Distance (Light
Sensor) for light, was a study to investigate the relationship between light intensity
and the distance from the light source. The dependent variable, the intensity of
light, and its imaginary spherical boundary from the point light source was measured
by a meter stick in the traditional laboratory version. In the MBL version, light
intensity was measured by a light sensor interfaced to a computer as it moves away
from a stationary DC light source. (See Appendices Eleven and Twelve).
Instructional Plan
The instructional plan for the traditional group included an introduction and
discussion of the concept of investigation presented deductively. The instructional
plan for the MBL group included scientific-case-study guideline exemplars and
class activity. (See Appendix Fifteen). It included an analysis o f the concept of light
(e.g., fruit battery or wet cell) as described in the operational rule and four critical
attributes.
Two sets of positive and negative exemplars were developed to help
students derive the concept, then test and confirm it. The first set of exemplars was
presented with labels to have identifiable examples of the concept and can be
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analyzed for its critical attributes. The second set is presented without labels. After
the performance o f the class activity, students were asked to assign positive and
negative labels to them on the basis of the concept they have hypothesized using the
first set and their experimental observations in the lab activity..
The positive exemplars reflected almost all critical attributes of the concept
o f light. The negative exemplars did in almost every case contain some light
energy source which was also a critical attribute o f the positive exemplars. Close
comparison of the two kinds of exemplars, however, revealed that the negative ones
did not describe the inverse variation with reflecting areas and direct variation with
the distance o f separation. The negative exemplars were not incorrect examples.
They were true scientific case studies but not reflective activities o f the concept of
interest.
Administration of the Posttest
The third or last part in the one-group pretest-posttest experimental design
used in this study was the administration of a posttest. In this study, the use o f the
term posttest was to describe the measured variable that was the intended outcome
of the experimental treatment. The posttest was a 30-multiple-choice test similar to
the fin a l pretest. That is, using nearly the same number of questions and concepts
in thermodynamics, electricity, and light. It was administered to the participants in
their physical science classes. The results o f the posttest were compiled and
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tabulated for analysis. Later on, concept difficulties were listed, identified and
reviewed (see Table 11).
A post-laboratory discussion was held in order to generate the range of
hypothesis and assisted the participants to confirm the hypothesis. The
discussion also attempted to modify or put to closure to commonly held conceptual
difficulties or misconceptions about the three concepts of investigation by using
graphical or numerical outputs in MBL or traditional laboratory activities.
Reliability and Validity Issues
Although an experiment is a powerful research design, it is not perfect (Gall
et al., 1996). Popper (1968) observed that, no single laboratory activity provides a
irrefutable display of cause and effect, and therefore replications o f experiments—
especially ones that test alternative causal hypothesis—are desirable.
Suitable controls of extraneous variables common to a one-group pretest-
posttest experimental design were established so that any change in the posttest can
be attributed only to the experimental treatment that was manipulated by the
researcher. The extraneous variables used as suitable controls were the selection of
a convenient sample, use o f a uniform classroom environment, adm inistration of
valid and reliable written documents, and the adoption of a six-week classroom
instructional timeline used in the implementation of the experimental treatment.
These extraneous variables were adopted to m inim ize the threat to internal validity
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such as time, maturation, testing, instrumentation, and interaction of selection to other
factors such as compensatory rivalry and resentful demoralization of the control
group and external validity such as interaction o f selection with MBL treatment and
interaction of testing with MBL..
Similarly, as previously mentioned, suitable controls were adopted in the
design of the pilot test which was used in the pilot study and in refining the final
pretest. The design of the questions used in the pretest, were focused on common
misconceptions from a review of the literature, misconceptions noted from the
experience of the instructors and conceptual difficulties noted from the participants.
An alternative method to verify the internal consistency estimate of the final pretest
was performed using Cronbach’s alpha (SPSS 10 for Windows, 1999). The measured
value is discussed in Chapter 4 (Results and Analysis).
Other Statistical Measures
Science instructional experts and the researcher in the collection of data
developed three tests and adopted a study survey. The pilot test was developed and
used in the pilot study. Similarly, it was used further to refine the development of
the fin a l pretest and posttest. The development o f the pretest, posttest and
application of the three steps of the one-variable design were described earlier in
part two. The data from these instruments were used to answer the two research
questions. Likewise, the student-computer attitude survey (Delcourt & Kinzie, 1993)
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was adopted and modified to measure students’ attitude towards the use of computer
technology in the science classroom. The administration of the survey is discussed
below.
Student Computer Attitude Survey
The evaluation of computer-based education (CBE) programs like MBL in
terms o f student attitude towards computers had been encouraged by many
researchers (Kulik et al., 1985; Loyd & Gressard, 1984). In this study, it was
appropriate to examine the attitude and perception o f competence towards the use
of MBL in the science classroom or technology-enhanced science laboratory.
Hence, an appropriate instrument like the Attitude towards Computer Technology
(ACT) (Delcourt & Kinzie, 1993) is adopted for evaluation purposes.
The ACT instrument is a Likert-type questionnaire that was developed and
validated for use with teacher education students, practicing teachers, and other
specialized population groups. The alpha reliability for the entire ACT instrument is
fairly high (0.89); as were reliability values obtained for two conceptual factors
(Comfort/ Anxiety, (0.90); Usefulness, (0.83) (Delcourt & Kinzie,1993). According
to Gable (1986), reliability figures of above 0.70 are acceptable levels for an attitude
measure.
From this premise, the ACT instrument is used but modified to make it more
applicable to the investigation. The modified-computer attitude survey (m-SCAS)
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contains slight changes in the wording o f several items based on the MBL
instructions. This survey instrument is composed o f 19 items, 11 measuring
Usefulness (for example, “With the use of computer technologies, I can understand
instructional materials to enhance my studies”) and 8 for measuring Comfort/Anxiety
(“I feel at ease learning about computer technologies”). The responses to the attitude
survey (N=149) are subjected to a Principal Component Analysis (PC A) to find the
total variance.
The purpose of a Principal Component Analysis (PC A) is to group the 19-
question attitude survey into three factors. Grouping the variables into factors
served as a data reduction. Hence, Factor I contained 11 items measuring Comfort-
Anxiety in using computer technology and Factor II and II contained 8 items
measuring Usefulness of computer technology in the classroom. (See Appendix
Thirteen).
Moreover, the Principal Component Analysis is one method of factor
extraction in Factor Analysis (FA). Factor analysis is used in data reduction to
identify a small number of factors that explain most of the variance observed in a
much larger number of manifest variables. Seven methods of factor extraction and
five methods o f rotation are available including Varimax, Direct Oblimin and
Promax for non-ortogonal rotations (SPSS 10 for Windows, 1999). In this study,
the Varimax rotation is used to reveal factor structures and display the factor
loadings obtained from each group of items. The main reason for using a Varimax
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rotation was it was more commonly used and highly recommended in PCA to perform
maximum rotations and extractions than other methods (SPSS 10 for Windows,
1999). (See Appendix Sixteen).
Data Analysis
The data from the pretest and posttest total scores were collected and scored
by the researcher. The data were analyzed using descriptive statistic such as
percentage, frequencies, group means, and inferential statistics such as F-test of
Analysis of Variance (ANOVA). ANOVA was used to measure the mean
differences in pretest and posttest total scores and pretest-posttest total scores on
level of differentiated achievement and treatment group. Similarly, a post hoc range
comparison test was used to determine the significant difference of specific means
such as posttest on level of differentiated achievement. Furthermore, a Principal
Component Analysis (PCA) was performed in the responses of the modified-
student survey.
Summary of the Methodology
The main purpose of this study was to investigate the impact of the use of
microcomputer-based laboratory activities m the physical sciences on concept
attainment among middle school students o f varying levels of cognitive
development. It also investigated the impact on the use o f microcomputer-based
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laboratory instruction on student attitudes towards the use of computer
technologies. Two methods o f conducting laboratory activities or investigations,
MBL and traditional (TRL) approach were investigated. The MBL students used a
computer to collect, display, store, and print the graphically presented data from
their exercises while the TRL group used traditional equipment (i.e., stop watches,
thermometers, paper, voltmeter, electrodes, etc.) to collect data and construct their
graph manually. Students in both groups conducted simple class activities in
thermodynamics, electricity, and light as written in the current District Guidelines for
Instruction (Science) for middle school science (LAUSD, 1999).
In order to analyze the results of the study, different methods were employed
in collecting data. The classic one-group variable experimental design was used. It
was composed of three steps: the administration of the pretest, the implementation
of the experimental treatment, and the administration of the posttest. Suitable
control of extraneous variables were undertaken in the pretest. Also, the reliability
and validity of the pretest were piloted by a group of science instructional experts and
the use of a statistical procedure (e.g., Cronbach’s alpha). Likewise, the responses to
the modified computer attitude survey were subjected to a Principal Component
Analysis (PCA) to reveal factor loadings (SPSS 10 For Windows, 1999).
In the administration o f the experimental treatment, the Concept Attainment
Model o f teaching was used in the MBL group to categorize the target concepts.
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Positive and negative exemplars (labeled and unlabeled) were presented as an aid to
help students identify the concepts. Likewise, instructional plans and laboratory
activities were utilized in augmenting the application of the concept attainment
model for the MBL group. Similarly, instructional plans and laboratory activities
were employed for the traditional group (TRL) and presented deductively. Finally,
descriptive and inferential statistical techniques were used to analyze and interpret
the collected data from the written documents (e.g., pretest, posttest, and modified
attitude-survey) used by all participants. The results and analyses o f data collected are
discussed in Chapter 4 (Results and Analysis).
I l l
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CHAPTER 4
RESULTS AND ANALYSIS
Overview of Statistical Procedures
This chapter presents the results and analysis of data collected from middle
school students of mixed-gender, mixed-ability, and from low income families in
an urban inner-city school of Los Angeles, California. The statistical analysis will
enable science teachers to better understand the construction of an instructional
program that integrated ideas about teaching thinking skills using computers.
Microcomputer-based laboratory (MBL) was used to develop an instructional
program so that middle school students could organize and explore concepts based
on the concept attainment model, which help students learn to determine the
characteristics of a category. Simultaneously, a computer survey on the impact of
MBL on student attitudes towards the use of computer technologies was
administered and analyzed. The data were collected and analyzed based on the
procedures of a one-group pretest-posttest design and two research questions.
The first research question was an attempt to investigate the impact of
microcomputer-based laboratory (MBL) on concept attainment among students of
varying levels of differentiated achievement. The information collected from written
documents such as pretest total scores and posttest total scores was analyzed using
descriptive and inferential statistics such as percentages, frequencies, mean
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standard, standard deviation, F-test analysis (one-way ANOVA), and post hoc
comparison test. A one-way ANOVA was utilized to reveal any significant
difference in the pretest and posttest total scores, pretest-posttest total score with
level of differentiated achievement and treatment group. Finally, a post hoc
comparison range test was performed to determine specifically what means differ
significantly in the dependent variables.
