a case study of the physics enhancement project for two year colleges, its effects and outcomes on...

8/11/2019 A Case Study of the Physics Enhancement Project for Two Year Colleges, Its Effects and Outcomes on the Teachin

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Orde r Number 91S0457

A concept learning and teaching approach to the instruction of linear motion in introductory college physics

Chyuan, Jong-pyng Michael, Ph.D.

The Ohio State University, 1991

U M I300 N. Zeeb Rd.Ann Aibor, MI 48106


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A CONCEPT LEARNING A N D TEACHING APPROACH TO THE

INSTRUCTION OF LINEAR MOTION

IN

INTRODUCTORY COLLEGE PHYSICS

DISSERTATION

Presented in Partial Fulfillment o f the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Jong-pyng Michael Chyuan, B.S., M.S .

* * * * *

The Ohio State Universi ty

1991

Dissertation Committee:

Dr. Arthur L. White

Dr. Wil li am D. Ploughe

Dr. Keith A. Hall

Approved by

Coll ege of Education


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To my parents, my wife, and my son

ii


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ACKNOWLEDGMENTS

X wish to express my deep grati tude to Dr. Arthur L.

White for his enthusiastic and expert guidance throughout

this research project. Th e complet ion of this dissertation

would no t have been possible w ithout support.

Sincere appreciation and thanks is expressed to Dr.

Will iam D. Ploughe for sharing his knowledge and experience

teaching physics and for generously implementing the three

teaching methods in his physics 101 classes.

I would also like to thank Dr. Keith A. Hall fo r his

valuable counsel and thoughtful suggestions throughout this

research project.

Finally, my deep appreciat ion and thanks to a dear

friend and colleague, Evelyn Zei fman Becker, for her patience and help in edi ting manuscript.

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VITA

November 13, 1952 ........... Born - Taiwan,Republic of China

1974 ......................... B.S., National Taiwan NormalUniversity, Taiwan, Republic of China

1974-1975 ................... Teaching Assistant,Provincial Tainan Teachers Co11ege, Ta iwan, ROC

1979 ......................... M.S., National Taiwan NormalUniversity, Taiwan, Republic of China

1979-1980 ................... Instructor National Defense Medicine

College, Taiwan, ROC

1979-1987 ................... InstructorProvincial Taipei Teachers College, Taiwan, ROC

1987-present ................. Associate Professor,Taipei Teachers College,

Taiwan, ROC1987-1988 . . . Director of Computer Center

Taipei Teachers College Taiwan, ROC

1990-present ................. Graduate Research AssociateThe Ohio State University, Columbus, Ohio, USA

PUBLICATIONS

Chyuan, J. M. (1979). The growth and the analysis of Cu-Zn

alloy crystal . Unpublished mas ters thesis, National

Taiwan Normal University, Taipei, Taiwan, ROC.

Chyuan, J. M. (1980). Physics of Matter, Temperature and Heat (chapter 6, 7). In J. Chou (Ed.), Teachers College Phvsics (I). Taipei: Cheng-jong Book Company.

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Chyuan, J. M. (1981). Waves and sound, Optics, Electricity and Magnetism, Electromagnetism, Modern Physics (chapter 8, 9, 10, 11, 12). In J. Chou (Ed.), Teachers College Physics (II). Taipei: Cheng-jong Book company.

Chyuan, J. M. (1984). Water and energy. In T. Li (Ed.),Children Natural Science Research and Study Curriculum and Development (I). Taipei: Taipei Teachers College.

Chyuan, J. M. (1985). The analysis and research of primary school natural science learning adaptation's problem in Taipei area . Taipei: Jong-shing Publishing.

Chyuan, J. M. (1985). Energy and soil. In M. Kou (Ed.),Children Natural Science Research and Study Curriculum and Development (II). Taipei: Taipei Teachers College.

Chyuan, J. M. (1986). Energy and soil. In J. Chen (Ed.), Children Natural Science Research and Study Curriculum and Development (III). Taipei: Taipei Teachers College.

Chyuan, J. M. (1987). Energy and soil. In J. M. Chyuan(Ed.), Childrerr-i'Jatural Science Research and Study Curriculum and Development (IV^. Taipei: Taipei Teachers College.

Chyuan, J. M. (1988). Taipei teachers college entrance examination information system (EEIS). In J. M. Chyuan (Ed.), Computer Education Symposium of Teachers colleges in Taiwan Area (pp. 97 - 129). Taipei: Taipei Teachers College.

FIELDS OF STUDY

Major Field: Education

Studies in Science Education: Dr. Arthur White,Dr. Victor Mayer, Dr. Patricia Blosser.

Studies in Educational Research: Dr. Arthur White,Dr. Keith Hall, Dr. John Kennedy.

Studies in Technology in Science: Dr. William Ploughe.

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TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...................................... iii

V I T A ..................................................... iv

LIST OF T A B L E S ........................................ viii

LIST OF FIGURES ....................................... xii

CHAPTER

I. INTRODUCTION .................................. 1

Need for Study ................................ 3Statement of the Problem ...................... 6Definition of Terms ............................ 8

A s s u m p t i o n s ....................................... 10Delimitations .. ................................ 10L i m i t a t i o n s .................................... 11

II. LITERATURE REVIEW ............................... 13

Misconception .................................. 13Concept Definition ............................ 20Concept Learning .............................. 22Concept M a p ....................................... 26Teaching Concept .............................. 32Concept Teaching Model ........................ 39General Instruction Model 4 6

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III. METHODS AND PROCEDURES ............................ 50

Design ............................................. 50Sample and Population ........................... 59Instrumentation .................................. 65Research Design .................................. 80

Analysis ........................................... 85Procedures ......................................... 86

IV. R E S U L T S ............................................. 90

Cognitive Learning Effect ....................... 90Cognitive Teaching Effect ....................... IllLearning Physics Attitude Effect ............... 116 Multivariate Regression Effect ................. 121S u m m a r y ............................................ 137

V. SUMMARY AND RECOMMENDATIONS .................... 139

Summary of Findings ......................... 139Discussion ........................................ 142Summary and Interpretation ..................... 149Specific Instructional Recommendations ........ 154Future Research .................................. 159

BIBLIOGRAPHY .......................................... 162

APPENDICES

A. Physics Concept Instruction Design ............. 169

B. Physics Concept Map T e s t .......................... 221

C. Physics Misconception T e s t ........................ 226

D. Physics Achievement T e s t .......................... 231

E. Physics Attitude Test ............................. 236

F. Course Copies for the Concept Teaching Method . . 246G. Students' Constructed Concept Maps ............. 255

H. Frequency Distribution for Items of the Physics Misconception Test and the Physics Att itude Test. 258

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LIST OF TABLES

Table Page1. Univariate ANOVA on the Physics Misconception

Pretest by Retained/Deleted Group ............. 61

2. Distribution by Gender among Three Classes . . . 62

3. Distribution by Program Areas among three Groups. 62

4. Distribution by Age among Three Classes . . . . 63

5. Distribution by High School Physics Chemistry Courseamong Three Classes ............................ 64

6. Items of the Achievement Test by Knowledge Level. 70

7. Means, Standard Deviations, and Item-TotalCorrelations for the Physics Learning Concept

Attitude Test .................................. 74

8. Means, Standard Deviations, and Item-TotalCorrelations for the Physics Misconception Attitude T e s t ............................................ 75