The second research question was an attempt to find out the impact of
microcomputer-based laboratory (MBL) on attitude towards computer-based
technologies among students of varying levels of differentiated achievement. An
attitude-survey (Delcourt & Kinzie, 1993) was adopted to measure student attitude
toward the use of computer technology. This student-computer-attitude survey was
modified to make it more applicable to this study. The responses to the modified-
survey were subjected to a Principal Component Analysis (PCA). The purpose of
PCA was to search for clusters of variables that were correlated with each other.
Then the clusters were used to reveal factor structures and display factor loadings
of the 19 variables in the attitude-survey. The factor structures from the original
student-computer-attitude survey were adopted in the modified student-computer
attitude survey.
The first factor structure was loaded as Factor I (comfort/anxiety), the
second factor structure as Factor n (usefulness-positively phrased), and the third
factor structure as Factor m (usefulness-negatively phrased). The factor loadings
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(e.g., correlation coefficients) were displayed in every variable of Factors I, II, and
III (Delcourt & Kinzie, 1993). (See Appendix Thirteen).
Characteristics of Participants
Participants from middle school physical science classes (N=149)
volunteered in this study as described in Chapter 3 (Methodology). The
participants were grouped into three levels of differentiated achievement such
as accelerated science students (ACS), regular science students (RSS), and
sheltered science students (SSS), based on the results of the 1999-2000 Stanford-9
Test, Aprenda 2 Test (Spanish only), and language status (e.g., home language
survey) per Master Plan of the District. Each level performed three MBL and
traditional lab activities. (See Table 2 & 3).
The concepts of investigations were thermodynamics (hot-cold water
thermodynamics), electricity (fruit battery or wet cell), and light (intensity o f light)
for both treatment groups (MBL andTRL). (See Appendices 7 to 12).
Cooperative-learning groups of four students from each level were used to perform
the three MBL and TRL activities.
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Description of Results for Two Research Questions
First Research Question
Research Question 1: What is the impact of the use o f microcomputer-based
laboratory activities in the physical sciences on concept attainment among middle
school students of varying levels of differentiated achievement?
To answer the first research question, this study employed a one-group
pretest-posttest design involving the manipulation of a single treatment, and
microcomputer-based laboratory (MBL) instruction and was followed by
observing the effects of MBL by comparing: a) pretest total score and posttest total
score (Primary Analyses) and b) pretest-posttest total scores on level of differentiated
achievement and treatment group (Supplemental Analyses). The results on the
effects of MBL in the primary analyses and supplemental analyses are presented
below.
Primary Analyses
Pretest Total Score
The pretest is the dependent variable measured before administering the
experimental treatment. It is composed of 30-multiple-choice questions and is refined
based on the reliability and validity of the pilot test verified by the group o f science
instructional experts described in the Chapter 3 (Methodology). The pretest was
administered to six eighth grade classes (N=172) and its reliability and validity
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verified by the same group o f science instructional experts. The pretest involved three
sets of test questions. The first set of test questions was composed o f ten questions
about the principle of thermodynamics. The questions were drawn from a picture of a
pan of ice being heated. A temperature versus time graph accompanied the diagram
showing the curves at different temperature and time intervals. The other questions
involved the principles o f specific heat, kinetic energy, and thermal equilibrium. (See
Appendix Five).
The second set of test question was composed of ten questions about the
principle of electricity. Some questions were taken from a diagram showing the parts
and functions of an electro-chemical cell. Two different metallic electrodes were
connected to a 1.5 volt light bulb and submerged in dilute sulfuric acid solution.
The other questions came from a diagram showing a fruit battery connected to a
voltage sensor. (See Appendix Six).
Finally, the third set of test question was composed of ten questions about
the principle of light. The questions were derived from diagrams of the visible
spectrum and light emitted by point or real light sources using direct current (DC)
or alternating current (AC). Also, the distance between the light source and
illuminating surface were included. (See Appendix Seven).
Measures o f Reliability o f Pretest
The responses to the pretest were subjected to a reliability analysis to
determine its validity and reliability of internal consistency. Table 6 summarizes
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the measures of reliability for the 30-item multiple choice pretest questions. The
pretest reliability estimate was 0.7850 (using Cronbach’s alpha) and the
standardized item alpha was 0.8028. The value for the standardized item alpha was
computed based on a binary item o f l ’s (for a right answer) and 0’s (for a wrong
answer) for a specified dichotomized scored items.
TABLE 6. Measures of reliability o f the pretest total score
Item Means
Variance
Mean Minimum Maximum Reliability
coefficient
Standardized
Item Alpha
0.0399 0.3037 0.0741 0.9295 0.7849 0.8028
Analysis o f Reliability o f Pretest
The true alpha coefficient o f0.7849 (using Cronbach’s alpha) indicates that
about 79% of the variance o f the scale is systematic. The standard item alpha
coefficient o f0.8028 indicates that 80% of the standard scores on the items or
equivalent of the average correlation among items is used to estimate the internal
consistency. Nunnally (1978) established that a reliability estimate or internal
consistency of 0.70 is sufficient for experimental purposes. Hence, the pretest’s
reliability internal consistency is systematic for this experimental design.
As a result, the weighty alpha values of the pretest decrease error variance
and make a Type I error more likely (e.g., rejection of null hypothesis). Thus, if
significant effects are found between the criterion and predictor variables and other
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factors, they are more likely to be due to the application of the experimental
treatment and not by chance.
Measures o f Variability o f Pretest Total Score
Table 7 shows the variability o f the pretest total score. This is measured using
descriptive statistics such as percentages, frequencies, mean standard, standard
deviation, skewness, and kurtosis. The mean scores of 2.91 (SD = 1.5) for
thermodynamics, 2.97 (SD = 1.26) for electricity, and 2.60 (SD = 1.45) for light
show a normal distribution. The skew and kurtosis are less than one.
TABLE 7. Measures of variability of the pretest total score (N = 149)
Concept of
Investigation N Mean
Standard
Deviation
Skewness Kurtosis
Thermodynamics 149 2.906 1.4993 0.406 0.157
Light 149 2.569 1.4469 0.353 0.316
Electricity 149 2.973 1.2625 -0.071 -0.423
Pretest Total Score 149 8.481 2.7501 0.046 0.146
Valid N (listwise) 149
Similarly, the means are significantly different between the three concepts
of investigation. This difference indicates that students exhibited varying degrees
of conceptual difficulty in the questions asked for the three concepts. The negative
skew and kurtosis for electricity questions show a distribution of extreme scores at
the left end than the right end. The mean is in the direction of the extreme scores.
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This indicates a greater degree o f difficulty of the electricity questions fielded in the
pretest.
Posttest Total Score
Table 13 shows the frequency of the valid percent o f each question in the
posttest. The posttest is the dependent variable measured after administering the
experimental treatment. The posttest was a 30-item multiple-choice test similar to
the pretest which was administered to the participants in their respective physical
science classes. In this study, the use of the term posttest was to describe the
measured variable again.
Likewise, Table 8 shows the percentage o f the degree of conceptual difficulty
in each question of the posttest. The higher percentage, shown by one asterisk,
indicates a high degree of students’ conceptual difficulty in each question. This is
likely due to random guessing by the participants and the difficulty o f the
physics questions presented in to eight grade students coming from a school with an
API o f 2 out of 9. Moreover, 70% of the students of the school are classified as
English Language Learners (ELL) from low income families which likely contributes
to a high degree of conceptual difficulty. The low test scores in the Language Arts
and Reading sections of the 1999-2000 Stanford 9 Test attest to to this theory.
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TABLE 8. Frequency o f valid percent of posttest (N= 30)
Question
Number
Frequency
(L) (H)
Valid Percent
(L) (H)
Concept
PI 12 — 15 44.4 --------55.6 Thermodynamics
P2 22 — 05 81.5*...........18.5 Thermodynamics
P3 18 — 09 66.7*-------33.3 Thermodynamics
P4 23 — 04 85.2*-------14.8 Thermodynamics
P5 15 — 12 55.6 -------44.4 Thermodynamics
P6 11— 16 40.7 -------59.3 Thermodynamics
P7 18 — 09 66.7*----- 33.3 Thermodynamics
P8 21 — 06 77.8*-------22.2 Thermodynamics
P9 16 — 11 59.3 -------40.7 Thermodynamics
P10 22 — 05 81.5*-------18.5 Thermodynamics
P ll 02 — 25 07.4------**92.6 Light
P12 06— 21 22.2------**77.8 Light
P13 25— 02 92.6*-------07.4 Light
P14 19 — 08 70.4*-------29.6 Light
P15 12 — 15 44.4 -------55.6 Light
P16 21 — 06 77.8*-------22.2 Light
P17 23 — 04 85.2*-------14.8 Light
P18 18— 09 66.7*--------33.3 Light
P19 22 — 05 81.5*-------18.5 Light
P20 24— 03 88.9*-------11.1 Light
P21 24 — 03 88.9*-------11.1 Electricity
P22 16 — 11 59.3 -------40.7 Electricity
P23 16— 11 59.3 -------40.7 Electricity
P24 21— 06 77.8* -------22.2 Electricity
P25 24 — 03 88.9*-------11.1 Electricity
P26 22— 05 81.5* -----18.5 Electricity
P27 24 — 03 88.9*-------11.1 Electricity
P28 22 — 05 81.5*-------18.5 Electricity
P29 20 — 07 74.1*-------25.9
Electricity
P30 18 — 09 66.7*-------33.3 Electricity
Legend: * Higher degree of conceptual difficulty
** Lesser degree of conceptual difficulty
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Pretest-Posttest Total Score Comparison
The final step in the one-group experimental design involved the
administration o f a posttest to measure the dependent variable again. The effects of
the experimental treatment (e.g., MBL) were determined by comparing the means of
the pretest total score and posttest score.
Table 9 summarizes the results of the pretest-posttest total score comparisons.
The distribution of both scores is normal (skew and kurtosis < 1).
The means o f the pretest (8.48) and posttest (9.25) are statistically significant with a
difference o f 0.77 or 9.1% increase in test scores in the three concepts of
investigation. A 9.1% increase in physical science test scores for students
of mixed-ability, mixed-gender and from low income families in an inner-city
school (with an API o f 2 out of 9) of Los Angeles, California is indicative that the
experimental treatment (e.g., MBL instructions) affected positively student concept
attainment o f physical science concepts.