9. Means, Standard Deviations, and Item-TotalCorrelations for the Physics Teaching Concept

Attitude Test .................................. 76

10. Means, Standard Deviations, and Item-TotalCorrelations for the Physics Concept Map Attitude Test .......................................... 77

11. The Mode of Physics Instruction ............... 82

12. Sample Size, Means, and Standard Deviations of thePhysics Concept Map Pretest and Posttest, Physics

Misconception Pretest and Posttest, and Physics Achievement Test among Three Groups ........... 92

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13. Multivariate Tests of Regression Effect for theRelationship between Dependent Variables, Physics Concept Map Posttest, Physics Misconception Posttest, and Physics Achievement Test, and Covariates, PhysicsConcept Map Pretest and Physics Misconception P r e t e s t .......... 93

14. Multivariate and Univariate Analysis of Covariance: Treatment Effect on the Concept Map Posttest, the

Misconception Posttest, and the Achievement Test with the Concept Map Pretest and the Misconception

Pretest as Covariates ......................... 95

15. Bryant-Paulson Comparisons on Physics Concept Map T e s t ............................................ 96

16. Test of Significance for Discriminant Functions about Dependent Variables: Physics Misconception Posttest, Physics Concept Map Posttest, and Physics

Achievement T e s t ................................ 98

17. Canonical Discriminant Functions Evaluated at Group Centroids of the Physics Misconception Posttest, the Physics Concept Map Posttest, and the Physics Achievement Test ........................ 99

18. Standardized Discriminant Function Coefficients of Dependent Variables ............................. 100

19. Sample Size, Means, and Standard Deviations of the Proposition, Hierarchy, Cross-Link, and Example of the Concept Map Posttest among Three Groups . . 102

20. Multivariate Tests of Regression Effect for the Relationship between Dependent Variables, Four Parts of the Concept Map Test, and Covariates,Concept Map Pretest and Misconception Pretest . . 103

21. Multivariate and Univariate Analysis of Covariance:

Treatment Effect on the Concept Map Posttest with thePhysics Concept Map Pretest and the Physics Misconception Pretest as Covariates .......... 104

22. Bryant-Paulson Comparisons on the Proposition of thePhysics Concept Map T e s t .......................... 106

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23. Bryant-Paulson Comparisons on the Hierarchy of the Physics Concept Map T e s t .......................... 106

24. Test of Significance for Discriminant Functions about Dependent Variables: Proposition, Hierarchy,Cross-Link, and Example of the Concept Map Test . 107

25. Canonical Discriminant Functions Evaluated atGroup Centroids of the Concept Map Test . . . . 109

26. Standardized Discriminant Function Coefficientsof Dependent Variables ......................... 110

27. Mult ivariate Tests of the Effect of Concept Map and Misconception Pre-Post-Tests and the Effectof Group by Pr e- Po st -T es ts .................... 113

28. Within-Subject Effect on Misconception Test . . 114

29. Within-Subject Effect on Concept Map Test . . . 114

30. Means and Standard Deviations of Four AttitudeTests ............................................. 118

31. Multivariate Tests of Regression Effect for theRelationship between Dependent Variables, Four

Attitude Subtests, and Covariates, Concept Map Pretest and Misconception Pretest ............. 119

32. Multivariate Analysis of Covariance: Treatment Group Effect Test of Four Parts of the Concept

Map P o s t t e s t .................................. 119

33. Means of the Physics Attitude T e s t ............ 120

34. Correlation of the Dependent Variables and thePredictors for the Traditional Teaching Method (Group 1) 126

35. Canonical Correlation, Coefficients, and Componentsfor Each Canonical Variate for the Group 1 . . . 127

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36. Multivariate Regression Analysis of the Relation between the Concept Map Posttest, Misconception

Posttest, and Achievement Test, and the Four Attitude Subtests for the Traditional Teaching Method . . 128

37. Univariate F-Test of the Dependent Variables forthe Traditional Teaching Method ............... 128

38. Multiple Regression Test on the Traditional Teaching Method ................................ 129

39. Correlation of the Dependent Variables and the Predictors for the Example-NonexampleTeaching Method (Group 2) 130

40. Canonical Correlation Mult ivariate Regression Analysis of the Relation between the Concept Map

Posttest, Misconception Posttest, and Achievement Test, and the Four Attitude Subtests for the Example-Nonexample Teaching Method (Group 2) . . 131

41. Correlation of the Dependent Variables and the Predictors for the Concept Teaching Method (Group 3 ) ........................................... 132

42. Canonical Correlation, Coefficients, and Components for Each Canonical Variate for the Group 3 . . . 133

43. Multivariate Regression Analysis of the relation between the Concept Map Posttest, Misconception

Posttest, and Achievement Test, and the Four Attitude Subtests, for the concept Teaching Method . . . 134

44. Univariate F-Test of the Dependent Variables forthe Concept Teaching Method .................. 134

45. Multiple Regression Test on the Concept Teaching M e t h o d ............................................. 135

46. The Overall Significant Correlation of the Dependent Variables and the Predictors Relating Group 1, 2, and 3 136

47. Summary of the Significant Results in This Study. 137

48. Concepts Taught in the Three Lecture Sessions . . 156

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LIST OF FIGURES

Figure Page

1. Probabil ity Levels of Examples and Nonexamples . 36

2. A Concept Teaching-Model ( Tennyson and Cocchiarella, 1986) 47

3. General Instructional Model (Berlin & White,1987) 49

4. The Schema of the Physics Concept InstructionalDesign ........................................ 53

5. Physics Instructional Design Model ............. 54

6. The Teacher-Made Concept Map About Position . . 57

7. The Instructional Design for the Concept,P o s i t i o n ..................................... 58

8. The Name and Contents of Experiment Instruments . 79

9. The Concept Teaching Sequence among ThreeTreatments .............................. 81

10. Physics Concept Teaching and Learning ResearchD e s i g n ....................................... 83

11. Data Collection Procedure ...................... 88

12. Time T a b l e .................................... 89

13. The Three Group Centroids of the Physics Misconception Posttest, the Physics Concept Map

Posttest, and Physics Achievement Test in the Discriminant-Function Space ................... 99

14. The Three Group Centroids of the Concept MapPosttest in the Discriminant-Function Space . . . 109

15. Interaction between the Three Teaching Methodsand the Concept Map Pretest and Posttest . . . . 115

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concepts, Novak and Gowin (1984) illustrate an educational

tool referred to as concept mapping to help students learn

and to help teachers organize teaching material. A concept

map is a schematic device for representing a set of concept

meanings embedded in a framework of propositions which are

two or more concept labels linked by words in a semantic

unit.

As for teaching concepts in the classroom, Merrill and

Tennyson (1977), based on the conceptual model of

classification behavior (Woolley & Tennyson, 1972) and

experimental research studies (Tennyson, 1973; Tennyson,

Steve, & Boutwell, 1975), developed a concept-teaching model

in which a set of instructional design guidelines is

provided to enhance concept teaching. Tennyson and

Cocchiarella (1986) update and extend the concept-teaching

model which is composed of two fundamental components of

instructional design: (1) the content structure of a given

domain of information and (2) the organization of

instructional design variables related to the use of

specific content structures.

In classroom teaching, the organization of teaching

materials and activities is closely concerned with concept teaching. Berlin and White (1987) present an instructional

model which provides for the infusion and integration of

teaching technology into the instructional process, and it

also assists teachers in effectively promoting the learning


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of concepts in the classroom.

Need for Study

Understanding of kinematical concepts is the ability to

apply them successfully in learning and interpreting simple

motions of real objects. However, certain conceptual

difficulties occur frequently and predictably among

introductory physics students in college. Student

understanding of physical concepts has been subject to

descriptive analysis (Trowbridge & McDermott, 1980). Physics

instructors generally share a common interpretation of the

kinematical concepts based on operational definitions and

precise verbal and mathematical articulation. On the other

hand, students are likely to have a wide variety of somewhat

vague and undifferentiated ideas about motion based on

intuition, experience, and their perception of previous

instruction. Thus students often have insufficient

qualitative understanding of position, velocity, and

acceleration (Trowbridge & McDermott, 1981). Moreover, in

introductory physics teaching, Ploughe (1990, private

communication) indicates that many college students have

verbal problems in explaining the phenomena of motion and

incorrectly use physics definitions to discriminate the

concepts of motion.

Frequently, many students taking introductory physics


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cannot apply what they have learned about graphs from their

study of mathematics to physics. The difficulties

experienced by students in connecting graphs to physical

concepts includes the indecision as to whether to extract

the desired information from the slope or the height of a

graph. Students also find it more difficult to interpret

curved graphs than straight-line graphs (McDermott,

Rosenquist & Zee, 1987; White, 1987; Mokros & Tinker, 1987).

Furthermore, many students are unable to translate back and

forth from a position versus time graph to a velocity versus

time graph (McDermott et al., 1987).

Prior to the instruction of introductory college

physics, many students have a set of protoconcepts for

interpreting motion in the rea l world (Trowbridge &

McDermott, 1980). McCloskey (1983) indicates that the

protoconcepts, misconceptions, appear to be grounded in a

systematic, intuitive theory of motion and they are not

consistent with fundamental principles of Newtonian

mechanics. Halloun and Hestenes (1985) also explain that a

system of beliefs and intuitions about physical phenomena

are possessed by each college student entering a first

course (motion) in physics and the system is derived from extensive personal experience. This system functions as a

common sense theory of the physical world which the student

uses to interpret what he uses and hears in the physics

course. Yet conventional physics instruction fails almost


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completely to take this into account. Moreover, the level of

mathematical competence is not sufficient for high

performance in physics. McDermott, Rosenquist, and Zee

(1987) express that students who have no trouble making

physics graphs and computing slopes of graph cannot apply what

they have learned about graphs from their study of

mathematics to physics. Differences in gender, age, academic

major, and high school mathematics showed no effect on

physics achievement. Conventional instruction had little

effect on the student's basic knowledge state and the basic

knowledge gain under conventional instruction is essentially

independent of the professor (Halloun & Hestenes, 1985).

Thus, Clement (1982) suggests that development of innovative

instruction techniques that emphasize rigorous understanding

of qualitative physics principles should be encouraged.Based on using the conceptual model of classification

behavior (Woolley & Tennyson, 1972), the example and

nonexample teaching strategy had significant results for

instructional procedures on the cognitive level of behavior

in non-science areas (Tennyson, Woolley, & Merrill, 1972;

Tennyson, 1973; Tennyson, Steve, & Boutwell 1975; Tennyson &

Tennyson, 1975) and in a study of crystal structure

(Tennyson, et al., 1975; Merrill & Tennyson, 1978). But,

this teaching strategy has not been used for instructional

procedures in the physics area.


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The concept-teaching model originally derived by

Merrill and Tennyson (1977) and updated and extended by

Tennyson and Cocchiarella (1986) is based on the theory of

cognitive learning processes and presents strategies of

concept instructional design. However, this model does not

clearly infuse and integrate teaching technology in its

strategies. On the other hand, the general instructional

model designed by Berlin and White (1987) relates to

organizing teaching materials and activities to combine with

the concept-teaching model.

Therefore, there is a need to (1) develop an

instructional design, in accordance with a physics concept

teaching model derived from learning theories and teaching

models, that is related to the motion topic in introductory

physics for college level students, (2) test whether the

design can improve students' concept learning about motion,

and (3) learn if it can be used as an instructional system

for introductory college physics.

Statement of the Problem

The major tasks of this study are to : (1) develop and

analyze a physics unit of physics instructional design

derived from the physics concept teaching model for college

non-science students, (2) test the design in regard to the

students' learning physics concepts, students' physics

misconceptions, students' physics achievement, and students'


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attitude toward learning physics, (3) compare whether the

design is better in helping students' concept learning than

the teaching strategy using example and nonexample or the

traditional teaching strategy, and (4) identify what should

be done to further improve the design.

Problems

According to the statement of the main tasks in this

study, three groups of college non-science students are

randomly assigned to three teaching methods: physics concept

teaching method, physics example-nonexample teaching method,

and physics traditional teaching method. Thus the problems

related to the major tasks are stated as follows:

(1) Are there significant differences among the three

groups of non-science students on measures of their learning

physics concepts, their physics misconceptions, and their

physics achievement after three teaching methods are

implemented?

(2) Are there significant difference in physics concept

learning and physics misconception between before and

after three different teaching methods being implemented by

three groups of non-science students?(3) Are there significant differences among three

groups of non-science students on measures of their

attitudes toward learning physics concepts after three

teaching methods are implemented?


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(4) What is the predictive contribution of such

variables to learning physics concepts, physics

misconceptions, physics achievement, and attitude toward

learning physics concepts for each of the three teaching

methods?

Definition of Terms

Physics traditional teaching method An approach to

physics concept instruction that involves the teaching of

(1) definition and label of a concept, (2) example or

examples about the concept, and (3) phenomena description or

demonstration to a large group of students.

Physics example-nonexample teaching method An

approach to physics concept instruction that involves the

use of example and nonexample after teaching of the definition of a concept in the traditional teaching method.

Physics concept teaching method An approach to

physics concept instruction that involves the presentation

of a concept map designed by Novak (1984), the teaching

technology of the general instructional model designed by

Berlin and White (1987) , and the instructional strategy of

the concept-teaching model designed by Tennyson and

Cocchiarella (1986).

Concept map A concept map is a schematic device for

representing a set of concept meanings embedded in a


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framework of propositions (Novak & Gowin, 1984).

Misconception Misconception is the term commonly

used to describe an unaccepted (but not necessarily "wrong")

interpretation of a concept illustrated in the statement in

which the concept is embedded (Novak & Gowin, 1984).

Concept teaching Concept teaching contains five

procedures: (1) present a definition, (2) provide an

expository presentation, (3) provide attribute isolation,

(4) provide an inquisitory practice presentation, and (5) provide a test of classification (Merrill & Tennyson, 1977).

Concept learning Learning the meaning of a concept,

tha t is, learning the meaning of its criteria attributes;

includes concept formation and concept assimilation

(Ausubel, Novak & Hanesian, 1978).