TABLE 9. Summary of Descriptive Statistics for the Pretest-Posttest
Comparison (N= 149)
Test Total Scores Min-Max Mean Standard
Deviation
Skewness Kurtosis
Pretest 1— 17 8.48 2.75 0.048 0.15
Posttest 1— 17 9.25 3.02 -0.023 0.28
Valid N (listwise)
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Supplemental analysis
Pretest-Posttest Total Score on Level of Differentiated Achievement
Table 10 shows the summary of means between the pretest-posttest scores
on level of cognitive development. The mean in each level is statistically
significant in the pretest-posttest total scores. Also, Table 10 shows an increase in
test scores in the sheltered science group by 0.9%, regular science group by 22.2%,
and accelerated science group by 6.2%.
TABLE 10 Summary of Means: Pretest-Posttest Total Score
* Level of Differentiated Achievement (N=149)
Level of Differentiated Achievement Pretest Total Score Posttest Total Score
Sheltered science Mean 8.96 9.04
Students N 47 47
Std. Deviation 3 26 3.35
Regular science Mean 7.71 9.42
Students N 48 48
Std. Deviation 2.31 3.08
Accelerated science Mean 8.74 9.28
Students N 54 54
Std. Deviation 2.52 2.68
Total Mean
N
Std. Deviation
8.48
149
2.75
9.25
149
3.02
To test the statistical significance of these differences, an ANOVA was
performed on the collected data. Table 11 shows the ANOVA o f the pretest-posttest
on student’ level o f differentiated achievement. The F-test indicates a significant
difference between the sheltered, regular, and accelerated science groups F(149) =
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0.19, sig. = 0.832 at p<.05. Thus, this shows that MBL instructions made a positive
impact on the attainment o f science concepts.
T ABLE 11. Analysis of variance of Pretest-Posttest with Level of
Differentiated Achievement (N = 149)
Sum of
Squares df
Mean
Square F Sig.
Pretest Total Score Between Groups
Within Groups
Total
42.98
1074.20
1117.18
2
146
148
21.483
7.358
2.92* 0.057
Posttest Total Score Between Groups
Within Groups
Total
3.40
1342.42
1345.82.
2
147
148
1.70
9.20
0.19* 0.832
*p<.05
To determine specifically which mean differ significantly, a post-hoc range
test was performed. A Tukey High Significant Difference (HSD) comparison test was
used to provide tight boundaries for significant levels.
Table 12 shows a One-Way ANOVA Post Hoc Range Test on Level of
Differentiated Achievement. The result indicates that the accelerated science group is
significantly higher in their attainment of physics concepts as compared to the
regular science group. The sheltered science group has the lowest attainment of
concepts of all three levels.
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TABLE 12. One-Way ANOVA Post Hoc Range Test On Level of
Differentiated Achievement
Dependent
Variable
Comparison
Test
(I) Level of Differentiated
Achievement
(J) Level of Differentiated
Achievement
Sig.
Posttest
Total Score
Tukey HSD sheltered science regular science
accelerated science
0.819
0.920
regular science sheltered science
accelerated science
0.819
0.971
accelerated science sheltered science
regular science
0.920
0.971
Significance Level= 0.05
Pretest-Posttest Total Score on Treatment Group
The last observation made is between the pretest-posttest total scores and
MBL and traditional laboratory group. Table 13 shows the summary of means of the
pretest-posttest scores on the treatment group. The mean difference between MBL
group (9.78) and traditional laboratory group (8.70) shows that more students
attained the concepts of physical science adequately with MBL instructions in the
MBL group than the traditional laboratory group using deductive approach with
traditional equipment.
TABLE 13. Summary of Means: Pretest-Posttest Total Score *
Treatment Group (N=149)
Treatment Group Pretest Total Score Posttest Total Score
Traditional Mean 8.95 8.70
Group N 73 73
Std. Deviation 3.00 3.13
MBL group Mean 8.03 9.78
N 76 76
Std. Deviation 2.41 2.82
Total Mean 8.48 925
N 149 149
Std. Deviation 2.75 3.02
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To test the statistical significance of this difference, an ANOVA was
performed on the collected data. Table 14 shows the ANOVA of the pretest-posttest
on treatment group. The F-test indicates a significant difference between the
traditional and MBL group F(149) = 4.88, p<.05. This shows that students of the
MBL group obtained a higher degree of concept attainment of physical science
concepts than those students in the traditional laboratory group who used traditional
laboratory equipment.
TABLE 14. One-Way ANOVA for Pretest-Posttest and Treatment Group
(N= 149)
Sum of
Squares df
Mean
Square F Sig.
Pretest Total Score Between Groups
WithinGroups
Total
31.44
1085.73
1117.17
1
147
148
31.44
7.39
4.26* 0.041
Posttest Total Score Between Groups
Within Groups
Total
43.25
1,302.58
1,345.83
1
147
148
43.25
8.86
4.88* 0.029
*p<0.05
Summary of Results for Research Question 1
From the data of the written documents (e.g., pretest and posttest total
score) and procedures of a one-group pretest-posttest design, it was found out that
there is a positive impact of the use o f microcomputer-based laboratory activities in
the physical sciences on concept attainment among middle school students of
varying levels of cognitive development. Likewise, the impact of the MBL
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activities shows a significant difference between the traditional and the
experimental group. Overall, students’ concept attainment in thermodynamics,
electricity, and light was enhanced by the introduction of an instructional program
using MBL instruction. Thus, it can be fairly concluded that the integration of MBL
instruction and Concept Attainment Model o f teaching possess a great potential to
enhance the teaching and learning of science concepts in a technology-enhanced
science laboratory (TESL).
Second research question
Research Question 2; What is the impact of the use of microcomputer-based
laboratory activities in the physical sciences on attitude towards computer-based
technologies among students of varying levels o f cognitive development?
A computer-attitude survey (Delcourt & Kinzie, 1993) was adopted to
measure student attitudes towards the use o f computer technology. Delcourt &
Kinzie used data reduction by using clusters to reveal three factor structures
and display factor loadings of 19-variables in the computer-attitude survey. The
first factor structure is loaded as Factor I: comfort/anxiety, the second factor
structure as Factor II: usefiilness-positively phrased, and the third factor structure as
Factor HI: usefulness-negatively phrased. The factor loadings (e.g., correlation
coefficients) were displayed in every variable o f Factors I, n , and in (Delcourt &
Kinzie, 1993). This student-computer-attitude survey was modified to make it more
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applicable to this study. Finally, this survey is used for the Primary Analysis of the
Second Research Question and an ANOVA Post Hoc Test as Supplemental Analyses.
Primary analyses
Modified Student-Computer-Attitude Survey
The original student-computer-attitude survey was modified with slight
changes in wordings to make it more applicable to my investigation. (See Appendix
Thirteen). The modified-student computer attitude survey (m-SCAS) contains a
total of 19 items that includes eight Factor I (positively and negatively phrased)
items that measure perceived comfort and anxiety (e.g., “I feel at ease learning
about computer technologies.”) and 11 items consisting of Factor n (positively
phrased) items (e.g., “With the use of computer technologies, I can create science
reports to enhance my studies.”) and Factor III (negatively phrased) items (e.g., “I
don’t see how computer technologies will help me learn new skills”) that measure
perceived usefulness.
The participants were asked to convey their degree o f agreement or
disagreement with the statements found in the m-SCAS. They were told that the
survey was evaluating their personal beliefs in relation to computer technology after
performing a hands-on experience on MBL tools and activities in the three concepts of
investigation. They were also told that there are no right or wrong answer in the
survey. The students’ responses to the 19-question m-SCAS (N = 149) were
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subjected to a Principal Component Analysis (PCA) as a method of extraction (SPSS
10 for Windows, 1999).
Table IS shows the factor loadings obtained for each factor structure resulting
from the Varimax rotation. Factor I (mean = 0.5250, SD = 0.1375) contained eight
items reflecting “Comfort/Anxiety” in relation to computer technologies. The result
shows that high scores on the scale indicate that the participants feel comfortable and
confident about the prospect of using computer technologies. Similarly, the
underlined item numbers (e.g., negatively phrased) shows a medium score on the
scale that indicate feeling of anxiety towards computer technology among the
participants.
Moreover, the 11 items showing perceived “Usefulness” of computer
technologies were loaded as Factor II (mean =.6960, SD =.09) and Factor III (mean
= 0.4850, SD = 0.1946). The factor loadings show that participants with high scores
on this set o f items view computer technologies as worthy implements for doing
diverse school activities that enable them to be “productive” and “effective”
students. The 19-item m-SCAS instrument identified three empirical factors that
explained 62.7% of the total variance. This high value shows that the m-SCAS is
sufficient for measuring attitude towards computers. Furthermore, the high positive
correlation displayed in Table 15 between each factor provides evidence for
retaining a three-factor instrument (Delcourt & Kinzie, 1993) instead of a two-
factor instrument as suggested by Melnick & Gable (1990) and Escalada & Zollman
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(1997) to write only positively phrased items to measure attitude in computer
technologies.
TABLE 15. Principal Component Analysis: SPSS Varimax Rotation for Modified
Student Computer Attitude Survey (N= 149)
Item No.______________ Stem_____________________________ Factor I Factor 2 Factor 3
Factor I: Comfort/Anxiety
2
I feel at ease learning about computer technologies 0.74
1 I am confident about my ability to do well in a subject
that requires me to use computer technology 0.66
_ 4 The thought of using computer technologies
frightens me. 0.53
5 Computer technologies are confusing to me. 0.51
_7 I am anxious about computers because 1 feel
like I might break them 0.51
_ 3 I am not the type to do well with computer
technologies. 0.49
8 I feel comfortable about my ability to work
with computer technologies. 0.49
6 I do not feel threatened by the impact of
computer technologies. 0.27
Factor D : Usefulness (positively phrased, specific content)
13 Computer technologies can be used to assist me
with my discipline or behavior. 0.79
10 With the use of computer technologies I can
create science reports to enhance my studies. 0.75
1 1 If I can use word processing software, I will
be a more productive student. 0.73
12 I could use computer technologies to access
many types of information sources (CD ROM) for my work. 0.63
9 Communicating with others over a computer
network (Internet) can help me to be a more effective student. 0.58
Factor H I: Usefulness (negatively phrased, general content)
19 Knowing how to use computer technologies
will not be helpful in my future studies. 0.78
18 1 don't see how computer technologies will
help me learn new skills. 0.59
14 I don’t have any use for computer technologies
on a day-to-day basis. 0.45
17 Anything that computer technologies can be
used for, I can do just as well in some other way. 0.42
16 I do not think that computer technologies will
be useful to me as a student 0.40
15 Using computer technologies in my studies
will only mean more work For me. 0.24
* Underlined item numbers reflect negative phrased stems.
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Supplemental Analyses
Post Hoc Ranee Tests on Level of Cognitive Development
In order to further show the impact of microcomputer-based laboratory on
attitudes towards computer-based technologies among students o f varying levels of
differentiated achievement, a One-Way ANOVA Post Hoc Range Test was performed.