Physics 101 Nature of Physical World An

undergraduate physics course which is an elementary

description of the physical world emphasizing scientific

method and contemporary viewpoints. This course is the first

quarter of two quarters sequence. Laboratory work and

demonstrations are included in it.

Physics 111 General Physics: Mechanics and Heat

An undergraduate course in mechanics and heat for students majoring in the life sciences and in architecture. This

course is the first of a series of three courses which

presents major physical principle and concepts from a

contemporary point of view. The sequence includes laboratory


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work and demonstrations.

Physics 131 Introductory Physics: Particles and

Motion An undergraduate physics course that presents the

major concepts of physics from a contemporary point of view,

for students majoring in physical sciences, mathematics, or

engineering. This course is the first quarter of three

quarters sequence.

Assumptions

The assumptions of this study are stated as follows:

1. The non-science students in this study, before

attending the Physics 101 course, have at least studied

Mathematics 075 (previously Math 102) or placement in

mathematics course code R or higher.

2. The non-science students over the duration of this

study do not get any private physics tutorial except

classroom teaching and recitation and appointments with the

teaching professor and teaching assistant.

3. The non-science students responses to the

instruments in this study are a valid indication of their

physics concepts, physics achievement, and physics attitude

about motion.

Delimitations

1. The subjects of this study are college non-science

students studying non-calculus (algebra only) physics


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(Physics 101).

2. The total of three sections of Physics 101 will be

included in this study.

3. The concept map test for assessing students'

learning of physics concepts is related to Novak's learning

theory. The scoring criteria of the test are from Novak's

rules and include: (a) relationships, (b) hierarchy, (c)

cross lines, and (d) specific examples. Also, the content of

the concept map pretest and posttest is the same.

4. The content of the items of the misconception test

related to the specific physics topic in the design unit are

concerned with position, velocity, acceleration, and graph,

and the item sources of the misconception test are from

research results. Also, the content of the misconception

pretest and posttest is the same.

5. The items of the physics achievement test are based

on the objectives of Physics 101.

6. The item sources of the learning attitude test are

from the research results, students' written responses,

classroom observation, and personal interview. The contents

of the test are concerned with: (a) learning concepts, (b)

misconception beliefs, (c) teaching concepts, and (d)

concept mapping.

Limitations

1. The classroom teaching time of this study was


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determined by the schedule of Physics 101.

2. Results of this study apply only to the Physics 101

course, not to Physics 111 and 131 series physics courses.

3. Out-of-class physics learning is undetermined.

4. Results of this study cannot be generalized to other

units of study in the Physics 101 course, without further

testing.'

5. The tested results of any subject who misses one of

the implemented cognitive tests will not be considered in

this study.


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CHAPTER II

LITERATURE REVIEW

The relevant literature for this study encompasses

seven main topics: misconception, concept definition,

concept learning, concept map, teaching concept, concept

teaching model, and general instruction model.

Misconception

Recent studies have indicated that many students

construct their own informal theories to interpret a number

of physics events. Those students7 intuitive ideas often

conflict with Newtonian theory and so interfere with physics

learning and teaching. The ideas are always labeled as

misconceptions. Trowbridge and McDermott (1980) indicated

that students have a set of "protoconcepts" before

instruction which are a repertoire of procedures,

vocabulary, associations, and analogies for interpreting

motion in the real world. Students often fail to make connections between the protoconcepts and the concepts of

kinematics. Some students are even remarkably persistent in

certain preconceptions and they depend strongly upon the

establishment of satisfactory connection between the new

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motion concepts and the protoconcepts.

In observing a large number of college students taking

introductory physics, Clement (1982) found that "conceptual

primitives" which include key physics concepts and

fundamental physics principles and models are misunderstood

by many physics students at the qualitative level in

addition to any difficulties that might occur with

mathematical formulation. He explains that Newtonian ideas

are more likely misperceived or distorted by students so as

to fit their existing preconceptions; or they may be

memorized separately as formulas with little or no

connection to fundamental qualitative concepts.

McCloskey (1983) indicates that many students have

striking misconceptions about the motion of objects in

apparently simple circumstances and the misconceptions are

apparently grounded in a systematic, intuitive theory of

motion which is not consistent with basic principles of

Newtonian mechanics.

Halloun and Hestenes (1985) also found that each

student entering a first course in physics possesses a

system of beliefs and intuitions about physical phenomena

derived from extensive personal experience. These systems are referred to as a "common sense belief" of the physical

world which the student uses to interpret his experience,

including what he uses and hears in the physics course. The

common sense beliefs about motion are generally incompatible


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with Newtonian theory and they are very stable, and

conventional physics instruction does not .successfully

correct them. Halloun and Hestenes (1986) indicate that the

common sense beliefs which are incompatible with established

scientific theory are dismissed by most scientists, but

students are not so easily disabused of the beliefs because

their own beliefs are grounded in long personal experience.

They also express that Aristotle was the first to

systematically develop explicit formulations for the common

sense beliefs about physical phenomena and organize them

into a coherent conceptual system, and the beliefs systems

of students untutored in physics are sometimes characterized

as "Aristotelian." McClelland (1985) used "pre-Galilean

ideas" to characterize these views of the world which differ

from those ideas held by "scientists". Hewson (1985) indicated that students bring to the science classroom

surprisingly extensive "student theories" or "alternative

conceptions" about how the natural world works.

Misconception about motion

In learning introductory college physics, many students

have misconceptions in studying the kinematical concepts, the first subject of classical mechanics. Trowbridge and

McDermott (1980) found that college students with no

previous study of physics thought of the word 'speed' as a

relation between the distance traveled and the elapsed time


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but not necessarily as a ratio; 'accelerate ' was used to

indicate that an object 'speeded up'. From many exploratory

interviews and a number of preliminary trials, Trowbridge et

al. (1980) developed two speed comparison tasks and asked

student to compare the simultaneous motions of two identical

balls rol ling on parallel U channels. As a result, they

found that failure on the speed comparison tasks was almost

invariably due to improper use of a position criterion to

determine relative velocity. Moreover, they also found

evidence that some students' certain preconceptions may be

remarkably persistent.

About the understanding of the concept of acceleration

among college students, Trowbridge and McDermott (1981)

designed acceleration comparison tasks to find how students

apply their concepts of acceleration in interpreting simple motions of real objects. They found that many students use

different concepts as the criterion to compare accelerations

and these concepts are position, speed, relative velocity,

and average velocity.

In the real world experience, it is illustrated that

some students have the misconception in separating the

concepts of velocity and position at a particular instant.

Trowbridge and McDermott (1980) and Halloun and Hestenes

(1985) reported that some students believe that if two cars,

on the freeway, reach the same position at the same time,


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then they must have the same speed at that instant.

Misconception about motion graphs

Graphing is a powerful and key symbol system for

representation of data and scientific communication.

McDermott, Rosenquist, and Zee (1987) point out that many

undergraduates taking introductory physics seem to lack the

ability to use graphs either for imparting or extracting

information. The analysis of graphing errors identified in

their study indicates that many are a direct consequence of

an inability to make connections between a graphical

representation and the subject matter it represents. For

example, students frequently do not know whether to extract

the desired information from the slope or the height of a

graph, and many are unable to translate back and forth form

a position versus time graph to a velocity versus time graph.

Mokros and Tinker (1987) in their Microcomputer-Based

Lab (MBL) project identified that two major types of

graphing misconception are: (1) a strong graph-as-picture

confusion, and (2) a weaker indication of slope/height

confusion. White (1987) also indicates that students tend to

confuse the picture of an event with the graph of an event.

Implications for Instruction about Motion

Because many undergraduate students are unable to

discriminate between position and velocity and some students


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seem to depend strongly upon the establishment of

satisfactory connections between new physics concepts and

the protoconcepts with which the student is already

familiar, Trowbridge and McDermott (1980, 1981) suggest that

a conscious effort should be made to try to help students

relate physical concepts to their experience. For example,

the relationship between the use of technical vocabulary and

the understanding of physical concepts needs to be examined

carefully and more attention at the introductory level might

be devoted to the basic kinematical concepts. They also

suggest more attention to the detailed information about

conceptual understanding that can usefully help some college

students overcome deficiencies in studying introductory

college physics.

Clement (1982) thinks that it is important to find

teaching strategies that encourage students to articulate

and become conscious of their own preconceptions by making

predictions based on them and also encourage them to make

explicit comparisons between preconceptions, accepted

scientific explanations, and convincing empirical

observations. Furthermore, class discussions and arguments

between students are especially helpful in their qualitative

understandings about the Newtonian point of view.

Halloun and Hestenes (1985) from their diagnostic test

results argue that a student's initial knowledge has a large


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19

effect on his performance in physics, but conventional

instruction produces comparatively small improvements in his

basic knowledge. However, Hewson (1985) describes the use of

a microcomputer program designed to diagnose students who

use a position criterion for judging when two objects are

moving with the same velocity and the program is effective

in changing students' alternative conceptions of velocity.

Rosenquist and McDermott (1987) have developed an

approach to the teaching of kinematics. By means of specific

examples, instruction based on the direct observation of

motion can help students recognize key features of

definitions, distinguish related concepts from one another,

and make explicit connections among concepts, their

graphical representation, and the real world. For example,

Rosenquist and McDermott (1987) apply a limiting process of

a curved position versus time graph to have a conceptual

criterion at the limit. From the concrete experience of the

magnified sections of a curved graph, students can deepen

their understanding of both instantaneous velocity and the

slope of a curved graph.

When interpreting a graph in physics, McDermott,

Rosenquist, and Zee (1987) express that the ability to draw and interpret graphs is perhaps one of the most important in

the study of physics. They believe that facility with

graphing can play a critical role in helping students deepen

their understanding of the kinematical concepts.


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McDermott (1990) and Trowbridge (1990) introduce the

use of a computer software package, Graphs and Tracks, as

one way for students to deepen their understanding by having

a direct experience making connections between motion and

its graphical representation.

Concept Definition

In science teaching and learning, concepts are always

the important results of scientific processes. Pella (1966)

indicates that concepts may be viewed initially as a summary

of the essential characteristics of a group of ideas and

facts that epitomize important common features or factors

from a large number of ideas. Because of the comprehensive

nature of concepts, Pella states that concepts are useful to

the individual in order to gain some grasp of a much larger field of knowledge than he has personally experienced.

The most fundamental meaning of concept is exhibited in

individual behavior by responding to a class of observable

objects or object qualities such as those implied by the

names "color," "shape," "size," "heaviness," and so on, or

by common objects such as "cat," "chair," "tree," and

"house." Those concepts are concrete and they are concepts

by observation. Then, abstract concepts can be described and

even defined; [e.g., mass, temperature, and prime number.

(Gagne, 1970)].


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Shumway (1971) suggests a concept is a partitioning of

a class X, universal class over which the concept is

defined, into two disjoint classes X1# positive instances,

and X2 , negative instances of the concept. The class X is

the union of the class X1 and the class X2 and the classes

X1 and X2 are disjoint. To say that a student knows the

concept over the class X is to say that given any object

from the class X the student is able to identify the object

as a member of the class X^ or the class X2 associated with the concept over the class X. Thus their relationships can

follow from the above results:

X = xi x2 , X = X - X2, and X2 = X - X ^

According to the empirical research results by Merrill

and Tennyson (1977), a concept can be defined as "a set of

specific objects, symbols, or events which are grouped

together on the basis of shared characteristics and which

can be referenced by a particular name or symbol." Novak and

Gowin (1984) simply define concept as a regularity in events

or objects designated by some label. Gagne et al. (1988)

defines "a concept is a capability that makes it possible

for an individual to identify a stimulus as a member of a

class having some characteristic in common, even though such stimuli may otherwise differ from each other markedly." He

also identifies a concrete concept as an object property or

object attribute and a defined concept as an individuals

ability to demonstrate the meaning of some particular class


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of objects, events, or relations.

Concept Learning

An important type of learning is cognitive learning,

which is Ausubel's primary theory in cognitive processes.

Cognitive learning results in organized storage of

information in the learner's brain and this organized

complex is referred to as cognitive structure. The most

important concept in Ausubel's theory is what he describes

as meaningful learning. Meaningful learning occurs when new

information is linked to existing relevant concepts in the

learner's cognitive structure. Rote learning, on the other

hand, is also possible to learn new information with little

or no linkage to existing elements in cognitive structure

(Thorsland & Novak, 1974; Novak, 1976).

In the course of meaningful learning, individuals must

choose to relate new information to relevant concepts and

propositions they already know. The existing relevant

concept in cognitive structure is called the subsuming

concept or subsumer. The linkage of new information with a

relevant subsumer in the process of meaningful learning is

the course of subsumption. For a period of time, the new

information learned will no longer be dissociable from the

subsuming concept. This case is called obliterative

subsumption. After obliterative subsumption, the residual


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concept remains and much of the growth that occurred during

subsumption is retained. Therefore, the remaining concept is

strengthened and more capable of facilitating new meaningful

learning in the future (Novak,1976).

As the subsumption process proceeds, existing concepts

become more elaborated or more differentiated; that is,

meaningful learning is a continuous process wherein new

concepts gain greater meaning as new relationships with

previously learned, relevant concepts are acquired. Thus,

concepts are always being learned, modified, and made more

explicit and more inclusive as they become progressively

differentiated (Novak,1976; Novak & Gowin, 1984).

In the process of learning and concept differentiation,

conflicting meanings may arise and the process by which

conflicting meanings between concepts are clarified is

known as integrative reconciliation (Novak 1976, 1979; Novak

& Gowin 1984).

Meaningful learning incorporates new knowledge into the

cognitive structure of our minds non-arbitrarily and

substantively (Novak, 1979). However, each student will form

his/her own idiosyncratic meaning for the concept, and most

of the new concepts will be achieved through reception learning (Ausubel et al., 1978). If the reception learning

is to be meaningful, the learner must form unique linkages

between the concepts s/he already has and the new

descriptions of regularities that are to be learned. Thus,


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meaningful learning is always idiosyncratic, and in this

sense the learner "discovers" the meaning of all concepts by

the nonarbitrary way in which s/he learns the new concepts.

As the study of human learning has proceeded, Gagne

(1988) indicates that a whole set of factors that influence

learning may be called the conditions of learning. Some of

these conditions pertain to the stimuli that are external to

the learner. Others are internal conditions that are sought

within the individual learner.