The purpose of the Post Hoc Range Test was to identify homogeneous subsets of
means (e.g., individual question of m-SCAS) that are similar from each other (alpha =
0.05).
Table 16 shows the multiple comparison (post hoc test) of the dependent
variable (e.g., survey question) and the independent variable (e.g., level of
differentiated achievement). Column 5 shows the significance of each individual
survey question and the significance using Tukey High Significant Difference
between each level of differentiated achievement.. For example, the significance
(alpha = 0.05) of Question 2 is 0.662 (homogeneous subset) while the significance
of the multiple comparison (post hoc test) o f sheltered science to regular science is
0.673, regular to accelerated science is 0.736, and accelerated science to sheltered
science is 0.991. The significance from the homogenous subset (e.g., 0.662 )
is the representative value for Question Number 2. This value is displayed with an
asterisk found in the fifth column.
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TABLE 16. One-Way ANOVA Post Hoc Test of Attitude and Level of Differentiated
Achievement
Level of
Differentiated
Achievement
Sig. (alpha =.05)*
N Mean S.D. Tukey
HSD*b
Sig.
(W)
Level of
Differentiated
Achievement
Sig. (alpha =
.05)*
N Mean S.D Tukey
HSD**
Sig.
(I-J)
1 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.978
2.979
2.870
0.87
0.97
1.01
1.00
0.835
0.838
0.839
10 sheltered sci
regular sci
accelerated sci
Sig*
47
48
54
2.38
2.75
2.55
0.73
0.88
0.86
0.193
0.679
0.609
0.180
2 sheltered sci
regular sci
accelerated sci
Sin.*
47
48
54
2.319
2.167
2.296
0.81
0.95
0.86
0.673
0.736
0.991
0.662
11 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
3.34
3.04
3.21
0.97
1.06
0.87
0.240
0.894
0.444
0.226
3 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
1.978
2.063
2.241
0.68
0.91
0.75
0.861
0.485
0.214
0.220
12 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.94
2.92
2.74
0.79
1.67
0.94
0.996
0.689
0.737
0.693
4 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.872
3.063
2.870
0.97
0.84
0.99
0.584
0.555
1.00
0.564
13 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
1.95
2.04
1.93
0.88
0.94
0.84
0.766
0.789
0.998
0.758
5 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
3.085
3.104
3.148
0.80
0.90
0.96
0.994
0.967
0.934
0.935
14 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.45
2.58
2.18
0.90
1.01
1.06
0.783
0.387
0.110
0.166
6 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.234
2.125
1.889
0.63
0.84
0.77
0.761
0.057
0.255
0.059
15 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.85
2.81
3.07
0.91
1.02
0.95
0.979
0.476
0.356
0.366
7 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
1.915
2.146
1.901
0.75
0.95
0.92
0.404
0.999
0.356
0.366
16 sheltered sci
regular sci
accelerated sci
Sig*
47
48
54
2.11
2.44
2.07
0.84
1.05
0.99
0.216
0.985
0.139
0.147
8 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.851
2.667
2.926
0.81
0.99
0.91
0.583
0.910
0.310
0.330
17 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
3.06
2.63
2.87
0.93
1.10
1.13
0.088
0.569
0.475
0.080
9 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.170
2.229
2.181
0.73
0.88
0.86
0.936
0.990
0.874
0.877
18 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.02
1.91
1.93
0.89
0.87
0.93
0.838
0.856
0.999
0.832
19 sheltered sci
regular sci
accelerated sci
Sig.*
47
48
54
2.19
2.33
2.52
0.80
0.93
1.19
0.768
0.228
0.618
0.233
Legend: Tukey HSD*-b
a. Uses Harmonic Mean Sample Size = 49.482
b. The group sizes are unequal. The harmonic mean of the
group sizes is used. Type I error levels are not guaranteed.
Significance Level: alpha .05
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Finally, from the information of Table 20, the individual result of every survey
question showed a significant difference in student’s attitude towards computer-based
technology among students of varying level of differentiated achievement..
Summary of Results for Research Question 2
From the data gathered from the primary and supplemental analyses of the
modified student-computer attitude survey, it was found out that there is a favorable
influence o f microcomputer-based laboratory tools and activities on attitudes
among students of varying levels of differentiated achievement. Student’s feeling of
comfort (74%) had a higher correlation than anxiety among sheltered, regular, and
accelerated science students. Likewise, the higher correlation (79%) on the
usefulness o f computers among the three groups of science students viewed computer
technologies as worthy implements for doing diverse school activities to enable
them to be “productive” and “effective” students. Eventually, students’ perceptions
toward computer technology such as MBL instruction have raised the potential to
enhance the quantity and quality of science education in the schools of the District.
The MBL tools and activities have great potential, but successful realization depends,
always, upon the teacher.
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Summary of The Description of Results
The procedures o f a one-group pretest-posttest design used in manipulating a
single treatment namely microcomputer-based laboratory (MBL) instruction, and
observing its effects on pretest-posttest total scores, and pretest-posttest total scores
on level of cognitive development and treatment group impacted an instructional
program that integrated ideas about teaching thinking skills by using computers.
Microcomputer-based laboratory (MBL) was used to develop an instructional
program so that middle school students could organize and explore concepts based on
the concept attainment model. The model helped students learn to determine the
characteristics of a category of thermodynamics, light, and electricity..
Finally, based on the results of the statistical procedures in this study, it can
be concluded that the integration of MBL instructions and the use o f the concept
attainment model of teaching, possess great potential in advancing the teaching and
learning of physical science concepts (e.g., knowledge content) and thinking skills
(e.g., cognitive skills) while promoting positive attitudes (e.g., comfort &
usefulness) toward computer technologies (e.g., MBL) among students of varying
level of differentiated achievement in the schools of the second largest Unified School
District in the country.
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CHAPTER 5
SUMMARY, DISCUSSION, AND RECOMMENDATIONS
Summary
Background
Using the work of Jean Piaget as a foundation, science instructional experts
have formulated a number of technologies of instruction to explain cognitive learning.
These science instructional experts, sometimes called cognitive scientists, are
intrigued with how information is processed and retained. They agree with Piaget that
knowledge is constructed and theorize that learners are builders of knowledge
structures. Also, these cognitive scientists have developed a number of models of
teaching that have direct implication for teaching science to middle school students.
Models of teaching vary in some respects but they share general attributes
such as a) the importance of content knowledge and b) the integration of skills
and content. With regard to content knowledge, cognitive scientists have placed
much emphasis on expert knowledge. Learning requires knowledge, yet knowledge
cannot be given directly. Hence, students must construct their own knowledge but
the teacher must provide a learning environment where students can investigate
new information and build new knowledge structures (Hassard, 1994; DeBoer,
1991; Lemlech, 1998).
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The integration of skills and content is central to cognitive learning. It
places the student in the center of learning, therefore the development of creative
thinking skills must be integrated with content knowledge. This is an idea of
sequencing instructions as proposed by Piaget and Bruner. Models o f teaching built
around the studies of thinking conducted by Bruner (Bruner et. al., 1967) provide the
learner with information and concepts, some emphasize concept formation, and still
others generate creative thinking. The ability to analyze information and create
concepts is generally regarded as the fundamental thinking skill (Joyce & Weil, 1996).
The integration of skills and content in science learning and teaching is
illustrated in this research study. The purpose of this study was to design an
instructional program that will integrate ideas about thinking skills to create
concepts by using microcomputers. Microcomputer-based laboratory (MBL) was
used to develop an instructional program so that middle school students from varying
levels of differentiated achievement could categorize and create concepts grounded
on the Concept Attainment Model. (See Table 3). Likewise, student attitude towards
the use of computer technology (e.g., MBL) was investigated using a modified-
student computer attitude survey (m-SCAS). (See Appendix Sixteen).
This study was conducted in the Spring semester of the 1999-2000
academic year using a sample of (N= 172) composed of typical grade eight
students of mixed-gender, mixed-ability and coming from low-income families
in an inner-city school of Los Angeles, California. The school has a diverse
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population (mostly Hispanics, Asians, Armenians, and African-American) of 70%
English Language Learners (ELL) and with an API o f 2 out of 9 among other
schools in the District. The classification of students to the mainstream is based on the
results o f the yearly Stanford 9 Test, Aprenda 2 Test (Spanish only), and language
classification (e.g., Home Language Assessment). The researcher obtained a high rate
of participation (87%). Twenty three students were unable to participate in the
posttest.
Research Findings
1. Analysis of the data collected in this experimental study revealed that
microcomputer-based laboratory activities in the physical sciences had a
positive impact on concept attainment among middle school students of
varying levels of differentiated achievement and treatment group. The positive
impact of the experimental treatment (e.g., MBL) is verified by the following
results.
First, the experimental treatment revealed a modest 9.1% increase in
physical science test scores among middle school student of mixed ability,
mixed gender, and from low income families in an urban inner-city school
o f Los Angeles, California. Second, the experimental treatment (e.g., MBL)
revealed an increase in test scores in each level o f cognitive development.
The following increases in test scores are disclosed: sheltered science group
1%, regular science group 22.2%, and accelerated science group 6.2%.
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Third, the experimental treatment (e.g., MBL) showed a mean difference
between the MBL group (mean = 9.78) and traditional laboratory group
(mean = 8.70) of 11.1% in favor of the MBL group.
From the appreciable increases in test scores, level of differentiated
achievement, and treatment group, it can be concluded that the
participant’s newly acquired instructional program enhanced their concept
attainment strategies in the three concepts of investigation. It
can be concluded further that the significant effects between MBL
instructions and concept attainment among students of varying levels of
differentiated achievement and the treatment group were more likely
attributed only to the administration of the experimental treatment rather by
chance in the active process of learning physical science concepts of
thermodynamics, light, and electricity.
2. The results o f the Principal Component Analysis o f students’ responses in
the modified-student computer attitude survey (m-SCAS), revealed that the
use of microcomputer-based laboratory activities in the physical sciences
had a positive impact on attitude toward computer-based technologies
among students o f varying levels of cognitive development. The impact is
shown by high factor scores (e.g., correlations) on the scale. (See Appendix
Sixteen).
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For example, in Factor I (Comfort/Anxiety), participants displayed a
significant positive correlation of 74% feeling o f comfort in the prospect of
using computer technologies. Likewise, the participants showed a
significant positive correlation of 66% confidence in their abilities to do well
in subjects that require the use of computer technologies. In addition, the
participants showed a significant positive correlation o f 79% usefulness
(positively phrased) that computer technologies can be used to assist with
discipline or behavior among students of varying level o f differentiated
achievement.
However, the loadings of Factor III (Usefulness—negatively phrased)
indicated an extraneous correlation of 78% usefulness (negatively phrased)
that computer technologies will not be helpful in students future studies.