Advance Organizer

Advance organizers are introductory material at a high

level of abstraction, generality, and inclusiveness and they

facilitate meaningful verbal learning and retention

(Ausubel, 1960; Ausubel & Fitzgerald, 1961).

Two different ways that advance organizers facilitate

the incorporability and longevity of meaningful verbal

material: (1) the organizers explicitly draw upon and

mobilize whatever relevant subsumers are already established

in the learner's cognitive structure and make them part of

the subsuming entity, (2) the organizers at an appropriate

level of inclusiveness provide optimal anchorage. Thus, the more unfamiliar the learning material, the more inclusive or

highly generalized the subsumers must be in order to be as

proximate as possible to the degree of conceptualization of

the learning task. If appropriately relevant and proximate


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subsuming concepts are not available, the most dependable

way of helping retention is to introduce the appropriate

subsumers and make them part of cognitive structure before

the actual presentation of the learning task. The introduced

subsumers become advance organizers for the reception of new

material (Ausubel, 1960). Moreover, the organizer better

enables the learners to put their background knowledge to

effective use in structuring the unfamiliar new material

(Ausubel & Fitzgerald, 1962).

By using Ausubel's theory about the ideas of

subsumption as a promising base for research formulation,

Novak (1971) found that advance organizers can facilitate

learning before students have the available subsumers, and

effective instruction for meaningful reception learning

could benefit by use of advance organizers in sequences with

instruction. Thus, a hierarchical series of organizers would

be planned into the instructional sequence. Novak (1976)

also uses "cognitive bridges" to emphasize the "linking" or

"bridging" function of "advance organizers". Short segments

of learning material can be used to provide guidance to the

student by establishing appropriate subsuming concepts so

that new concepts can be assimilated in the cognitive

structure for meaningful learning. Moreover, the key

concepts in the new material and their subordinate or

superordinate relationship to concepts the learner already


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has can be helpful as cognitive bridges.

26

Concept Map

Concept mapping is a model to demonstrate the nature of

concept learning to students (Novak, 1980, 1981; Novak,

Gowin & Johansen, 1983). Concept maps are intended to

represent meaningful relationships between concepts in the

form of propositions, which are two or more concept labels

linked by words in a semantic unit (Novak & Gowin, 1984). A

simple method for constructing concept maps is to supply

students with a list of related concepts and have them

construct a map, placing the most inclusive, most general

concept at the top and then showing successively less

inclusive concepts at lower positions on a hierarchy. Novak

et al. (1984) indicate that this idea of hierarchical

structure incorporates Ausubel's concept of subsumption,

namely that new information often is relatable to and

subsumable under more general, more inclusive concepts. The

hierarchical structure can also show the set of

relationships between a concept and other concepts

subordinate to it. If sections of a concept map are too

general or too specific, the hierarchical structure of it indicates either misunderstanding or the need for more

careful integration of superordinate and subordinate

concepts.

Concepts are always being learned, modified, and made


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more explicit and more inclusive as they become

progressively differentiated. Novak et al. (1984) state when

concept maps for one topic are cross linked to concept maps

for other related topics, progressive differentiation of

concepts is enhanced. Because the positive emotional

experience that derives from meaningful learning is a major

source of sustained intrinsic motivation for learning, Novak

et al. (1984) state that progressive differentiation of

concepts through concept mapping can provide emotional as

well as cognitive rewards, both in the short term and,

especially, in the long term.

When the learner recognizes new relationships between

related sets of concepts or propositions, integrative

reconciliation in the process of meaningful learning is

occurring. Novak et al. (1984) suggest that concept maps

that show valid cross links between sets of concepts that

might otherwise be viewed as independent, can suggest

learners' integrative reconciliation of concepts.

Concept Mao and Misconception

Because concept maps are an explicit, overt

representation of the concepts and propositions a person

holds, they allow teachers and students to exchange views on

checking propositional linkage or recognizing missing

linkages between concepts. More importantly, because concept

maps contain externalized expressions of propositions, they


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are effective tools for showing misconceptions.

Misconceptions are usually signaled either by a linkage

between two concepts that leads to a clearly false

proposition or by a linkage that misses the key idea

relating two or more concepts (Novak & Gowin, 1984) .

Concept Map and Evaluation

Concept mapping is a technique which allows the student

to demonstrate what he or she know in a visual form. Concept

maps help learners identify the key concepts to be learned,

and show links between what is to be learned and what he or

she already knows. Thus scoring systems have been designed

for concept maps, and the basis for the systems is the

quantification of meaningful learning relative to some

discipline area. Credit is given not only for the number of

valid propositions or concept linkages, but even more

importantly, for the number of valid hierarchical levels

(Novak & Gowin, 1984; Ridley & Novak, 1988).

Concept Map and Teaching

Using concept mapping techniques, in an introductory

biology course for biology majors, Arnandin, Mintzes, Dunn,

and Shager (1984) trained students to map, tested their pre

instruction maps and post-instruction maps, and assessed

their maps. They advanced the following claims made on behalf of

concept mapping: (1) concept mapping helps students


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understand what meaningful learning is, (2) concept mapping

facilitates meaningful learning, (3) concept mapping is an

effective study technique, (4) concept mapping provides a

useful evaluation tool, and (5) concept mapping is a useful

tool for organizing and sequencing instruction.

Ault (1985) introduced concept mapping as a study

strategy in an earth science course and indicated that

concept mapping is one strategy for solving the problem of

why students often learn so little. Judiciously used by

either instructors planning lectures or students preparing

for an examination, concept mapping enhances opportunities

for meaningful learning. However, in nonsupportive settings,

Ault indicates that students' tolerance for mapping

exercises will fade rapidly. And, grading practices can

thoroughly undermine the value of concept mapping by

reinforcing rote learning. Ault cautions that students

should not be asked to memorize instructor-prepared maps.

Moreover, students often do not feel comfortable working

within highly interconnected systems of thought and some

believe they must remember information precisely in the form

presented or be penalized. Thus the rote knowledge in novel

contexts becomes painfully evident. Ault suggests that meaningful learning requires uncompromising commitment by

both teachers and students to an understanding of structure

in knowledge, and concept mapping skill leads in this

direction.


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In order to actively promote meaningful learning,

Cliburn (1986, 1990) uses teacher-made concept maps as

potential applications of the advance organizer. He uses

whole-unit maps to represent the unit's conceptual framework

and then forms this large-scale map. A series of more

specific, higher-resolution maps can be drawn to show more

detail, result ing in a nested set of conceptual maps for the

unit. Furthermore, he makes a color-coded composite map

joining all the individual concept maps and posts it on the

classroom bulletin board. After the formal study of the

concept map, Cliburn found students who were taught by using

concept maps learned better and retained the material

better. Thus the effectiveness of concept maps promoting

long-term retention is strongly confirmed.

Some Alternate Assessments of Cognitive Structure

Shavelson (1974) presents a model of human information

processing which can be divided into two general components:

perception and memory. In the memory component, long-term

memory (LTM) and a retrieval and decision process serve to

define the cognitive structure. Two measurement methods that

retrieve the student's representation of a subject-matter

structure from his cognitive structure are Word-Association

Method and Graph-Construction Method.