Also, a correlation of 59% usefulness (negatively phrased) affirmed that the
participants did not see how computer technologies will help them learn new
skills. The average subtest measure (43%) o f Factor III (Usefulness—
negatively phrased, general content) did not correlate with the average
subtest measure (77%) of Factor II (Usefulness—positively phrased,
specific content). Instead, the subtest measure is heavily loaded on
Factor II (r = 0.79 and 0.75 with an average o f 77%). These loadings
confirmed that participants with high factor scores on this set o f items view
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computer technologies like MBL as worthy implements for doing diverse
school activities.
3. A One-way ANOVA Post Hoc Range Comparison Test established that the
total variance for MBL instruction with level of differentiated achievement,
was 83.2%. That is, the F ratio showed a significance of 83.2% o f the posttest
total score. Likewise, the results of the post hoc test using Tukey HSD,
revealed the following significance: accelerated-sheltered science group 92%,
accelerated-regular science group 97.1%, and regular sheltered group
97.1%. (See Table 15). This high significance attests that MBL instructions
impacted positively sheltered science students, regular science students, and
accelerated science students ofmixed-ability, mixed gender, and from an
urban inner-city school in attaining physical science concepts. It can be
concluded that MBL instruction has a great potential to enhance higher
thinking skills through the use of the Concept Attainment Model of teaching
among middle school students.
4. A Principal Component Analysis (PCA) of the responses in the m-SCAS
loaded three empirical factors, Factor I (comfort/anxiety), Factor Q
(usefulness—positively phrased), and Factor HI (usefulness—negatively
phrased), and extracted 62.7% of total variance (common variance plus error
variance). The value of 62.7% indicated a moderate extraction (average =
70%) o f variance. That is, the factors used in the PCA were derived and
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correlated with each other resulting to an oblique solution (viz., useful and
meaningful) in the correlation matrix (Pedhazur St Schmelkin, 1991; Gall et.
al., 1996).
Discussion and Implications
The effect of the one-group experimental design
This study employed a classic one-variable experiment design which involved
the manipulation of a single variable or experimental treatment (e.g., MBL
instruction). This was followed by the observation of the effects o f this manipulation
on the pretest total score and posttest total score, and followed by a pretest-posttest
total score on level of differentiated achievement and treatment group. Suitable
control o f extraneous variables common to a one-group design was observed so that
any change in the posttest can be attributed only to the experimental treatment
manipulated by the researcher.
To prevent changes in results on the posttest, the following controls were
executed: the selection o f a convenient sample, use of a uniform classroom
environment, adoption o f reliable written documents (pretest/posttest), adopting
a six-week classroom instructional timeline used in the implementation of the
experimental treatment. The control of these extraneous variables were adopted
to minimize the threat to internal validity like time, maturation, testing,
instrumentation, and interaction of selection to other factors such as compensatory
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rivalry and resentful demoralization of the control group and external validity such as
interaction of selection with MBL treatment and interaction of testing with MBL)
common to a one-variable experimental design.
Furthermore, the statistical results used in this study showed a positive
effect o f MBL instruction on concept attainment among students of varying levels of
differentiated achievement and the treatment group to which they were assigned.
Also, students’ attitude towards computer technology indicated feelings of
comfort/confidence and usefulness of computers in school activities. From this
premise, the procedures o f the classic one-variable experiment design used in this
study was appropriate and systematic. The positive results supported an appropriate
curriculum (e.g., instructional program) that have a direct impact in the integration of
the Concept Attainment Model and microcomputer-based laboratory.
The outcome of pretest total scores
The 30-item multiple choice question pretest was subjected to a reliability
analysis that revealed an alpha o f0.7849 and standard item alpha coefficient of
0.8028. These weighty alpha values for the pretest indicated that the variance of the
scale was systematic. However, some participants incurred low test scores in the
pretest which were likely due to guessing by the participants and the degree of
difficulty of the thematic areas in the three concepts o f investigation being introduced.
Also, these low test scores are corroborated by the low API of 2 out 9 o f the school
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where 70% of the students are English Language Learners (ELL) from low income
families in an urban inner-city school environment.
Identifying Degree of Conceptual D i f f i c u l t y
The pretest was used to identify the degree o f conceptual difficulty in each
question of the pretest. The participants exhibited appreciable degree of conceptual
difficulty in the three concepts o f investigation: thermodynamics (60%), light (70%),
and electricity (70%). An average of 66.7% indicated a substantial degree of
conceptual difficulty in learning the three physics concepts.
The outcome of the pretest-posttest comparison
The results of the summary of means and one-way ANOVA of the pretest
and posttest revealed significant differences in test scores. These results indicated
that a positive response was exhibited by the students after their exposure to MBL
tools and activities while exploring the three concepts of investigation. The modest
increase of 9.1% in test scores for students of mixed-ability, mixed-gender, and from
low income families in an urban inner-city school, attested to the positive impact
of MBL in student concept attainment. Therefore, it can be concluded that the
positive impact o f MBL instruction fostered modest academic gains and enhanced
critical thinking skills among students of varying levels of differentiated achievement.
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The Effect on the Level of Differentiated Achievement
A one-way ANOVA of the pretest-posttest total scores with level of
differentiated achievement indicated an F ratio of 0.19 and a significance o f0.832 at
(p = 0.05). Likewise, to determine specifically which means differ significantly, a post
hoc range test using Tukey HSD as a comparison technique was administered. The
results of the Tukey HSD comparison test revealed the following significance: in
the accelerated-sheltered science group 92%, accelerated-regular science group
97.1%, and regular-sheltered science group 82 %.
These figures confirmed that students with varying levels o f differentiated
achievement acquired different degrees of attaining concepts through the use of MBL
instruction. Hence, the positive impact of microcomputer based-laboratory
instructions in effecting concept attainment signals a promising instructional
program to enhance student gains in the active process of learning. Finally, the
positive effects are fairly and confidently attributable solely to the experimental
treatment and not by chance.
The Effect on the Treatment Group
Participants in the MBL group or exploratory group showed a significant
increase in test scores compared to the students in the traditional lab group (TRL).
This increase was revealed in the summary of means for the pretest-posttest and
treatment group. The MBL group showed a 22% increase in test scores compared to
the traditional group. Likewise, a one-way ANOVA of the pretest-posttest total test
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scores with the treatment group revealed an F ratio of 4.26 (significance = .041) and
4.88 (significance = .029) respectively between groups.
The appreciable increases in test scores, means, and F ratios attested that
students in the MBL group experienced substantial concept attainment in the active
process of learning physical science concepts. Therefore, the purpose of this study, to
illustrate the integration of content and skills in science learning and teaching, is firmly
authenticated.
The effect of concept attainment model
The use of the Concept Attainment Model in this study paved the way to
teach the participants appreciable content in thermodynamics, electricity, and light
and at the same time training them specific thinking skills o f observing,
hypothesizing and hypothesis tests, and engaging them in metacognition by using
microcomputer-based laboratory instruction. Metacognition means the ability to
reflect on one’s own cognitive processes or going beyond knowing.
Moreover, as students begin to attain each concept, they tested their
understanding by testing the concept deductively by using the general hypothesis of
the concept to determine which new set of exemplars do and do not “belong” to it.
Furthermore, the use o f a three-phase instruction plan presented the instructors
with very real challenges. They needed to select significant and appropriate
concepts and develope their positive and negative exemplars. Also, they created
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case study-guides in which students can apply the concepts they learn and guide
them through careful observation, analysis, and working.
Finally, the instructors found ways to help students enhance about their
thinking skills.. Thus, the use of Concept Attainment Model helped students become
consciously aware of the their thinking skills as they apply and integrate them to
derive the concept being studied.
The results of the modified-student computer attitude survey
The 19-item modified-student computer attitude survey (m-SCAS)
instrument was administered to 149 students. Responses to the instrument were
subjected to a Principal Component Analysis (PCA) to identify factor structures and
factor loadings and to determine its total variance. The instrument identified three
empirical factors which explained 62.7% of the variance. Factor I (Comfort/Anxiety)
showed that participants with high scores on the scale feel comfortable about the
prospect of using computer technologies. Factor II (Usefiilness-positively phrased)
and Factor m (Usefulness, negatively phrased) showed that the participants with
high scores on this set o f items view computer technologies as worthy implements
for doing diverse school activities such as using probes or sensors in analyzing the
relationship of physical quantities.
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Recommendations for Further Research
The empirical findings of this study on the impact o f MBL instructions on
concept attainment and attitudes toward computer technologies among students of
varying level of differentiated achievement come down to various recommendations
for further research.
The first recommendation is the allotment of ample funds and time for
depth exploration o f MBL instructions in a technology-enhanced science laboratory
or technology-based learning environment in the schools o f the District. The
common denominator in school reform is money. The use of state-of-the-art
instructional technology integrated with information-processing models of teaching,
may enhance the goal o f innovative science education in the 21st century.
Second, the collaboration between classroom teachers, science curriculum
developers, MBL developers, and science education experts and researchers in MBL
teaching and learning must be cemented. The continuous interaction of these
science instructional experts may lead to better instructional design and approach in
using microcomputer-based laboratory tools and activities as an agent of change to
improve academic achievement among diverse and multiethnic students.
Third, a replication study in the integration of MBL instruction to other
Information-Processing Models of Teaching such as Mnemonics (Michael
Pressley), Advance Organizers (David AusubeF), Scientific Inquiry (Joseph
Schwab), Inquiry Training (Richard Suchman), and Synectics (Bill Gordon) should
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be undertaken. Knowing the implication of these models of teaching to MBL
instruction may result to novel instructional programs in cognitive learning among
middle, junior, and high school students from underperforming schools (e.g., API of
less than 2 out of 9) of the Los Angeles Unified School District.
Fourth, continue to test the possible predictive relationship o f attitudes,
values, self-efficacy, concepts, intellectual development, skills, information and
relevant outcome measures to include adoption, use, and modeling o f MBL in a
technology-enhanced learning environment (TELE).
Conclusion
Teaching and learning with computer technology has a history of about 35-
40 years. Since about 1960, digital computers have been used to support teaching
and learning in several ways, initially as a subject content, as a means to enhance
learning, as a support tool, as a vehicle fo r multimedia delivery and finally as an
asynchronous communication link between teacher and student (Gillespie, 1998).
Through most of its history, the impact of instructional technology on
instruction has not been very significant. Until just very recently the computer has
been used mainly as an aid to faculty productivity and to support or enhance normal
teaching activities. There have been very few examples were computer have really
changed what is actually taught and how we teach. The promises o f thirty years ago
have not been realized, but progress has been made in the instructional design and
147
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system development of innovative digital tools to support teaching and learning of
physical science in middle, senior, and post-secondary schools around the nation.
Microcomputer-based laboratory is one o f the these promising and novel tools
in instructional technology to support teaching and learning. However, as
mentioned earlier, many teachers have exhibited fear or abandoned the use of
instructional technology because it will create angst among students with little
computer backgrounds, thus, could cause a diminishing effect in the learning
process. This study negates that perception. The use o f a cutting-edge and user-
friendly technology like MBL could enhance student scientific and creative thinking
skills in the active process of learning science concepts. Hence, it is possible
that instructional technology in the form of MBL technology can have a meaningful
influence on pre-service and in-service teachers if they adopt or model MBL
instruction to their students.
Eventually, the potential of MBL technology in teaching and learning
science will continue to access and share scientific information. If this happens,
productivity tools such as word processing, spreadsheets, graphics packages,
database management, multimedia, telecommunication, and other novel forms
of instructional technology will be the norm. Then, instructional technology in
the next decade will support both synchronous and asynchronous interaction
between learner and the sources of knowledge and information. Also, it will resort
148
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to the development o f a variety of teaching strategies and increase connectivity
between teachers, student, student and the teacher, other students, the classroom, the
library, and the world.
In the interim, the effect of the procedures of a one-variable experimental
design and the significant results of the different statistical techniques, firmly
corroborated the purpose of this research study to construct an instructional program
that integrated ideas about thinking skills (observation, hypothesis testing,
visualization, categorizing, pattern recognition, etc.) and content knowledge
(thermodynamics, light, electricity). Irrevocably, a more appropriate assessment of
this innovative instructional program used in the integration o f skills and content
knowledge can offer a far-reaching avenue to catalyze metacognition among children
of varying grades and achievement level or among children with exceptional needs in
the schools of the Los Angeles Unified School District, the second largest District in
the country.
149
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APPENDICES
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APPENDIX ONE
INFORMED CONSENT FORM
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use
RQSS1ER
SCHOOL OF
EDUCATION
Inform ed Consent Form
August 1999
Dear Grade Eight Student:
This is to inform you that you have been selected to participate in a research
study that I am conducting towards my doctoral studies at USC.
This experimental research study is about the impact of microcomputer-
based laboratory (MBL) on student concept attainment and attitude towards
computer-based technologies. You will be taught how to use MBL tools and
activities in studying the concepts of thermodynamics, light, and electricity.
This training will not affect your grade but will give you the chance to
obtain a hands-on experience about MBL, a cutting-edge instructional
technology, and improve your science process skills. Finally, you will fill a
survey about your perception towards the use of computer or instructional
technologies such as MBL.
If you have any question regarding this study, you may call me at (818) 897
4542 or my advisor, Dr. William F. McComas at (213) 740-3470. If you
have any question about your rights as research participants, you can call
the Program Evaluation and Research Branch, Los Angeles Unified School
District, (310) 838-6449.
I am hoping that you will be part of this research study. Your input will be
very significant in making recommendations about teaching and learning of
science in the secondary schools of our District.
Sincerely,
SERGIO ALBINA OSIO
Name_______________________________________________
Parent/s signature______________________________________
Date;_________________________________________________
laivtriily of
Soathcra Califoraia
Loa Aafclea, CA, NH 1
Tel: (213)74* till
168
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APPENDIX TWO
LETTER FROM THE RESEARCH
AND EVALUATION BOARD: LAUSD
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LOS ANGELES U N IFIED SCHOOL DISTRICT
Program Evaluation and Research Branch
8810 Emerson Avenue Los Angeles, C A 90045
December 10, 1988
Mr. S. A. Osio
King Middle School
4201 Fountain Avenue
Los Angeles, CA 90029
Dear Mr. Osio:
We received your request for information regarding number o f schools in our District that
utilize computer-based laboratory system. We have enclosed the necessary materials for
you to request and collect data within the District. The District Policy requires that all
researchers fill out an application to obtain permission for data collection within the
District. Upon receiving your application, the research committee will review your
application.
For research in a single school, submit the proposal to the school's principal for review and
possible approval. No committee review or approval is required. If the principal wants an
opinion on the proposal from the Research Committee, a brief consultation is availabl. Call
the Chair, Committee on Research Studies.
Thank you for your interest in learning more about our students.
Sincerely,
William William Renfroe U
Chair, Research Committee
170
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APPENDIX THREE
LETTER FROM ADMINISTRATOR
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Los A sgcks Unified School District RAMON CORTINES
Interim Superintendent o f Schools
Thomas Starr King Middle School
4201 Fooataio Aveaae, Los Aafelcs, California 90029
Telephone: (323) 604 -1176
DR. THELMA YOSHI1
Pri nci pal
July 10, 1999
Certification
TO WHOM IT MAY CONCERN:
This is to certify that Mr. Sergio A. Osio is a science teacher at this school.
Mr. Osio is conducting a research study as part of his doctoral dissertation at
USC on the impact of Microcomputer Based-Laboratory (MBL) on student
concept attainment and attitudes towards the use of computer technologies.
He will be introducing MBL instructions to his students and will administer
a pretest, posttest, and a survey on student computer attitudes towards the
use of computer technologies.
His research is in accordance with the Guidelines for Researchers, LAUSD.
Since his research is only at one school of the District, no committee review
approval is required as per Article 6 of the Research Not Subject to These
Guidelines, Program Evaluation Branch, LAUSD.
Therefore, approval is granted to conduct his research study in our school
provided he adheres to all the regulations of the above Guidelines for
Researchers.
DORO' REENE
Assistant Principal
172
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APPENDIX FOUR
PRETEST FOR THERMODYNAMICS
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Thermodynamics
This test about the concept o f thermodynamics is given to assess your past
knowledge o f heat loss and heat gain during heat transfer when hot and cold water are
mixed.
While a pan of ice is heated, the temperature is measured every 30 seconds.
The graph at the left shows the results. Use the graph to answ er questions
1 through 5.
HOT
WATER
110
100
9 0
-10
- 10,
Tim e (minutes)
PAN O F ICE BEING HEATED
1. W hat process is taking place
between point A and point B?
a) m elting c) vaporization
b) boiling d) radiation
2. W hat explains why the graph
levels o ff at point A?
a) heat o f fusion c) radiant heat
b) heat o f d) specific heat
vaporization
3. W hat process is taking place between point C and point D?
a) melting c) convection
b) boiling d) conduction
4. W hat explains w hy the graph levels off at point C?
a) heat of fusion c) radiant heat
b) heat of vaporization d) specific heat
5. W hat is the state o f m atter o f the t o O at point D?
a) freezing point c) melting point
b) critical point d) boiling point
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Thermodynamics- Page 2
Use the c h a rt on the left to answ er questions 6 and 7.
6 . W hich substance has the highest
specific heat?
a) w ater c) air
b) glass d) iron
7. W hich substance will require the least
amount o f heat to raise its temperature 1 °C?
a) mercury c) iron
b) wood d) glass
Select the ap p ro p ria te scientific term described in questions 8 to 10.
8 . The total o f all kinetic energy o f all particles in a substance is
a) thermal energy b) heat c) temperature d. thermal equilibrium
9. W hen substances o f different temperatures are mixed, the hotter one heats the
cooler one until the temperature of the mixture reaches a balance called
a) change in b) thermal c) change in. d) heat transfer
P.E . equilibrium K .E.
10. The final temperature of a mixture o f cold and hot water compare to the lowest
temperture o f the 1 0 0 ml o f cold water and the highest temperature of 1 0 0 ml of
hot w ater will be
a) slightly lesser than the lowest temperature o f the cold water
b) slightly lesser than the highest temperature o f the hot water
c) midway between the high temperature o f the hot water and the low
temperature o f the cold water
d) will cancel to zero.
Specific H e a t(c a l/g x ° C )
iron 0 . 1 1
glass 0 . 2 0
wood 0.4
water 1 .0 0
m ercury 0.33
air 0.25
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APPENDIX FIVE
PRETEST FOR ELECTRICITY
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Electricity
Part I. The diagram below shows a simple construction o f an electrochemical
cell (e.g., we cell). It is composed to two different metallic electrodes o f copper
(Cu) and magnesium (M g). They are connected to two alligator clips, one
black and one red. These electrodes are submerged in a dilute solution o f
sulfuric acid, H2 SO 4 . A 1.5 volt light bulb is connected in series which glows
when current passes through it.
Use the diagram below to answer questions 1 to 5
Bulb Bulb
Cu “ Mg Cu
dillu te H2 SO 4
1.The bubbles around the Mg electrode indicates that
a) Cu supplies electrons to Mg
b) Mg supplies electrons to Cu
c) the bulb glows brighter
d) the bulb glows dimmer
2. The Mg electrode serves as the
a) positive terminal b) negative terminal
c) resistor d) capacitor
3. The Cu electrode serves as the
a) positive terminal b) negative terminal
c) resistor d) capacitor
4. W hen current is flowing, circuit is
a) open b) closed
c) short d) live
5. A wire gets hot in the circuit because of its
a) voltage b) resistance
c) current d) power
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Electricity- page 2
II. The diagram below is sim ilar to the w et cell above but this lime a fruit (e.g., fruit
cell) is used as the source of energy. The energy found in fruits can be converted into
electrical energy. Instead o f a bulb it is connected into an interface that is further
connected to computer to produce the necessary graphs. Use this diagram to answer
questions 6 - 1 0 .
6. When two different kinds o f
metal electrodes are used and
placed in a fruit, it will produce
a) current c) resistance
b) voltage d) power
7. If these two electrodes are
placed closer, the voltage will
a) go up
b) go down
c) remain the same
d) cause fire
8 . If a pineapple is used instead
o f an apple, w hat will increase
significantly
a) resistance
b) voltage
c) current
d) power
9. Instead o f an interface, a 1.5 light bulb is used in series with the electrodes
embedded inside a lemon fruit. W hat will happen to the bulb?
a) blow light filam ent c) glow brighter
b) no effect d) blow the glass
10. The flow o f current in fruit juices is possible because o f its acidic properties.
In this instance these juices act as an/a
a) catalyst c. cation
b) electrolyte d) anion
Galvanized Nail Cep per Wire
B A TTER Y
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APPENDIX SIX
PRETEST FOR INTENSITY OF LIGHT
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Intensity of Light
I. This test about the concept of L ight is given to assess your knowledge of the
variation in intensity o f light from different light sources.
. Use the diagram (e.g. white light of the visible spectrum) on the left to answer
questions 1 to 5. The diagram represents light from the sun to form a rainbow.
1. W hat color is represented by the letter A?
a) red c) violet
b) orange d) blue
2. W hat color is represented by the
letter C.
a) yellow c) blue
b) indigo d) green
3. W hat letter represents the color with the longest wavelength?
a) A b) D c) G d) C
4. W hat letter represents the color with the shortest wavelength?
a) G b) C c) A d) D
3. W hat process creates a rainbow?
a) refraction b) reflection c) polarization d) diffraction
II. The amount o f light em itted from a bulb o r any source of
light is measured in units o f lumens. For example, a 100
watt-lamp emits about 1750 lumens.Use the diagram
to answer questons 6 to 7.