In the Word-Association Method, the student's list of

responses to each stimulus word in a test are scored through


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their number, type (Shavelson, 1973), or overlap (Shavelson,

1974). Garskof and Houston (1963) provided a method of the

Relatedness Coefficient (RC) to define the relatedness of

each two words in a set of associates. This method was used

to judge the physics concepts in the subject matter

(Johnson, 1967); Preece, 1976). Then the observed

coefficients of all pairs of concepts can be analyzed by

using factor analysis, multidimensional scaling (Kruskal,

1964; Davison, 1983), or hierarchical cluster analysis

(SPSS, 1990) to present the student's cognitive structure.

Gussarsky and Gorodetsky (1988) use the method of

constrained word associations to gain knowledge on the

chemical equilibrium concept.

In the Graph-Construction Method, the student is given

a list of key words and asked to build a linear tree graph by connecting pairs of words. The distances between all

pairs of words on the graph are analyzed by various scaling

techniques the same as in the word-association method, in

order to examine the student's cognit ive structure (Waern,

1972; Shavelson, 1974).

Diekhoff and Diekhoff (1982) state that an instructor's

numerical judgments of all possible pairs of key concepts

selected from a knowledge domain should be useful in

conveying structural information to students. The

instructor's judgments can be analyzed through principal


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components analysis (SPSS, 1990) to translate relationship

judgments into distance. These distances are used in

creating a graphic array of concept-points in space, called

a "cognitive map".

Jonassen (1984) develops a technology, Pattern Notes,

for analyzing and classifying the relational links between

concepts. To construct a pattern note, a primary subject is

first identified and written in block letters in the center

of a blank sheet of paper, and a box drawn around it. Next,

the student free associates about the subject, thinking

about the key related concepts, and writes them on lines

connecting them to the box. The number of lines between two

concepts may be summarized in a distance matrix and then

they are analyzed by using the method of multidimensional

scaling. As a result, the structure of conceptual

relationships derived by the Pattern Notes test and the Word

Associat ion test are statistically and visually very similar

(Jonassen, 1987).

Teaching Concept

Concept Classification

Most studies of conceptual behavior have dealt mainly with the characterization of the specific concept to be

learned. The general form of solution (rule) has been simple

and familiar, e. g., a conjunction of attributes, and has

been described for the learner during preliminary


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instructions and/or practice problems. Under these

circumstances, the learning task can be described as

attribute identification and the rule relating the relevant

attributes given has received little attention. Thus, a rule

learning (RL) task based on the conceptual rules which are

conjunction, inclusive disjunction, joint denial, and

conditional is constructed and concerned with separable and

unique behaviors. In the RL task, changes in rule difficulty

are a function of the acquisition by learners of a stimulus-

coding strategy, and the strategy reduces a large and

potentially unlimited stimulus population to four classes:

(a) both, (b) the first but not the second, (c) the second

but not the first, and (d) neither of the two given relevant

attributes. In general, these classes are referred to as TT,

TF, FT, and FF in a bidimensional truth table. Each bidimensional rule can map the stimulus classes uniquely

into the two categories of a concept, positive and negative

instances. Once the strategy is mastered, learners are

merely required to learn the assignment of these four

classes to two response categories. Moreover, the

acquisition of a stimulus-coding strategy could reduce

differences in rule difficulty and produce apparent positive

interrule transfer effects (Haygood & Bourne, 1965).

In the study of the relationship between unknown

relevant attributes which are conjunctive, inclusive


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a concept class, and a nonexample (item) does not belong to

the class. Then the items in a class are normally

distributed based upon the ease in recognizing an item as

being either an example or a nonexample.

Example and Nonexample

During the process of concept learning, Woolley et al.

(1972) define three independent variables concerned with the

distribution to the examples: probability, matching, and

pairing. Probability levels of examples and nonexamples

presented are determined by their ease of recognition. The

relationships between high/low probability and examples/

nonexamples can be found in Figure 1. That the relationship

between examples and nonexamples is presented continuously

is called matching; that is, an example and nonexample are

matched when they share vir tually all of the same irrelevant

attributes, and differ only in some relevant attributes.

Pairing has two different types: divergent pairing refers to

the case when two examples presented in a sequence differ as

much as possible in their irrelevant attributes; convergent

pairing means two sequential examples differ only slightly

in irrelevant attributes.There are four concept learning outcomes specified in

relationship to the independent variables. If the


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High probability Low probability

Examples An example would be

recognized as belonging to a specified concept class.

An example would be incorrectly classified as belonging to a specified concept class

Nonexamples

A nonexample would be easily rejected as

belonging to a specified concept class.

A nonexample would be incorrectly accepted as belonging to a specified concept class.

Figure 1. Probability Levels of Examples and Nonexamples


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respondents would correctly classify previously

unencountered examples of all probability levels, it is the

case of correct classification. However, when previously

unencountered examples are correctly identified but some low

probability nonexamples are also included as being examples

is called overgeneralization; i. e. a respondent cannot

discriminate between examples and nonexamples and indicates

that the learner's range of acceptance is too great. On the

other hand, when the range of rejection is too great is

called undergeneralization. If the learner variously accepts

or rejects examples and nonexamples of all probability

levels based on their irrelevant attributes, it is

misconception (Woolley & Tennyson, 1972).

In investigating the relationship among examples and

nonexamples, concept learning is most effectively

facilitated as examples present a range from easy to

difficult, subsequent examples are divergent in variable

attributes (irrelevant attributes) from previous examples,

and examples are matched to nonexamples on the basis of

similarity of variable attributes (Tennyson, Woolley &

Merrill, 1972; Tennyson, 1973; Tennyson & Park, 1980).

Moreover, organized sequence of a presentation of examples is more effective than random presentation. (Tennyson, Steve

& Boutwell, 1975).

In a classroom study, Shumway (1971) found that

nonexamples discouraged overgeneralization errors by eighth


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grade students for concepts involving the properties of

mathematical binary operations.

In the study of the effects of an instructional

sequence of all examples and a sequence of examples and

nonexamples on the acquisition of the mathematical concepts

of commutativity, accociativity, distributiv ity , and

homomorphism, Shumway (1973, 1974) found that a sequence of

examples and nonexamples was superior to a sequence of all

examples for the acquisition of these concepts. The results

of these studies are consistent with Tennyson, Woolley, and

Merril l (1972) and Shumway (1972).

Instructional research on concepts in mathematics

(Shumway, 1974, 1977), Poetry (Tennyson, Woolley & Merrill,

1972; Tennyson, Steve & Boutwell, 1975), sentence (Tennyson,

1973), and chemistry crystal structure (Merrill & Tennyson,

1978) suggests that mixtures of examples and nonexamples are

favored over all examples in learning school-related

concepts. In order to identify important variables

influencing the role of nonexamples on logical thinking in

conjunctive feature identification tasks, the all example

treatment was favored over the mixed example and nonexample

treatment with the 1:1 feature frequency condition; the

mixed example and nonexample treatment was favored over the

all example treatment where the frequency of one feature of

the irrelevant dimensions was 9:1 compared to the other


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feature (Shumway, White, Wilson & Brombacher, 1983) . By

using Apple II microcomputer to design a program including

graphics animated to represent chemical and physical changes

which are often observed in qualitative chemical analysis,

White, Wilson, and Shumway (1981) also found the same

results.

In addition to organizing examples and nonexamples in

teaching concepts, additional improvements are suggested to

include: (1) the use of attention focusing devices

(attribute isolation) to focus the student's attention on

the critical attributes, (2) the use of a step-by-step

(algorithmic) presentat ion of the definition, and (3) the

combination of expository presentations with feedback-

accompanied practice (Merrill & Tennyson, 1978).

As to the question of how many examples and nonexamples

in concept teaching should be presented, Tennyson and Park

(1980) indicated that the appropriate number of examples

differs according to the learning characteristics of

individual students.

Concept Teaching Model

In carefully controlled experimental research studies, Merrill and Tennyson (1977) express that most concepts do

not exist in isolation but rather as part of a set of

related concepts. Thus " a concept taxonomy is a diagram

which is constructed to indicate the subordinate,


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superordinate, and coordinate relationships among a set of

related concepts." "When a superordinate concept is divided

into subordinate concepts, the subordinate concepts for a

single superordinate concept are called coordinate

concepts." Using the coordinate concepts selected from

psychology as a task wi th junior and senior high students,

Tennyson, Tennyson and Rothen (1980) examined three

presentation orders of sequencing examples of coordinate

concepts: (1) Simultaneous order, all of the coordinate

concepts were presented concurrently by grouping one example

from each concept in a rational set. Within a rational set

the representative examples had matched variable attributes

and different critical attributes, while between rational

sets the examples had divergent variable attributes; (2)

Collective order, the coordinate concepts are clustered according to shared critical attributes. That is, certain

critical attributes may be identical in various concepts,

with differences between these concepts based on subordinate

concepts; (3) Successive order, there were no groupings;

instead, each concept was presented independently. In terms

of concept learning, the results indicate that students

learn generalization behavior when they are given a range of

variable attributes between rational sets, and that they

learn discrimination behavior when given examples of each

concept within rational sets.


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In the research of concept learning, Tennyson, Chao,

and Youngers (1981) suggest that what is stored in memory is

a bes t example (prototype or clear case) and that learners

match newly encountered examples with that best example.

Thus they used three presentation methods to test concept

attainment: (1) expository, with labels and statements

clearly identifying examples and nonexamples; (2)

interrogatory, with questions requiring students to identify

examples and nonexamples; and (3) expository-interrogatory,

a combination of the first two. Data analyses showed that

learning was facilitated for fourth-grade students by a

presentation method that combined expository statements of

best examples with interrogatives over presentations that

were expository or interrogatory only.

From the extensive research on concept learning in

psychology and education, two processes should be involved

in the concept learning: first, formation in the

individual's memory of a best example and second,

development of the skill to recognize specific attributes of

similarity and difference between and among newly

encountered examples. As to the teaching concept, a good

concept lesson should include the following elements (Jassal & Tennyson, 1982):

1. a concept definition;

2. a best example;

3. an expository set of examples;


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4. an interrogatory set of examples;

5. a classification test.

For purposes of instructional design, the research

presented above have shown that concept learning involves

development of classification skills in generalization and

discrimination and that learners can learn most effectively

if the learners are presented with best examples of the

concept in both expository and interrogatory presentation

forms. In order to test (1) whether formation of conceptual

information may be learned by an instructional presentation

form that focuses attention on a best example or that

clarifies the relationship of the critical attributes, and

(2) the effect of expository examples in facilitat ing the

transition between the encoding of conceptual information

and the development of the classification skill, Tennyson,

Youngers, and Suebsonthi (1983) used the concept of a

regular polygon as a task with third-grade students. The

results of the study show (a) that presentation of best-

examples along with the definition facilitated prototype

formation more than did a presentation of the definition

along with a statement clarifying the relationship of the

critical attributes, and (b) that classification skill was

facilitated more by presentation of both expository and

interrogatory examples compared to an interrogatory-only

presenta tion .


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Based on a programmatic line of research that has

focused on the improvement of concept-learning through the

enhancement of instructional design variables, Tennyson and

Cocchiarel la (1986) present a concept-teaching model which

extends the earlier version of a model presented by Merrill

and Tennyson (1977). In this model, Tennyson et al. (1986)

use current theory and research findings from instructional

systems, cognitive science, and developmental psychology to

indicate two cognitive processes in a concept-learning

model: (a) formation of conceptual knowledge which is formed

in memory by the integrated storage of meaningful dimensions

selected from known examples and the connecting of this

enti ty in a given domain of information; and (b) development

of procedural knowledge which is developed by using

conceptual knowledge to solve domain-specific problems.

Based on this two-phase theory of concept learning, the

concept-teaching model is composed of two fundamental

components of design: (1) the content structure of a given

domain of information and (2) the organization of

instructional design variables related to the use of

specific content structures.

In the content structure variables of the concept-

teaching model, Tennyson et al. (1986) suggest that they

should be analyzed according to two conditions: (1) the

relational structure between concepts in a given domain of

information and (2) the variability of the attribute


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characteristics of each concept in the domain. The

relational structure of concepts are associated with two

classification skills: generalization and discrimination.

When engaging in a content analysis, successive and

coordinate are determined as the basic relations of the

domain's structures. With successive relationships, learning

is limited primarily to the development of generalizations

within a concept class. With coordination relationships,

learning includes the development of skills to generalize

within a concept class and discriminate between concepts.

The second condition, attribute characteristics, affects the

design of an instructional strategy for either coordinate or

successive concepts. Attributes can be thought of as having

constant or variable dimensions. When the definition of a

concept does not change with the context in which it is learned, the concept may be considered as having constant

attributes. On the other hand, a concept may be considered

as having variable dimensions when its definition and

examples tend to change with the context of instruction.

The instructional design variables for concept-teaching

suggested by Tennyson and Cocchiarella (1986) are directly

related to specific cognitive processes in concept-learning.

They are explained in the following:

(1) Label, definition, and context. Labels and

definitions seem to help the learner establish in memory the


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possible connection between existing necessary knowledge and

the to-be-learned concepts, but definitions play a secondary

role in concept learning because learners rarely learn well

from only definitions and most often verbatim definitions

are not encoded in memory. The relationship between a label

and the concept represented by a label often in an

unhelpful, arbitrary association established by cultural

convention. However, concept labels can assist learners in

conjuring up the concept in memory by relat ing the concept

to what is already known. A concept may have one label but

several definitions, a specific context for the presentation

of a problem within the appropriate situation or domain can

provide information to establish critical attributes of the

concept.

(2) Best examples. The best example represents an

average, central, or prototypical form of a concept, but it

is not necessarily a quantitative value. Because a best

example sets up the initial encoding of conceptual

knowledge, it should be an example that can conjure up in

memory existing knowledge structures.

(3) Expository examples. Expository examples provide

the dimensionali ty or richness of conceptual knowledge. In the process of teaching the examples, learners also acquire

the initial procedural knowledge for using the conceptual

knowledge.

(4) Interrogatory examples. Interrogatory examples


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further enhance the development of procedural knowledge.

(5) Attribute elaboration. This des ign variable assists

the learner in establishing the conceptual knowledge

structure of a given concept and its relationship in a

schematic network. There are two devices used for attribute

elaboration: (1) attribute prompting which is a tool to help

learners establish the conceptual knowledge by focusing

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