6 . The measure of how much light is striking a surface
per unit area is called
a) illuminance c) polarization
b) luminization d) refraction
7. If the 100-watt bulb is placed in the center o f two transparent spheres, assuming
no light absorption, at what distance from the bulb will seem to our eyes
the brightest?
a) 1 radius from the center
b) 2 radii from the center
180
c) 4 radii from the center
d) 8 radii from the center
Q
L00-watt bulb
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Light • Page 2
7. If the 100-watt bulb is placed in the center of two transparent
spheres, assuming no light absorption, at what distance from the
bulb will seem to our eyes the brightest?
a) 1 radius from the center c) 4 radii from the center
b) 2 radii from the center d) 8 radii from the center
8 . Electric light bulbs are powered by a voltage o f 60 Hertz (or 50 H z in some
countries) sinusoidal wave. The maximum amplitude o f the voltage, and thus a
maximum brightness, occurs twice per cycle because an electric bulb is
a) excited when voltage increases
b) excited when voltage decreases
c) excited due to polarity
d) excited because it is powered by direct current (DC)
9. The light intensity of a light source powered by DC will
a) blink off and on at a particular frequency
b) will show variations at all frequency
c) exhibits fluctuations on 60 Hz.
d) show no variations at all.
10. If a fluorescent bulb and incadescent bulb (light bulb) are
powered by alternating current (AC), which is true about
their light intensity?
a) The fluorescent tube has a greater variation in light
intensity than the incandescent bulb and as a “spiky”
appearance.
b) The incansdescent has a greater variation in light
intensity than the flourescent tube and as a “spiky”
appearance.
c) The incandescent bulb will show the variation as clearly
probably because the filament is cooled down
quickly enough.
d) The fluorescent tube does not “turn o ff’ as soon as the
voltage drops below a threshold level.
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APPENDIX SEVEN
TRADITIONAL LAB ACTIVITY 1:
HOT AND COLD WATER THERMODYNAMICS
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A ctivity I: M ixing Hot and Cold W ater— T herm odynam ics
M aterials
watch with a second hand
2 thermomometers
Tw o 250-mi beaker
ice & hot water
H ot W a ter C o ld W ater
fliiS S lr
M S
W hat happens when equal am ounts of
hot and cold w ater are mixed?
Procedure:
1. Fill the beakers with equal amounts
of hot and cold water.
2. Record their temperatures:
Beaker with hot water
Beaker with cold water
Ave. Tem p o f mixture
°C
°C
°C
4. Time Temperature
(sec) of mixture °C
0
10
70
20
t
30 i 60
m
40
e 50
50
40
60
30
3. Mix the cold water and the hot water and
get the temperature every 10 seconds.
Write your readings in step No. 4.
Use the table to graph your data.
5. M ake a graph to show your results.
Label the horizontal axis as tim e and the vertical
mixture temperature.
Describe the result/s o f your graph..
2 0
10
0
10 20 30 40 50 60 70
M ix tu re te m p e r a tu r e
183
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APPENDIX EIGHT
TRADITIONAL LAB ACTIVITY 2:
INTENSITY OF LIGHT VS. DISTANCE
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APPENDIX NINE
TRADITIONAL LAB ACTIVITY 3:
ELECTROCHMEICAL CELL
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Activity 3 . Electrochemical Cell
M aterials:
One 250 ml beaker
Copper and Zinc electrode
voltmeter
copper wires
water and salt
dilute sulfuric acid or
The purpose of this activity is to
measure the voltage produced in
an electrochemical cell (e.g., wet cell).
acetic acid (vinegar)
C o n d u c tin g
w ir e s
e -
Voltage (v)
-10 +10
Zn m etal Cu m etal
Cu+2 (aq) and SO* 2 (ad
(Inalds th« poroua cup]
Procedure:
1. Assemble the chemical cell according to the
diagram and connect it to the voltmeter.
2. Pour pure water into the beaker.
Record the reading of the voltm eter____
volts
3. Add salt to the beaker and stir the solution.
Record the reading of the
v o ltm e te r______ volts.
5. Put the Cu and Zn electrode near each
other. Record the reading of the voltmeter
volts.
6. Compare the readings of N ro. 4 & 5.
What is the reason for this difference ?
7. Which gives a higher voltage reading:
the salt or acid solution? Why? ______
4. Discard the solution in the beaker and
replace it with the dilute acid solution. Write
the reading of the vo ltm eter volts.
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APPENDIX TEN
MBL ACTIVITY 4:
HOT AND COLD WATER THERMODYNAMICS
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Name Class Date
Activity 4: Mixing Hot and Cold Water— Thermodynamics
Concept Data
Studio
ScienceWorkshop
(Mac)
ScienceWorkshop
(Win)
Thermodynamics GS303 G03 Hot & Cold G03-hot.SWS
Equipment Needed
Qty
Consumable
Qty
Temperature Sensor 1 Water, hot 150 ml
Beaker, 250 ml 2 Water, ice 250 ml
Graduated cylinder, 100 ml I
Protective Gear PS
What Do You Think?
When masses of substances at
different temperatures are mixed
together, the hotter substance
heats the cooler substance and
the resulting final temperature is
somewhere between the initial
temperatures of the two substances.
Can you predict the final temperature
of a mixture of two substances?
Background
Temperature is a measure of the average kinetic energy (energy in motion) of the particles in
a substance. The thermal energy in the substance depends on the mass of the substance,
the specific heat capacity of the substance, and the temperature of the substance.
Heat is the name given to thermal energy when it moves from a substance with a high
temperature to a substance with a lower temperature. Heat is energy in transit. When
substances of different temperatures are mixed, the hotter one heats the cooler one until the
mixture reaches a balance called thermal equilibrium.
The thermal energy (Q) in an object depends on the amount of mass (m), the specific heat
(c), and its temperature (delta T). In equation form:
Q (change in thermal energy) = HI C delta T
Q lost = Q gained
HOT WATER
?
COLD WATER
z
WHAT WILL THE TEMPERATURE OF*
THE MIXTURE BE COMPARED TO THE
TEMPERATURE OF THE HOT WATER AND THE
COLD WATER?
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Name Class Date
PART IIA: Equipment Setup- Equal Amounts of Hot and Cold Water
TEMPERATURE /TOomiFACE
SENSOR
COLD WATER HOT WATER
HOT AND COLD WATER-THERMODYNAMICS
(TEMPERATURE SENSOR)
flEPI.HEAmEIHE
T T U P E E A T U IE O F T H E
C O L D W A T D L
flEPl. M U H U
T H E T X M P E B A 1 D U
O F T H E H O T W A T E X .
1. Put 100 ml (milliliters) of cold
water into the first container so
that it is slightly less than half
full.
2. Put 100 ml of hot water into the
second container so that it is
also slightly less than half full.
PART HI A: Data Recording -
Equal Amounts of
Hot and Cold Water
1 . Place the Temperature Sensor
into the cold water. Start
recording data. Watch the
values of temperature in the
Graph display.
2. After about 40 seconds, move
the Temperature Sensor to the
hot water.
3. Alter a total of 80 seconds,
remove the Temperature Sensor
from the hot water. Pour the hot
water into the cold water.
4. Quickly place the Temperature
Sensor into the combined
liquid. Stir to thorougly mix the
two liquids.
5. Stop data recording after 120
minutes.
Analyzing the Data: Part A
1. Rescale the Graph to fit your data.
2. Find and record the LOWEST temperature in the Graph (the Y-coordinate of the
lowest point in the graph.
3. Find and record the HIGHEST temperature in the Graph (the Y-coordinate of the
highest point in the graph).
4. Find and record the final temperature of the combined liquids.
SIEP1. PO im T H E H O T W A T O
I N T O T H E C O L D W A 1 E X .
m P L H U a U TOTEM-
P E X A T D 1 E O F T H E W X T U X E .
190
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Name Class Date
LABORATORY REPORT
Prediction: PART A - Equal Amounts of Hot and Cold Water
Will the temperature of a mixture of hot and cold water be the same as the temperature of
the hot water by itself or the cold water by itself?
* Predict what the final temperature will be for the mixture of the hot and cold
water compared to the temperature of either the hot water by itself or the cold
water by itself.
Starting temp, of hot water Starting temp, of cold water Predicted temp, of mixture
Data Table: Part A. Equal Amounts of Hot and Cold Water
LOWEST Temperature HIGHEST Temperature FINAL Temperature
QUESTION
1. In Part A, how does the final temperature of the mixture compare
to the lowest temperature of the 100 ml of cold water and the
highest temperature of the 100 ml of hot water.
191
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APPENDIX ELEVEN
MBL ACTIVITY 5: FRUIT BATTERY
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Name Class Date
For this activity, use the Voltage Sensor to measure the voltage (or potential difference)
produced by a piece of fruit that has a copper electrode (copper wire) and a zinc (galvanized
nail) in it. Use DataStudio or ScienceWorshop to record and display the measured voltages.
Prediction
Will the voltage from a piece of fruit be as much as the voltage from a D size
dry cell battery (about 1.5 volts)? Predict what the voltage will be from the
piece of fruit. Record your prediction and the type of fruit you are going to use.
Type of fruit Predicted voltage (V)
PART L Computer Setup
1 . Connect the interface to the computer, turn on the interface, and then
turn on the computer.
2. Connect the Voltage Sensor DIN plug into the Analog Channel A on
the interface.
3. Open the file as shown:
DataStudio Science Workshop
(Mac)
ScienceWorkshop
(Win)
GS01 Fruit Battery.ds GOl Fruit Battery G01-BATT.SWS
Experiment Setup Window
For this activity, the window shows the icon for the Voltage Sensor below
Analog Channel A. Read the instruction in the WorkBook.
193
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Name Class Date
PART Q. Equipment Setup
1. Stick the piece of copper wire and the galvanized nail into the piece of fruit to turn it
into a battery.
Galvanized N a il Copper W ire
Al&gitor dip
included with
VofcageSoisar
FR U IT
Voltage Sensor
To interface
FRUIT BATTERY
2. Put alligator clips on the ends of the Voltage Sensor leads. Connect the red end of
the Voltage Sensor to the copper wire and the black end of the Voltage Sensor to
the nail.
PART III: Data Recording
1. Start recording data. (Watch the values of voltage in the Digits display and Meter).
.2. Record data for about 10 minutes.
3. Stop recording data.
Analyzing the Data
1. Set up the Table to show statistics.
2. Record the Maximum value of voltage in the Data Table
Record your results in the Lab Report section.
194
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Name Class Date
Lab Report - Activity 5: Fruit Battery
What Do You Think?
In this activity you will measure the voltage of a "fruit battery" using a Voltage Sensor. What
type of fruit will produce the most voltage. (In this activity, the word battery will be used in
place of a cell.
Data Table
Type of Fruit Maximum Voltage (V)
Optional
Try inserting the electrodes to different depths
Try inserting the electrodes closer to each other or farther
from each other.
Try different kinds of fruit.
Try different sizes of the kind of fruit.
Questions
1. How does your prediction compare to the actual value of maximum
voltage? Was your prediction lower, higher, or very close?
2. If you tried any of the optional activities, what were the results?
a) Did changing how far in the electrodes make the voltage go up,
go down, or stay the same?
b) Did putting the electrodes closer together make the voltage go up,
do down, or stay the same?
c) Did putting the electrodes farther apart make the voltage go up, go
down, or stay the same?
d) Did a different kind of fruit make any difference? If so, what was the
kind of fruit, and was its voltage higher or lower than your first piece
of fruit?
195
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APPENDIX TWELVE
MBL ACTIVITY 6:
INTENSITY OF LIGHT VS. DISTANCE
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Name Class Date
Activity 6 - - Light Intensity vs. Distance
| Concept DataStudio ScienceWorkshop ScienceWorkshop
! Inverse Square
I Law
P56 Light vs
Distance.DS
(Mac) P56 Light vs.
Distance
(Win) P56_LTVD.SWS
Equipment Needed
Qty
Equipment Needed Qty
Light Sensor (C1-6504A l Meter Stick i
Light Source ((OS-8517) i
W hat Do You Think?
Does the total light energy that passes through an imaginary spherical boundary change as
distance from the point light source increases?
Does the light intensity on the surface of an imaginary spherical boundary change as the
distance form the point light source increases?
How does the change in light intensity with distance for a "real" light source compare to the
change in light intensity with distance for an ideal "point" light source?
Take time to write an answer to these questions in the Lab Report Section.
The purpose of this activity is to investigate the relationship between light
intensity and the distance from a light source.
Background
The light from a point light source spreads out
uniformly in all directions. The intensity at a
given distance r from the light will be equal
to the power output of the light divided by
the surface areas of the sphere through which
the light has spread. Since the area of the
sphere varies as the square of its radius, r,
the intensity, I, will vary 1/ r .
For You To Do
In this activity, use the Light Sensor to measure the light intensity as the sensor moves
away from stationary light source powered by DC. Use the meter stick to measure the distance
between the light source and the light sensor. Use Manual Sampling in DataStudio or use
Keyboard Sampling in ScieceWorkshop to record and display the light intensity and the typed
in values of distance. Use graph of light intensity vs. distance to determine their relationship.
197
Graph
r
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Name Class Date
Lab Report - Activity 6: Light Intensity vs. Distance - Keyboard Method
What Do You Think?
Does the total light energy that passes through an imaginary spherical boundary change as
distance from the point light source increases?
Does the intensity on the surface of an imaginary spherical boundary' change as the distance
from the point light source increases?
How does the change in light intensity with distance for a "real" light source compare to the
change in light intensity with distance for an ideal "point" light source?
Questions
1. What is the relationship of light intensity to distance?
2. The light bulb is not really a point source. How docs this afreet the experiment (and what
can be done to minimize this error)?
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APPENDIX THIRTEEN
INSTRUCTIONAL PLAN FOR THERMODYNAMICS
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Concept: Thermodynamics
O perational Rule:
Thermodynamics is the science that is concerned with the relations between heat
and mechanical energy or work and the conversion from one form into the other.
Modem thermodynamics includes temperature as a necessary coordinate.
C ritic a l A ttribntes:
1. form of energy
2. uses SI units
3. causes change in phase or heat transfer
4. atoms or molecules are always moving
Exemplars fo r D eriving the Concept:
1+ Thermos bottles can hold liquids from 0° C to nearly boiling point and constructed
from the principles of convection, conduction, and radiation.
2+ If you pour water at 100° C into a glass, it may break. But if there is a metal knife or
teaspoon in the glass it won’t break.
3+ Railroad tracks are constructed to have spaces in between the rails at certain
intervals to counter the effect of below zero and hot temperatures.
4- Chlorination of water at normal temperature kills the bacteria for safe water consumption.
5- Ice at 0 °C (solid), steam at I00°C, (gaseous) tap water at 25 °C (liquid) are represented by
the same molecular formula, HiO.
6- Metals become hot and malleable when hammered into thin sheets.
Exemplars from Practicals fo r Testing and C onfirm ing Hypothesis:
7 When equal amounts of hot and cold water are mixed, the resulting temperature of the
mixture is between their initial temperatures. (+)
8. Washing clothes with hot water removes the dirt faster than cold water. (-)
9. When substances of different temperatures are mixed, the hotter one heats the cooler one
until the temperature of the mixture reaches a balance or equilibrium. (+)
10. High temperature substances cool down and cold temperature substances warm up when
they are in thermal contact due to the action of light from the sun. (-)
200
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APPENDIX FOURTEEN
INSTRUCTIONAL PLAN FOR ELECTRICITY
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Concept: Electricity
O perational Rule:
Energy stored in a piece of food can be converted into energy that helps you move
and grow. Energy stored in certain kinds of food (e.g., tubers, fruits, etc.) can also
be converted to electricity. Potatoes, apple, or orange can produce a voltage or
potential difference when electrodes made of different metals are placed into
the fruit or tuber.
C ritical A ttributes:
1. undergoes energy change
2. contains acidic/basic or good conducting properties in substances
3. transfer electrons due reactivity series of different metal
4. use SI units
Exemplars fo r D eriving the Concept:
1. + Cars run on a 12-volt battery made of six metallic terminals dipped in acid solution.
2. + Two different metals (e.g., Zn & Cu) are placed in a salt solution and connected to the
terminals of a galvanometer, a current is produced as shown by the deflection of the
needle
3. + Copper (e.g., SWG 14) is one of the best conductor of electricity that is why it is used
in electrical wires.
4. - Power lines carrying 7000 volts or more can cause accidents like electrocution.
5. - Gold is refined by electrolysis. That is, gold is plated out on thin gold foil cathodes,
which are then melted and poured in molds as gold bars.
6. - Corrosion, the chemical reactions of metals with their environment, causes billions of
dollars of damage to metal structures each year.
Exemplars from P ractical fo r Testing and C onfirm ing Hypothesis:
7. Putting the two electrodes farther apart in the fruit increases the voltage slightly. (+)
8. A large grapefruit will produce a higher fruit voltage than a small grapefruit. (-)
9. The juicier the fruit (e.g., more acidic) the higher the fruit voltage. (+)
10. Inserting the same metallic electrodes into the fruit will produce a higher fruit voltage.(-)
202
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APPENDIX FIFTEEN
INSTRUCTIONAL PLAN FOR LIGHT
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Concept: Light
O perational Role:
The light from a point light source spread out uniformly in all directions. The
intensity, I, at a given distance, r, from the light will be equal to the power output
of the light divided by the surface area of the sphere through the light has spread.
Since the area of a sphere varies as the square of its radius, R , the intensity will
vary as 1/R2 .
C ritic a l A ttributes:
1. uses energy source (point or real)
2.. exhibits rectilinear propagation
3. varies inversely with reflecting area
4. varies directly with distance of separation
Exemplars fo r D eriving the Concept:
1. + Volcanic eruptions can brighten up the sky.
2. + Lighthouses guide ocean going vessels and sea-farers.
3. + Police helicopters uses searchlight to fight street crime.
4. - Drivers put on low-beam while driving at night to avoid accidents caused by
glaring the incoming driver.
5. - The sun shines the hottest along the equator.
6. - Luminous watches are very helpful in dark places.
Exemplars from P ractkals fo r Testing and C onfirm ing Hyptothesis:
7. The degree of brightness on the surface of an imaginary spherical boundary change as
distance from the point light source increases. (+)
8. Total light energy that passes through an imaginary boundary change as distance from
point light source increases. (-)
9. The change in the degree of brightness with distance for a “real” light source compare
to the change in the degree of brightness with distance for an ideal “point” light source
follow the simple mathematical model (-).
10. The light bulb is not really a “point “source but fits an inverse square mathematical
model. (+)
204
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APPENDIX SIXTEEN
MODIFIED STUDENT COMPUTER ATTITUDE SURVEY
205
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Item Strongly Disagree Agree Strongly
Number Stem Disagree
(1) (2) (3)
Agree
(4)
Factor 1: Comfort/Anxiety
( ) () () ()
1 I am confident about my ability to do well in a
subject that requires me to use computer technology
() () ( ) ()
2 I feel at ease learning about computer
technologies
() () () ()
1 lam not the type to do well with computer
technologies.
( ) ( ) () ()
4 The thought of using computer technologies
frightens me.
( ) () () ()
5 Computer technologies are confusing to me.
( ) () () ()
6 I do not feel threatened by the impact of
computer technologies.
( ) ( ) () ()
2 I am anxious about computers because I feel
like 1 might break them
() () () ()
8 I feel comfortable about my ability to work
with computer technologies.
() () () ()
Factor II: Usefulness (positively phrased, specific content)
9 Communicating with others over a computer
network can help me to be a more effective student.
() () () ()
10 With the use of computer technologies I can
create science reports to enhance my studies.
() () () ()
11 If 1 can use word processing software, I will
be a more productive student.
() () () ()
12 I could use computer technologies to access
many types of information sources for my work.
() () () ()
13 Computer technologies can be used to assist me
with my discipline or behavior.
() () () ()
Factor 01: Usefulness (negatively phrased, general content)
14 I don't have any use for computer technologies
on a day-to-day basis.
() () () ()
1S Using computer technologies in my studies
will only mean more work fix' me.
() () () ()
16 I do not think that computer technologies will
be useful to me as a student.
() () () ()
17 Anything that computer technologies can be
used for, I can do just as well in some other way.
() () () ()
18 I don’t see how computer technologies will
help me learn new skills will only mean more
work fix’ me.
( ) () () ()
19 Knowing how to use computer technologies
will not be helpful in my future studies.
() ( ) () ()
* Underlined item numbers reflect negative phrased stems,
from Delcourt & Kinzie, 1993, p. 40.
206
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Asset Metadata

Creator Osio, Sergio Albina (author)

Core Title An evaluation of the use of microcomputer-based laboratory instruction on middle school students' concept attainment and attitudes towards computer -based instruction

School Graduate School

Degree Doctor of Philosophy

Degree Program Education

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag education, sciences,education, technology of,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor McComas, William (committee chair), Crockett, Michele D. (committee member), Nyquist, Julie G. (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-227136

Unique identifier UC11338996

Identifier 3073828.pdf (filename),usctheses-c16-227136 (legacy record id)

Legacy Identifier 3073828.pdf

Dmrecord 227136

Document Type Dissertation

Rights Osio, Sergio Albina

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA