The effects of a multimedia constructivist environment.pdf

THE EFFECTS OF A MULTIMEDIA CONSTRUCTIVIST ENVIRONMENT ON

STUDENTS’ ACHIEVEMENT AND MOTIVATION IN THE LEARNING OF CHEMICAL FORMULAE

AND EQUATIONS

VICKNEASVARI A/P KRISHNASAMY

UNIVERSITI SAINS MALAYSIA

2007

THE EFFECTS OF A MULTIMEDIA CONSTRUCTIVIST ENVIRONMENT ON STUDENTS’ ACHIEVEMENT AND MOTIVATION IN THE LEARNING OF

CHEMICAL FORMULAE AND EQUATIONS

by

VICKNEASVARI A/P KRISHNASAMY

Thesis submitted in fulfilment of the requirements

for the degree of

Doctor of Philosophy

June 2007

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ACKNOWLEDGEMENTS

I would like to express my sincere appreciation and gratitude to my supervisor,

Associate Professor Dr. Toh Seong Chong for his professional expertise, invaluable

guidance, persistant encouragement, patience, incisive comments, enthusiasm and

generous contribution of his valuable time in supervising the design of the multimedia

courseware as well as conducting of this research. I would also like to thank my co-

supervisor, Dr. Irfan Naufal Umar, for his unwavering encouragement, patience,

consistent guidance, great assistance and inspiration in the development of my thesis.

Special appreciation is also directed to the members of the research proposal

committee, Associate Professor Dr. Merza Abbas, Associate Professor Dr. Nor Azilah

Ngah, Associate Professor Dr. Zurida Ismail and Dr. Mona Masood for their reviews and

comments on early drafts that were most useful.

Gratitude and sincere appreciation are also extended to the Malaysian Education

of Planning and Research Department and the State Education Department of Penang

for granting me the permission to conduct my research in the schools. I am grateful to

Mr. Koay Teng Theam and Ms. Komala, senior science teachers, for assessing the

contextualisation of the items in the instrument and the content validation of the

courseware as well as the pretest and posttest.

My sincere thanks are also extended to Ms. Josephine for proof reading the final

draft of the thesis. I am also grateful to my colleagues, Mr Zuber Ali and Ms. Asha Rani

Suppiah, for translating the content of the courseware from Bahasa Melayu to English.

It is a pleasure for me to acknowledge the principals, teachers and students of

SMK Kampong Kastam, Butterworth, SMK Dato Onn, Butterworth and SMK Teluk Air

Tawar, Butterworth, for helping in the pilot study as well as the actual study. Their

cooperation, endurance and involvement in data collection are very much appreciated.

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Above all, I express sincere gratitude to my friends who faithfully gave me

encouraging support throughout the duration of my writing. My sincere thanks are also

conveyed to my father, Mr. R.Krishnasamy for his love and confidence that encouraged

my pursuit. I also wish to dedicate this research to my late mother, who is the great

inspiration in my life. Finally, I wish to thank my husband, Mr. E.Gnasegaran, my niece,

Yaashnipriya, and my children, Jihan Prasaad, Narressh Guhan and Hemanth Avinash,

for their love, sacrifice, patience and moral support.

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CONTENTS

Acknowledgements (ii) Table of Contents (iv) Appendices (vii) List of Tables (viii) List of Figures (x) Abstrak (xii) Abstract (xiv) Table of Contents Chapter 1: Statement of Problem 1 1.0 Introduction 1 1.1 Background of Research 2 1.2 Statement of the Problem 5 1.3 Research Objectives 13 1.4 Research Questions 13 1.5 Research Hypotheses 15 1.6 Significance of the Study 18 1.7 The Theoretical Framework 20 1.8 The Research Framework 21 1.9 Limitations 22 1.10 Operational Definitions 23 1.11 Conclusion 27 Chapter 2: Review of Literature 28 2.0 Introduction 28 2.1 Conceptual Difficulties in Chemistry Education 28 2.2 Learning Theories and Instructional Design 32

2.2.1 Introduction 32 2.2.2 Behaviourism and Instructional Design 33 2.2.3 Cognitivism and Instructional Design 35 2.2.4 Constructivism and Instructional Design 38

2.3 The Learning Environment 47 2.3.1 The Objectivist Learning Environment 47 2.3.2 The Constructivist Learning Environment 49

2.4 Literature Review on Variables 51 2.4.1 Multimedia Learning and Achievement 51 2.4.2 Multimedia Learning and Motivation 55 2.4.3 Ability Levels 59 2.4.4 Cognitive Styles 60 2.4.5 Gender 62

2.5 The Theoretical Framework 64

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2.5.1 Model for Designing the Constructivist Learning Environment (CLE) 64 2.5.1.1 Question/Case/Problem/Project 65 2.5.1.2 Related Cases 68 2.5.1.3 Information Resources 68 2.5.1.4 Cognitive Tools 69 2.5.1.5 Conversation and Collaboration Tools 70 2.5.1.6 Social/Contextual Support 70 2.5.1.7 Supporting Learning In CLEs 70

2.5.2 e-learning and Instruction 73 2.5.3 The Model for Motivation 82

2.6 Evaluation Based on the Constructivist Framework 83 2.7 Conclusion 84 Chapter 3: Methodology 85 3.0 Introduction 85 3.1 Research Design 85 3.2 Variables 87 3.3 Research Sample and Sampling 89 3.4 Instructional Materials 90 3.5 Research Instruments 90

3.5.1 Pretest and Posttest 91 3.5.2 The Cattell “Culture Fair’ Intelligence Test 92 3.5.3 The Instructional Materials Motivation Scale (IMMS) 92 3.5.4 The Group Embedded Figures Test (GEFT) 92

3.6 Research Procedure 93 3.7 Procedures to Ensure External and Internal Validity of the Study 94 3.8 Data Analysis 95 3.9 Conclusion 96

Chapter 4: Courseware Development 97

4.0 Introduction 97 4.1 Courseware Development Model for Multimedia Instruction 97

4.1.1 Models of Instructional Systems Design 97

4.1.2 Gagne’s Nine Events of Instruction 100 4.1.2.1 Gaining Attention 101 4.1.2.2 Informing learners of objectives 103 4.1.2.3 Stimulating Recall of Prior Learning 103 4.1.2.4 Presentation the Content 104 4.1.2.5 Providing Learning Guidance 104 4.1.2.6 Eliciting Performance (Practice) 105 4.1.2.7 Provide Feedback 106 4.1.2.8 Assessing Performance 108 4.1.2.9 Enhancing retention and Transfer of Learning 109

4.2 The Development of the Courseware 110 4.2.1 The MCI Courseware 110 4.2.2 The MOI Courseware 117

4.3.3 Differences between the MCI and MOI 118 4.3 Evaluation of the Courseware 121 4.4 Pilot Test 122

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4.5 Conclusion 123 Chapter 5: Results 124 5.0 Introduction 124 5.1 Descriptive Statistics of the Variables 125 5.2 Inferential Statistics 127

5.2.1 Pretest Score Analysis for Independent and Moderator Variables 128 5.2.2 Achievement Score Analysis for the Independent and

Moderator Variables 128 5.2.3 ANOVA of Achievement Mean Scores for the Moderator

Variables on Multimedia Approaches 131 5.2.4 IMMS Score Analysis for Independent and Moderator

Variables 139 5.2.5 ANOVA of IMMS Mean Scores for the Moderator

Variables on Multimedia Approaches 142 5.3 Summary of the Findings 149 Chapter 6: Discussions, Implications and Conclusions 154 6.0 Introduction 154 6.1 Effects of the MCI and MOI Approach on Achievement 155 6.1.1 Effects of the MCI and the MOI Approaches on

Ability Levels in Achievement 158 6.1.2 Effects of the MCI and MOI Approaches on

Cognitive Styles in Achievement 159 6.1.3 Effects of the MCI and MOI Approaches on

Gender in Achievement 162 6.2 Effects of the MCI and MOI approaches on IMMS Score 164 6.2.1 Effects of the MCI and the MOI Approaches on

Ability Levels in Motivation 165 6.2.2 Effects of the MCI and the MOI Approaches on

Cognitive Styles in Motivation 167 6.2.3 Effects of the MCI and the MOI Approaches on

Gender in Motivation 168 6.3 Implications for Educators 169 6.4 Implications for Future Research 171 6.5 Summary and Conclusion 172 References 174

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Appendices 186 Appendix A Mathematics and Science Achievement of Eight-Graders in 1999 186 Appendix B Average Mathematics Scale Scores of Eight-Graders

Students by Country 2003 187

Appendix C Average Science Scale Scores of Eight-Graders Students by Country 2003 188

Appendix D Table of Specifications For The Pretest 189 Appendix E Table of Specifications For The Posttest 190 Appendix F Curriculum Specifications Chemistry Form Four 191 Appendix G The Conceptual Map of “Chemical Formulae and Equations” 197 Appendix H Pretest on Chemical Formulae and Equations 198 Appendix I Posttest on Chemical Formulae and Equations 204 Appendix J Cattell Culture Fair Intelligence Test 210 Appendix K Instructional Materials Motivation Scale (IMMS) 220 Appendix L Group Embedded Figures Test (GEFT) 223 Appendix M Summary of Lesson’s Protocol for the teachers to conduct MCI 230 Appendix N Summary of Lesson’s Protocol for the teachers to conduct MOI 231 Appendix O Reliability of Pilot Test 232

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List Of Tables Table 2.1 Five Types of Content in e-Learning 74 Table 2.2 Inform and Perform e-Learning Goals 75 Table 4.1 The Conditions of Learning (Gagne, 1985) 101 Table 4.2 Differences between MCI and MOI 120 Table 5.1 Mean Scores and Standard Deviations of Cognitive

Styles and Ability Levels 125

Table 5.2 Descriptive Statistics for Independent and Moderator Variables 126 Table 5.3 Descriptive Statistics for the Multimedia Approaches 127 Table 5.4 ANOVA of Achievement Mean Scores for School

Type and Multimedia Approaches 128

Table 5.5 ANOVA for Multimedia Approaches and Moderator Variables 129 Table 5.6 Mean Scores and Standard Deviations of Achievement Score for the Independent and Moderator Variables 130 Table 5.7 Descriptive Statistics of Achievement Mean Scores of

the Ability Levels on Multimedia Approaches 132

Table 5.8 Descriptive Statistics of Achievement Mean Scores of the Cognitive Styles on Multimedia Approaches 132

Table 5.9 Descriptive Statistics of Achievement Mean Scores of Gender on Multimedia Approaches 133

Table 5.10 Levene Test – Test of Homogeneity of Variance for Ability Level 133

Table 5.11 ANOVA of Achievement Mean Scores of the Ability Level on Multimedia Approaches 135 Table 5.12 ANOVA of Achievement Mean Scores of the Cognitive Styles on Multimedia Approaches 136 Table 5.13 ANOVA of Achievement Mean Scores of Gender on Multimedia Approaches 138 Table 5.14 ANOVA of IMMS Score for Multimedia Approaches and

Moderator Variables 139

Table 5.15 Mean Scores and Standard Deviations of Achievement Scores for the Independent and Moderator Variables 140

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Table 5.16 Levene Test – Test of Homogeneity of IMMS Score Variances for the Moderator Variables 142

Table 5.17 ANOVA of IMMS Mean Scores of the Ability Levels on Multimedia Approaches 144

Table 5.18 Descriptive Statistics of IMMS Mean Scores of the Ability Levels on Multimedia Approaches 144

Table 5.19 ANOVA of IMMS Mean Scores of the Cognitive Styles on Multimedia Approaches 145 Table 5.20 Descriptive Statistics of IMMS Mean Scores of the Cognitive Styles on Multimedia Approach 146 Table 5.21 ANOVA of IMMS Mean Scores of Gender on Multimedia Approaches 147 Table 5.22 Descriptive Statistics of IMMS Mean Scores of Gender on Multimedia Approaches 148 Table 5.23 Summary on the Achievement Score 149 Table 5.24 Summary on the Motivation Score (IMMS Score) 150

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List of Figures Figure 1.1 The Research Framework for Multimedia Instruction

(Jonassen, 1999) 22 Figure 2.1 The Constructivist Learning Environment Model (1999) 65

Figure 2.2. The e-Learning Model by Clark and Mayer (2003) 73 Figure 2.3 Access of Visual and Auditory Channels with Presentation

of Narration and Graphics (Clark & Mayer, 2003) 77

Figure 3.1 The Achievement Score and Experimental and Control Group Design 86

Figure 3.2 Experimental and Control Group Designs by Low-Ability

and High-Ability, Field-Dependent and Field-Independent and Male and Female 87

Figure 3.3 The Relationship between Variables 88 Figure 4.1 The Model for the Design and Development of the MCI and MOI

by Alessi and Trollip (2001, p.410) 98

Figure 4.2 The Animated Title Screen with Sound Effects 102

Figure 4.3 Simulating Recall of Prior Learning 103 Figure 4.4 Video Presentation - An example of a Different Learning

Modality 104 Figure 4.5 Case Studies as Guidance Strategies 105 Figure 4.6 Practice in the MCI as Problem Statement Questions and Related Cases 106 Figure 4.7 Practice in the MOI as Sample Exercises 106 Figure 4.8 Feedback in the MCI 107 Figure 4.9 Feedback in the MOI 107 Figure 4.10 Assessing performance in the MOI 108 Figure 4.11 Evaluation in the MOI 109 Figure 4.12 Problem Statement with Some Guidance 110 Figure 4.13 Main Menu Buttons 111

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Figure 4.14 Five Support Tools and Music Button 112 Figure 4.15 The Problem Statement Menu 113 Figure 4.16 Positive Feedback and “Next” button 114 Figure 4.17 Negative Feedback 114 Figure 4.18 Related Cases 115 Figure 4.19 Related Cases for the Sub Titles 116 Figure 4.20 The Main Menu for the MOI Courseware 117

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ABSTRAK

KESAN PERSEKITARAN MULTIMEDIA BERASASKAN PENDEKATAN

KONSTRUKTIVIS TERHADAP PENCAPAIAN DAN MOTIVASI PELAJAR DALAM

PEMBELAJARAN FORMULA DAN PERSAMAAN KIMIA

Penyelidikan ini dijalankan bagi mengkaji kesan persekitaran multimedia

berasaskan konstruktivisme terhadap pencapaian dan motivasi pelajar Tingkatan Empat

dalam pembelajaran ‘Formula dan Persamaan Kimia’. Koswer Instruksi Multimedia

Konstruktivis (MCI) dan Instruksi Multimedia Objektivis (MOI) telah dibangunkan.

Seramai 80 pelajar menerima MCI manakala 89 pelajar menerima MOI. Kajian

eksperimen kuasi ini menggunakan reka bentuk faktorial 2 x 2. Pembolehubah bebas

melibatkan pendekatan multimedia, iaitu MCI dan MOI, manakala pembolehubah

bersandar merupakan pencapaian serta motivasi pelajar. Tiga pembolehubah moderator

telah digunapakai, iaitu tahap kebolehan pelajar (kebolehan rendah, LA atau kebolehan

tinggi, HA), gaya kognitif (field-dependent, FD atau field-independent, FI) serta jantina

(lelaki atau perempuan) .

Kajian ini mendapati (i) pelajar MCI memperoleh pencapaian lebih baik secara

signifikan dan lebih bermotivasi secara signifikan pelajar MOI, (ii) pelajar HA mencapai

pencapaian lebih baik secara signifikan dan lebih bermotivasi secara signifikan

berbanding pelajar LA, (iii) pelajar FI tidak memperoleh pencapaian lebih baik secara

signifikan tetapi lebih bermotivasi secara signifikan berbanding pelajar FD, (iv) pelajar

lelaki juga tidak memperoleh pencapaian lebih baik secara signifikan tetapi lebih

bermotivasi secara signifikan berbanding pelajar perempuan, (v) pelajar HA memperoleh


berbanding pelajar LA dalam MCI, (vi) pelajar HA menggunakan MCI memperoleh

pencapaian lebih baik secara signifikan tetapi tidak bermotivasi secara signifikan

berbanding pelajar HA menggunakan MOI, (vii) pelajar LA menggunakan MCI tidak

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memperoleh pencapaian lebih baik secara signifikan walaupun lebih bermotivasi secara

signifikan berbanding pelajar LA menggunakan MOI, (viii) pelajar FI memperoleh


berbanding pelajar FD dalam MCI, (ix) pelajar FI menggunakan MCI memperoleh

pencapaian lebih baik secara signifikan tetapi tidak bermotivasi secara signifikan

berbanding pelajar FI menggunakan MOI, (x) pelajar FD menggunakan MCI tidak

memperoleh pencapaian lebih baik secara signifikan tetapi lebih bermotivasi secara

signifikan berbanding dengan pelajar FD menggunakan MOI, (xi) pelajar lelaki tidak

memperoleh pencapaian lebih baik secara signifikan tetapi lebih bermotivasi secara

signifikan berbanding pelajar perempuan dalam MCI, (xii) pelajar lelaki menggunakan

MCI memperoleh pencapaian yang signifikan tetapi tidak bermotivasi secara signifikan

berbanding pelajar lelaki menggunakan MOI, dan (xiii) pelajar perempuan menggunakan

MCI memperoleh pencapaian lebih baik secara signifikan tetapi tidak bermotivasi secara

signifikan berbanding pelajar perempuan menggunakan MOI. Secara umumnya,

dapatan-dapatan pencapaian dan motivasi pelajar menunjukkan kesan positif yang

dibawa persekitaran multimedia berasaskan konstruktivis terhadap pembelajaran

“Formula dan Persamaan Kimia”.

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ABSTRACT

THE EFFECTS OF A MULTIMEDIA CONSTRUCTIVIST ENVIRONMENT ON

STUDENTS’ ACHIEVEMENT AND MOTIVATION IN THE LEARNING OF CHEMICAL

FORMULAE AND EQUATIONS

This study is conducted to examine the effects of a multimedia constructivist

environment on Form Four students’ achievement and motivation in the learning of

“Chemical Formulae and Equations”. Multimedia Constructivist Instruction (MCI) and

Multimedia Objectivist Instruction (MOI) courseware were developed. The MCI was

assigned to 80 students whereas the MOI was assigned to 89 students. This quasi-

experimental study employed a 2 x 2 factorial design. The independent variables were

the multimedia approaches, i.e. the MCI and the MOI, whereas the dependent variables

were the students’ achievement and motivation. Students’ ability levels (high-ability, HA

or low-ability, LA), cognitive styles (field-independent, FI or field-dependent, FD) and

gender (male or female) were the moderator variables.

This study found that (i) the MCI students performed significantly better and were

significantly more motivated than the MOI students, (ii) the HA students performed

significantly better and were significantly more motivated than the LA students, (iii) the FI

students did not perform significantly better but were significantly more motivated than

the FD students, (iv) the male students did not perform significantly better but were

significantly more motivated than the female students, (v) the HA students performed

significantly better and were significantly more motivated than the LA students in MCI,

(vi) the HA students using MCI performed significantly better but were not significantly

more motivated than the HA students using MOI, (vii) the LA students using MCI did not

perform significantly better but were significantly more motivated than the LA students

using MOI, (viii) the FI students performed significantly better and were significantly

more motivated than the FD students in MCI, (ix) the FI students using MCI performed

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significantly better but were not significantly more motivated than the FI students using

MOI, (x) the FD students using MCI did not perform significantly better but were

significantly more motivated than the FD students using MOI, (xi) the male students did

not perform significantly better but were significantly more motivated than the female

students in MCI, (xii) the male students using MCI performed significantly better but

were not significantly more motivated than the male students using MOI, and (xiii) the

female students using MCI also performed significantly better but were not significantly

more motivated than the female students using MOI. Overall, these findings support the

positive effect of multimedia constructivist environment on the learning of “Chemical

Formulae and Equations”.

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CHAPTER 1

INTRODUCTION

1.0 Introduction

Malaysia, as a fast developing nation, is moving towards realising her vision

to be a progressive and fully developed country by the year 2020. One of the

challenges of Vision 2020 is “to establish a scientific and progressive society, a

society that is innovative and forward-looking, one that is not only a consumer of

technology but also a contributor to the scientific and technological civilisation of the

future” (Information Department of Malaysia, 1997). The emphasis on science and

technology has introduced multimedia as one of the delivery systems in schools.

With the advent of the knowledge-economy (k-economy) and globalisation,

an effective instructional design is pivotal. Kumar and Helgeson (2000) noted that

science education reform emphasised the need for integrating computer technology

into learning, teaching and assessing. They felt that there are possibilities of

providing a better education in science by modifying the teaching and learning of

science, with special emphasis on computer technology such as computer-based

laboratories, interactive videos, simulations, intelligent tutors, the Internet and the

World Wide Web. Since Malaysia is committed to developing and providing world-

class educational systems, an effective instructional medium, with the incorporation

of an appropriate learning environment in various fields, is needed.

This study intended to design and develop a multimedia constructivist

environment to solve the learning difficulties in chemistry. It has been reported that

chemistry is a subject that contains many abstract concepts that are difficult to

understand (Chan, 1988; Gabel, 1999; Muth & Guzman, 2000; Yalcinalp, Geban &

Ozkan, 1995). The mole concept is the fundamental concept in quantitative

chemistry that poses understanding difficulties among students and has been

identified as a difficult concept in chemistry (Cervellati et al., 1982; Friedel &

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Maloney, 1992; Gabel & Sherwood, 1984; Gabel, Sherwood & Enochs, 1984;

Yalcinalp et al., 1995).

1.1 Background of Research

Chemistry has been documented as a difficult subject and chemistry courses

are generally taught at a level of abstraction (Chan, 1988; Gabel, 1999; Muth &

Guzman, 2000; Yalcinalp et al., 1995). The abstract knowledge of chemistry is said

to have an influence on the learning difficulties of students (Yalcinalp et al., 1995).

In Malaysia, the report on the performance of Sijil Pelajaran Malaysia (SPM)

2003 and 2004 (Lembaga Peperiksaan, 2003, 2004) concluded that the topic

“Chemical Formulae and Equations”, “The Atomic Structure”, “The Periodic Table”

and “Chemical Bonding” are the basics in chemistry that need to be emphasised.

The mole concept is the main concept that is taught in the topic of “Chemical

Formulae and Equations”. Therefore, the teaching and learning of chemistry should

be improvised to enhance a better understanding of the mole concept because it

involves the concrete foundation in chemistry.

The Ministry of Education has taken steps to improve the teaching and

learning of science in the schools. These include the introduction of the Multimedia

Super Corridor (MSC) in 1997 to accelerate Vision 2020, that is, to transform

Malaysia into a knowledge-based society (Ministry of Education, 1997). The need for

Malaysia to make the transformation from an industrial to an information-based

economy led to the Smart School Flagship Application (Ministry of Education,

Malaysia, 1997). This was to produce a technologically-literate thinking workforce

who is able to perform in a global environment as well as to use information age tools

and technology to improve productivity. The Smart School is also a learning

institution that has been systematically reinvented in terms of its teaching-learning

practices and school management in order to prepare students to practise self-

assessed and self-directed learning focusing on individual achievements and

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development (Ministry of Education, Malaysia, 1997). Thus, the introduction of

multimedia has become significant in order to achieve the objective of the Smart

School Flagship Application.

In Malaysia, the application of computers is now infiltrating all levels and

areas of education. Computers, as the technology in the multimedia stream, have

long been introduced to Malaysian schools but students use these computers as a

tool. Many schools use computers for documentation purposes only but in this age

of information technology, the usage of computers as an instructional medium in

schools is more imperative due to the huge volume of information that is changing

rapidly. Thus, most schools are equipped with computer laboratories but they are

hardly used as instructional mediums.

Educators should integrate computers as an instructional medium to facilitate

the teaching and learning process. Multimedia instruction is possible since many

schools are now equipped with computer laboratories. Students are not only

encouraged to be computer literate but are also allowed to surf the Internet to gather

more information to construct their own knowledge. Educators are needed to

facilitate students’ construction of knowledge in a very exciting, presentable and

practical method. Multimedia instruction not only contains the expected components

but also instructs students on an individual basis. This differs from the traditional

instruction used in classrooms where the teacher has to deal with a large number of

students.

Like other science courses, chemistry courses have undergone several

changes in the past few years both in terms of what is taught and how it is taught.

The latest change took place in the year 2000 with the reorganisation of the

Malaysian chemistry syllabus and the integration of the constructivist learning

method. One of the objectives of this reorganisation of the syllabus was to

incorporate the constructivist approach with the learning and teaching of science.

There are various constructivist learning methods that are being introduced, such as

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inquiry learning, mastery learning, problem-solving and discovery learning. Although

teachers have been provided with a substantial amount of introduction on the various

constructivist learning methods to facilitate the teaching and learning process, they

have not been provided with expertise training to implement these methods. These

teachers still lack constructivist materials and readily accessible models of instruction

that are in line with the student-centred models of instruction to enable novice

teachers to reflect on and change their science teaching praxis.

The reorganisation of the syllabus is also followed by the usage of laptops in

classrooms. The aim of the Ministry of Education is to encourage teachers to use the

laptops to facilitate the teaching and learning process. So, science teachers are now

using the provided laptops as a teaching aid. However, the students still have not

been given a chance to be hands-on with computers. To overcome this problem, the

Ministry of Education had decided to provide all schools their own computer

laboratories. The main intention is for the schools to use computers as an

instructional medium, but the use of computers as a learning tool is left much to be

desired.

Since 2003, the Ministry of Education has supplied laptops to all schools in

Malaysia to enable teachers integrate their usage into the teaching and learning

process. Computer laboratories were installed in many schools and this clearly

shows that the Ministry of Education is emphasising the usage of computers in

education. At present, in most primary and secondary schools in Malaysia,

computers are used primarily to improve literacy and as a teaching aid.

Very few schools have started utilising computers as an instructional medium

due to the inaccessibility of appropriate courseware. The courseware that is now

being widely used at present by government schools in Malaysia is from the

Curriculum Development Centre, Ministry of Education, Malaysia. Based on the

researchers’ expertise in teaching of science and chemistry, these courseware are

used as teaching aids and are developed in a linear manner. If students are

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provided courseware designed in a constructivist environment, they will be

encouraged to explore on their own and at their own pace to construct knowledge.

1.2 Statement of the Problem

Chemistry, as a discipline, has a bright future, and chemistry education

delivers a truly broad scope and integral position of chemistry, not only among the

sciences but also in daily life and human activities in general (Price & Hill, 2004).

However, chemistry has been reported as one of the most difficult subjects to learn

(Chan, 1988; Muth & Guzman, 2000; Stieff & Wilensky, 2002). Stieff and Wilensky

(2002) expressed the view that learning chemistry imposes great demands on

students and teachers; instructors often have to use mathematical formulae,

chemical symbols and scientific measurements simultaneously to illustrate the non-

visible scenario in chemistry. Gabel (1999) also documented that chemistry is a very

complex and difficult subject, and this imposes implications for its teaching. Many

researchers reported that chemistry is an abstract and a formal subject (Chan, 1988;

Gabel, 1999; Muth & Guzman, 2000; Yalcinalp et al., 1995) and thus, it often results

in learning difficulties of students.

Johnstone (2000) noted that much effort has been expended to design

demonstrations, microchemistry, computer-assisted learning, CD-ROMs, units on

societal issues and a plethora of textbooks. All these were associated with the

transmission of chemical knowledge only rather than to the nature and desirability of

the content or to the nature of the learning process. According to Johnstone (2000),

the International Journal of Science Education had devoted over a third of its space

to work on “Alternative Frameworks” and this had encouraged an approach to

research which was negative and offered few solutions to the problems that were

exposed.

New theories and a change of paradigms play an important role in improving

and also overcoming learning difficulties in chemistry. The Malaysian Form Four

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students who are still at the concrete operations level or early formal operations level

will experience difficulty in understanding and applying a formal subject. Since they

are in the transitional position from concrete operations to formal operations

according to Piaget’s (1970) perspective on thought processes, it is vital for

educators to design an instruction that will enable them to learn chemistry in a formal

setting. The instruction designed for this study should be able to elevate these

students to the formal operations level that promotes hypothetical-deductive

reasoning, scientific-inductive reasoning and reflective abstraction.

A shift in the dominant theory of learning from behaviourism to constructivism

has had a significant impact on chemistry education research over the past 50 years

(Johnstone, 2000). He expressed that the changes in chemistry education research

that have emerged from these two perspectives seemed dichotomous. Behaviourist-

based research attempt to narrow things down. Learning is placed under the

microscope in order to identify salient variables that could assure improvement in

performance. In contrast, constructivist-based research reverses that focus, using a

telescope to broaden the view of learning.

All along, the teaching and learning in Malaysia has been conducted in an

objectivist environment. The objectivist approach is basically pouring information into

the learner. The most basic assumption is that knowledge is external to humans,

and the meaning of the world exists independent of the human mind (Jonassen,

1992). The educator identifies the knowledge to be imparted to learners and this

knowledge is identified via specific behavioural objectives. Jonassen also stated that

all learners are expected to achieve learning objectives in the same manner. The

evaluation procedure entails using an objective evaluation method to determine

whether the objectives have been met and to what degree (Jonassen, 1992).

When the education reformation took place in the year 2000, the

constructivist environment was introduced into the Malaysian education system. In

this environment, knowledge is constructed in the mind from experience. Merrill

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(1991) noted that in a constructivist environment, learning is a personal interpretation

of the world and is an active process in which meaning is developed on the basis of

experience. The conceptual growth comes from the negotiation of meaning, the

sharing of multiple perspectives and the changing of our internal representations

through collaborative learning (Merrill, 1991). According to Merrill, learning should

also be situated in realistic settings and testing should be integrated with the task and

not as a separate activity.

Since the mole concept has been reported as a difficult, abstract and formal

topic, the chemistry educators have devoted considerable time in developing

curricula that help students visualise the molecular world and connect classroom

concepts to the world outside school (Stieff & Wilensky, 2002). Hence, it is

necessary to develop a constructivist learning environment incorporating the

instructional strategies to enhance the learning of abstract chemistry concepts. The

students will be able to visualise the molecular world and construct their own

knowledge in a more meaningful manner. They will also have to deploy hypothetical-

deductive and logical reasoning to solve problems in authentic situations.

The Ministry of Education in Malaysia emphasises the usage of multimedia to

facilitate the teaching and learning process in schools. They have provided laptops

to teachers, computer laboratories to schools and courseware to be utilised in the

teaching and learning process. The question is, “Are the computers and laptops

being used as teaching aids to facilitate the teaching and learning process or are

they being used as instructional medium?”

However, there are problems that have arisen such as the lack of sufficient

computers to enable students to be “hands on”. Most school computer laboratories

consist of about 20 computers only and there are usually an average of thirty

students in a class. Therefore, some of the students have to share the computers

with fellow students. This will hinder them from being self-paced and much time will

be wasted due to the sharing. However, to persuade the Ministry of Education to

8

provide each student with a computer, research must prove that it is essential to

improve the students’ achievement and enhance their motivation.

Although there are many local researchers (Fong, 2000; Fong & Ng, 1996,

1998, 1999, 2000; Kong, 2002; Norizan, 2002; Toh, 1995, 1998; Toh, Abdul Rahim &

Ng, 1998) conducted multimedia studies but as far as the researcher is aware, no

research on multimedia constructivist environment in chemistry was reported. Thus,

there is a need for a study in Malaysia to discover the effects of the multimedia

constructivist environment towards the students’ achievement and motivation.

There is no readymade courseware designed in a constructivist environment

in the Malaysian education system. The courseware provided by the Curicullum

Development Centre, Educational Technology Department and Pelangi Mind-edge is

generally based on the objectivist model. There might be some constructivist

elements embedded within the courseware but on the whole the courseware is

designed in an objectivist manner. The teachers might not have resources and the

time to prepare their own courseware using the constructivist environment due to the

lack of expertise and knowledge on courseware development. Thus, the Ministry of

Education must ensure that readymade courseware is provided to schools to assist

teachers implement their teaching and learning processes effectively in a

constructivist environment.

Malaysia as a fast developing nation is moving towards excellence through

education. Greater involvement and a bigger work force in science and technology

are required for the knowledge-economy and globalisation. Therefore, the root factor

will have to start from school level. More students are encouraged to enrol in the

science stream to congregate the work force required in the field of science and

technology. In order to achieve a developed country by the year 2020, the Ministry of

Education has set target to achieve the ratio 60:40 for the science stream students

compared to the arts stream students by the year 2010 (Pusat Perkembangan

Kurikulum, Kementerian Pelajaran, 2001). This has indirectly encouraged many

9

students with average and below average results to be accepted in the science

stream (Kong, 2002). Kong expressed that these students are the main challenge for

educators since most of them are weak in the science subjects. She also mentioned

that teachers have to deal with students of different levels of science skills and the

weaker groups might develop a negative attitude and the lack of interest in learning

pure science subjects. This practice had also indirectly created an environment

where the class will be of a mismatch of high-ability students and low-ability students

in this scenario. The low-ability students will be poorly motivated and might be

subjected to low achievement in the pure sciences. To hinder this, it is important for

the Ministry of Education to resolve this problem and provide a learning environment

and an instructional design that are suitable for the students to improve their

achievement and increase their motivational level.

Based on the Third International Mathematics and Science Study, TIMSS

Report (Appendix A), the average for Malaysia’s mathematics and science

achievement of eight graders in 1999 was higher than the international average.

However, Malaysia’s mathematics achievement was at the 16th position and science

achievement was at the 22nd position. In the year 2003 (Appendix B and Appendix

C), the TIMSS Report also showed a similar result for Malaysia. The average scale

score for mathematics and science was higher than the international average but it

fell to the 10th position for mathematics and 20th position for science. This shows that

Malaysia is still far behind Singapore (top position) and therefore, need to strive very

hard to improve its performance of mathematics and science. Hence, this implies

that the teaching and learning of science and mathematics need to be improved in

order to elevate the achievement in the two subjects to a higher level.

Since the introduction of Keller’s (1987) ARCS model of motivation, many

studies have been carried out to investigate students’ perceived motivation towards

instructional material and chemistry using the Instructional Materials Motivation Scale

(IMMS) instrument. Many foreign studies (Duchastel, 1997; Hykle, 1993; Malone,

10

1984; Mistler-Jackson & Songer, 2000; & Pulist, 2001) as well as local studies (Kong,

2002; Toh, 1998; Toh, Abdul Rahim & Ng, 1998) have documented the effects of

students’ perceived motivation towards multimedia. However, information on the

effects of the student’s perceived motivation towards instructional material and also

towards chemistry in the multimedia constructivist environment are relatively lacking,

especially, in Malaysia. Thus, this study intends to investigate whether students are

better motivated towards chemistry and also the multimedia instruction in a


Computers in education may provide their greatest potential for

disadvantaged students, those of low-ability and the concrete operational students.

Cavin and Lagowski (1978) investigated the effects of computer simulated or

laboratory experiments and student aptitude on achievement and time in a college

general chemistry laboratory course. They reported that computer-simulated

experiments are useful especially for low-aptitude students, both as a laboratory

experiment substitute and as a supplement, while being a satisfactory means of

instruction for higher-aptitude students. So, it is important to investigate the effect of

the multimedia constructivist environment on the low-ability as well as high-ability

students.

The design and development of the multimedia constructivist environment

must accommodate both low-ability and high-ability students. The rationale is to

ascertain that both groups of students will benefit equally when exposed to a

multimedia constructivist environment. Each individual (low-ability and high-ability)

is expected to perform differently (Cronbach & Snow, 1977) and this study intends to

find out whether both low-ability students and high-ability students can adapt easily to

a multimedia constructivist environment. A certain learning environment might only

motivate either the higher ability students or the lower ability students (Cavin &

Lagowski, 1978). It is also vital to know whether there are any differences in the

higher ability and lower ability students’ motivation towards the constructivist

11

environment and objectivist environment, as it is more appropriate to design an

instruction that motivates both groups of students.

Many psychological moderator variables affect students’ achievement.

Amongst them are the locus of control, spatial ability, students’ computer anxiety,

and field dependency. Previous studies undertaken on multimedia instruction (Kong,

2002; Irfan, 2000; Ng, 1996, 1998; Ng & Fong, 1996; Ng & Toh, 1996) showed that

field dependency stands out as the strongest among the various psychological

moderator variables. However, the effects of field dependency on students in the

multimedia constructivist environment are relatively unknown and still not well

researched especially in Malaysia.

This study also intended to predict whether the field-dependent or the field-

independent students would perform better in a constructivist environment. The

students thinking and learning styles have to be considered in designing an

appropriate multimedia instruction. This has an important implication on the

development of the courseware that is adaptive and customised to the psychological

profile of learners. The field-independent students are expected to learn better in an

unstructured and non-linear environment (Constructivist Learning Environment)

whereas the field-dependent students are expected to learn better in a more

structured and linear environment (Objectivist Learning Environment). However, it is

vital to design an instruction that would increase the achievement for both groups.

Therefore, Multimedia Constructivist Instruction should be appropriately designed to

suit both field-dependent and field-independent students.

Kahle and Meece (1994) reported gender differences in science achievement

while carrying out research on gender issues in the classroom. While investigating

the gender difference in attitudes, achievement and use of computers, Hattie and

Fitzgerald (1988) also found that there is a gender difference in the usage of

computers that is more evident at the secondary level than in the elementary years.

Therefore, an investigation into gender differences in terms of achievement and also

12

motivation would provide useful insight into this research. The rationale is to

determine whether boys and girls perform equally well when exposed to a multimedia

constructivist environment. There are a number of studies reported that they show a

significant difference in their formal operational level and learning abstract concepts

and therefore, a significant difference in their achievement score was reported

(Andersen & Nielsen, 2003; Turner & Lindsay, 2003; Yea Ru Chuang, 1999).

However, other studies reported that there is no significant difference in their formal

operation level and learning abstract concepts, and thus, both male and female

students show no significant difference in their achievement score using multimedia

instruction. Therefore, it is significant to design a multimedia constructivist

environment that enhances the achievement of both the male and female students

and elevates their motivation towards the instructional materials and chemistry.

This study developed a constructivist environment for the students to learn

the mole concept. It also employs multimedia instruction as a medium of instruction

to investigate their achievement in chemistry and its effect on their motivation

towards chemistry and multimedia instruction. As many researchers have

documented that the mole concept is taught and learned using the problem-solving

method (Gabel & Bunce, 1994; Hollingworth, 2001; Johnstone, 1993b; Nurrenbern &

Pickering, 1987; Reid & Yang, 2002), the multimedia instruction for the constructivist

environment will be based on the problem-solving component.

For this purpose, the researcher designed a Multimedia Constructivist

Instruction that is expected to enhance the achievement of the students and elevate

their motivation towards chemistry and the instruction. The researcher also

examined whether the students’ ability levels (low-ability and high-ability), cognitive

styles (field-dependent and field-independent) and gender (male and female) affect

their achievement in chemistry as well as their motivation towards chemistry and

multimedia instruction.

13

1.3 Research Objectives

The objectives of this research are:

a) To investigate the effects of the Multimedia Constructivist Instruction (MCI)

approach compared to the Multimedia Objectivist Instruction (MOI) approach

in the learning of “Chemical Formulae and Equations” (the mole concept) on

the students’ achievement score (as measured by the pretest and posttest)

b) To investigate the effects of the MCI approach compared to the MOI

approach in the learning of “Chemical Formulae and Equations” (the mole

concept) between

(i) the low-ability and high-ability students

(ii) the field-dependent and field-independent students

(iii) the male and female students

towards their achievement score

c) To investigate the effects of the MCI approach compared to the MOI

approach in the learning of “Chemical Formulae and Equations” (the mole

concept) on the students’ IMMS score (as measured by the Instructional

Materials Motivation Scale)

d) To investigate the effects of the MCI approach compared to the MOI approach

in the learning of “Chemical Formulae and Equations” (the mole concept)

between

(i) the low-ability and high-ability students

(ii) the field-dependent and field-independent students

(iii) the male and female students

towards their motivation as indicated by the IMMS score.

1.4 Research Questions

1 (a) Is there any significant difference in the achievement score (as

measured by the pretest and posttest) of the students in the

14

Multimedia Constructivist Instruction (MCI) approach as compared to

the Multimedia Objectivist Instruction (MOI) approach?

(b) Is there any significant difference in the achievement score between

the low-ability and high-ability students in the MCI approach as

compared to the MOI approach?

(c) Is there any significant difference in the achievement score between

the field-dependent and field-independent students in the MCI

approach as compared to the MOI approach?

(d) Is there any significant difference in the achievement score between

male and female students in the MCI approach as compared to the

MOI approach?

2 (a) Is there any significant difference in the IMMS score (as measured by

the Instructional Materials Motivation Scale) of the students in the MCI

approach as compared to the MOI approach?

(b) Is there any significant difference in the IMMS score between the low-

ability and high-ability students in the MCI approach as compared to

the MOI approach?

(c) Is there any significant difference in the IMMS score between the field-

dependent and field-independent students in the MCI approach as

compared to the MOI approach?

15

(d) Is there any significant difference in the IMMS score between male

and female students in the MCI approach as compared to the MOI

approach?

1.5 Research Hypotheses

The level of significance, α, used for this study was 0.05. The hypotheses of

this study that corresponded to the above research questions were stated in the

alternate form with reference from the past research conducted in multimedia

instruction as well as constructivist learning environment and were as follows:

H11: The students who are using the MCI approach will show a significant

difference compared to the students who are using the MOI approach in their

achievement score (as measured by the posttest minus the pretest).

H12: The high-ability students will show a significant difference compared to the

low-ability students who are using the multimedia instruction approach in their


H12a: The high-ability students will show a significant difference compared to

the low-ability students who are using the MCI approach in their

achievement score.

H12b: The high-ability students who are using the MCI approach will show a

significant difference compared to the high-ability students who are

using the MOI approach in their achievement score.

H12c: The low-ability students who are using the MCI approach will show a

significant difference compared to the low-ability students who are


H13: The field-independent students will show a significant difference compared to

the field-dependent students who are using the multimedia instruction

approach in their achievement score (as measured by the posttest minus the

pretest).

16

H13a: The field-independent students will show a significant difference

compared to the field-dependent students who are using the MCI

approach in their achievement score.

H13b: The field-independent students who are using the MCI approach will

show a significant difference compared to the field-independent

students who are using the MOI approach in their achievement score.

H13c: The field-dependent students who are using the MCI approach will

show a significant difference compared to the field-dependent

students who are using the MOI approach in their achievement score.

H14: The male students will show a significant difference compared to the female

students who are using the multimedia instruction approach in their


H14a: The male students will show a significant difference compared to the

female students who are using the MCI approach in their achievement

score.

H14b: The male students who are using the MCI approach will show a

significant difference compared to the male students who are using

the MOI approach in their achievement score.

H14c: The female students who are using the MCI approach will show a

significant difference compared to the female students who are using


H15: The students who are using the MCI approach will show a significant

difference compared to the students who are using the MOI approach in their

IMMS score (as measured by the Instructional Materials Motivation Scale).



IMMS score (as measured by the Instructional Materials Motivation Scale).

17

H16a: The high-ability students will show a significant difference compared to

the low-ability students who are using the MCI approach in their IMMS

score.


significant difference compared to the high-ability students who are

using the MOI approach in their IMMS score.


significant difference compared to the low-ability students who are




approach in their IMMS score (as measured by the Instructional Materials

Motivation Scale).

H17a: The field-independent students will show a significant difference

compared to the field-dependent students who are using the MCI

approach in their IMMS score.

H17b: The field-independent students who are using the MCI approach will


students who are using the MOI approach in their IMMS score.

H17c: The field-dependent students who are using the MCI approach will


students who are using the MOI approach in their IMMS score.


students who are using the multimedia instruction approach in their IMMS

score (as measured by the Instructional Materials Motivation Scale).

H18a: The male students will show a significant difference compared to the

female students who are using the MCI approach in their IMMS score.

18

H18b: The male students who are using the MCI approach will show a

significant difference compared to the male students who are using

the MOI approach in their IMMS score.

H18c: The female students who are using the MCI approach will show a

significant difference compared to the female students who are using


1.6 Significance of the Study

The problems in the mole concept are not a new phenomenon and are of a

major international concern. Many relevant studies have been carried out in various

parts of the world such as Italy, Israel, Turkey, USA, Canada and Malaysia. The

review of literature reveals very few international studies in chemistry with regard to

the Constructivist Learning Environment and based on the survey of the literature, no

specific study has been conducted in Malaysia related to teaching the mole concept

in this environment. Hence, this study is expected to provide some useful

information for instructional designers to create more multimedia instruction in the

constructivist environment that will enhance and provide some resources for CD-

ROMs and web-based learning.

The study was intended to suggest an alternative to the traditional learning

environment in schools. Although teachers were exposed to the constructivist

elements in the teaching and learning process during the revised curriculum of 2000,

they still use the “note-book” to facilitate the teaching and learning process and

therefore, one-to-one instruction is not possible. The multimedia constructivist

learning environment employed in the present study will enable students to learn and

construct knowledge on their own and the teacher will become the facilitator. Each

student will be provided with a computer and they will be able to construct their

knowledge according to their own experience at their own pace. The outcome of this

study is expected to benefit the educators, curriculum experts and other researchers.

19

One of the main objectives of the study was to test the efficacy of the MCI

compared to the MOI amongst the students’ achievement and motivation towards the

learning of chemistry and the usage of multimedia instruction. The results from this

investigation would indicate the effects of the constructivist environment in the

students’ chemistry learning outcome.

The study also aimed to seek the difference between the low-ability and high-

ability students, the field-dependent and field-independent students and the male and

female students in their achievement and motivation towards the MCI and MOI. The

findings from this study will contribute insightful information on the ability levels of the

students, different learning styles (FD/FI) and gender preferences. This can be used

as a basis for educators to improve the teaching and learning among Form Four

chemistry students. The students will benefit in the sense that they will be provided

with better learning environments and facilities. The instructional designers will also

be provided with useful insights into the aptitudes and psychological profiles of the

students in the constructivist environment.

One of the difficult transitions for new secondary science teachers is that from

novice teacher to master teacher. This is because there is a major revamp in the

teaching of science and mathematics where the medium of instruction is changed

from the Malay medium to English medium to enable Malaysia to compete in the

global economy. As a result, a large number of teachers who have been teaching

these subjects using the Malay medium find it difficult to adjust this shift. Therefore,

there is a dilution in the quality of teaching from those advocated by the curriculum

standards. This study provides adjunct instructional materials to meet these needs.

The majority of the cited research in the literature review on the mole concept

was conducted using the problem-solving approach. This concept involved extensive

calculations and problems to be solved. So, this study developed a multimedia

instruction for the mole concept using the problem-solving approach for the


20

1.7 The Theoretical Framework

The theoretical base for this study originated from Jonassen (1999), Clark

and Mayer (2003) and Keller (1987). Jonassen presented a macro model for the

learning environment; whereas Clark and Mayer proposed the instructional design

model for multimedia and Keller developed a framework for motivation as the micro

model.

Jonassen (1999) proposed the Constructivist Learning Environment model

that consists of a problem, question or project as the central focus of an environment.

The goal of the learner is to interpret and solve a problem or complete a project. The

environment would be filled with five interpretative and intellectual support systems

as guidance for the learner and to assist him/her to solve the problems. The support

tools or system for this Constructivist Learning Environment are related cases,

information resources, cognitive tools, conversation/collaboration tools and

social/contextual support.

Clark and Mayer (2003) introduced an instruction delivered on a computer by

CD-ROM and the Internet and this is called e-learning. e-Learning consists of

content (information) relevant to the learning objectives. It uses instructional

methods (techniques) such as examples and practice to help learning. It also uses

media elements such as words and pictures, to deliver the content and methods. e-

Learning builds new knowledge and skills linked to individual learning goals. In

general, e-learning courses include both content and instructional methods to help

students learn the content. Learning courses are delivered via the computer using

words in the form of the spoken or printed text and pictures such as illustrations,

photos, animation or video. The objective of e-learning courses is to help learners

reach personal learning objectives. In short, the goal of e-learning is to build

knowledge and skills to help individuals achieve personal learning goals.

Keller (1987) developed the ARCS model of motivation that was used in this

study as a measure of motivation among the Form Four students to investigate their

21

motivation towards chemistry and multimedia instruction. This model identifies four

essential strategy components for motivating instruction:

(i) A – Attention strategies for arousing and sustaining curiosity and interest.

(ii) R – Relevance strategies that are linked to learner’ needs, interests and

motives.

(iii) C – Confidence strategies that help students develop a positive expectation

for successful achievement.

(iv) S – Satisfaction strategies that provide extrinsic and intrinsic reinforcement

for effort (Keller, 1983).

1.8 The Research Framework

The research framework of this study is depicted in Figure 1.1. The model

postulated two independent variables that attempted to impose effect on the two

dependent variables. The independent variables were the MCI and the MOI. These

independent variables were expected to show a significant variance on the

dependent variables. The dependent variables were achievement and motivation.

There were three moderator variables present in this study and they gave a strong

contingent effect on the independent and dependent variable relationship. The

moderator variables were ability levels, cognitive styles and gender.

Multimedia Constructivist Instruction was expected to show a significant

positive improvement among the students in the mole concept achievement test.

From the past studies, multimedia instruction and the constructivist learning

environment has proven to enhance learning especially in higher order. So, the

combination of both would provide a better environment for a more meaningful

learning. This independent variable was also expected to provide a significant

positive result on the students’ motivation towards chemistry and multimedia

instruction. This study also intended to explain the relationships of ability levels,

cognitive styles and gender towards achievement and motivation.

22

MCI Achievement

(Pretest and Posttest) Mole Concept MOI Motivation (IMMS-Keller) Ability levels Cognitive styles Gender

MCI – Multimedia Constructivist Instruction MOI – Multimedia Objectivist Instruction Ability Levels – Low-Ability and High-Ability Cognitive Styles – Field-Dependent/Field Independent Gender – Male and Female

Figure 1.1 The Research Framework for Multimedia Instruction

1.9 Limitations

The study aimed to investigate the effects of the multimedia constructivist

environment on achievement and motivation in the learning of “Chemical Formulae

and Equations”. The sample of this study consisted of Form Four Science students

from two suburban secondary schools in Butterworth. Their ages ranged from 16 to

17 years old. This limited the generalisability of the study to non-science stream

students and students from other states in Malaysia, including Penang island.

This study investigated one out of 13 topics in chemistry that made

generalisation on chemistry impossible. The constructivist instruction was designed

for the problem-solving component, this being only one component investigated out

of many constructivist components. Thus, the results from this study could not be

generalised for all the components in constructivist instruction.

23

1.10 Operational Definitions

For the purpose of clarification, the following terms used in this study were

either adopted from other studies or were operationally defined as follows:

The Mole Concept

One mole is the quantity of a substance that contains the number of particles similar

to the one found in 12.000 of carbon-12. The number of particles forms a constant

number called the Avogadro number of Avogadro constant, NA, which is 6.02 x 1023

(Eng, Lim & Lim, 2006). This concept is taught in the third chapter of chemistry,

“Chemical Formulae and Equations” at the fourth level of secondary school in

Malaysia.

Multimedia

This is combination of content and instructional methods that encourages learners to

engage in active learning by mentally representing materials in words and pictures

making connections between the pictorial and verbal representations (Clark and

Mayer, 2003).

Multimedia Constructivist Instruction (MCI)

This is a courseware designed and developed based on Jonassen’s Constructivist

Learning Environment (1999) model, Clark and Mayer’s (2003) multimedia learning

and Allessi and Trollip’s (1991) instructional systems design and development for

multimedia. The discerning attributes are:

(a) The model consists of a problem as the central focus of the environment.

(b) Students are not guided on how to go about solving a problem but are

expected to do so by exploring the courseware on their own.

(c) The problem statement is loosely defined.

24

(d) A variety of tools are provided for the students to construct knowledge such

as:

(i) Related cases

(ii) Information resources

(iii) Cognitive tools

(iv) Conversational collaboration tools

(v) Social contextual support

(e) The learning activities, that provide instructional support in this courseware

and enable students to construct knowledge are modelling, coaching and

scaffolding.

Multimedia Objectivist Instruction (MOI)

This is a courseware designed and developed based on the tutorial approach, Clark

and Mayer’s (2003) multimedia learning, Allessi and Trollip’s (1991) instructional

systems design and development for multimedia and Gagne’s (1985) nine events of

instruction. The discerning attributes are:

(a) The courseware is designed in a highly linear manner. The students are

given tutorials with definition, information, historical background, formulae,

media and examples of cases in sequence according to the sub-topics.

(b) There are also summative evaluations at the end of each lesson.

(c) The students are guided and forced to follow the flow given in the

courseware according to the sub-topics.

(d) The objectives are presented clearly in a behavioural manner.

Field-Dependent Students

Students scoring below the GEFT (Witkin, et al., 1971) calculated mean for a sample

are considered field-dependent. The students having a score at the GEFT calculated

mean value will be omitted. These individuals tend to have highly developed social

25

skills; favour a spectator approach to learning, and need structured learning

environments.

Field-Independent Students

Students scoring above the GEFT (Witkin, et al., 1971) calculated mean for a sample

are considered field-independent. The students having a score at the GEFT

calculated mean value will be omitted. Field-independent individuals are more

accomplished at logical reasoning, may have inferior social skills, and can provide

their own structure to facilitate learning.

Low-Ability Students

Students scoring below the group mean in a Cattell “Culture Fair” Intelligence Test

(Cattell & Cattell, 1973) are considered to have low ability. The students having a

score at the calculated mean value will be omitted.

High-Ability Students

Students scoring above the group mean in a Cattell “Culture Fair” Intelligence Test

(Cattell & Cattell, 1973) are considered to have high ability. The students having a

score at the calculated mean value will be omitted.

High Motivation

Students scoring above the group mean in the Keller’s Instructional Materials

Motivation Scale (IMMS) inventory questionnaire. The students having a score at the

calculated mean value will be omitted.

26

Low Motivation

Students scoring below the group mean in the Keller’s Instructional Materials

Motivation Scale (IMMS) inventory questionnaire. The students having a score at the

calculated mean value will be omitted.

Achievement Score (Gain Score)

This is gain score obtained from the difference between the pretest score and the

posttest score. These tests were administered to the students in this study to

measure their achievement in “Chemical Formulae and Equations”.

The IMMS Score

The IMMS score is obtained from the implementation of the Instructional Materials

Motivational Scale by Keller (1987) after the treatment.

Form Four Students

These students are at the fourth level of the secondary school and are between 16-

17 years old. The chemistry subject will be introduced for the first time to the Form

Four students in Malaysia.

PMR

The Penilaian Menengah Rendah is a government examination sat by the students at

the third level of secondary school.

SPM

The Sijil Pelajaran Malaysia is a government examination sat by the students at the

fifth level of secondary school.

27

1.11 Conclusion

It is vital for a substantial amount of research being conducted to ensure that

the Constructivist Learning Environment benefits and improves the teaching and

learning in schools. This study investigated the effects of the Constructivist Learning

Environment on the students’ achievement in chemistry and motivation towards

chemistry and multimedia instruction. This study will contribute valuable information

on whether our Malaysian students perform well and are more motivated in a

Constructivist Learning Environment. Effects of the ability levels, cognitive styles and

gender were also researched to ensure that the Constructivist Learning Environment

is compatible for low-ability and high-ability students, field-dependent and field-

independent students as well as male and female students. Thus, this study is

expected to shed very useful information for researchers, educators and students.

28

CHAPTER 2

REVIEW OF LITERATURE

2.0 Introduction

Chemistry education poses understanding difficulties among students. Thus,

it is important to design and develop instruction in an appropriate learning

environment to facilitate better understanding. This chapter reviews the research on

chemistry education and the conceptual difficulties in chemistry. The chapter also

reviews the learning theories and instructional design based on the literature

currently available to identify the learning environment and variables that are

significant to solve the learning difficulties in chemistry. The review on multimedia

and the variables involved in the study will also be discussed. The advantages and

limitations will also be included in this discussion.

2.1 Conceptual Difficulties in Chemistry Education

Research in chemical education is still young especially in the constructivist

learning environment and initial research studies concentrated at assessing the

influence of the new chemistry curricula and their supporting materials (Fensham,

2002). Even Ware (2001) argued that for more than four decades, educators were

busy reforming science education that ended with only restructuring the curriculum.

However, Hassan, Hill and Reid (2004) stated that one of the major aims in all these

curriculum and syllabus developments was to promote student understanding of

basic chemical concepts. Fensham (2002) also documented that the teaching and

learning of a subject like chemistry have turned out to be more complex and he

suggested that chemical education needed a more substantial research base of its

own.

Johnstone (2000) delivered some unpleasant observations in chemistry

education such as “students are opting out of chemistry” and “unable to offer well

qualified and competent teacher for students”. Johnstone’s research was based

29

around two models. The first model was “information processing” that attempted to

suggest mechanisms for learning arising from a number of psychological schools.

Johnstone’s second model was connected to the nature of chemistry that exists in

three forms that can be thought of as corners of a triangle. No one form is superior

to another, but each one complements the other. These forms of the subject include

the macro and tangible: what can be seen, touched and smelt; the sub-micro: atoms,

molecules, ions and structures; and the representational: symbols, formulae,

equations, molarity, mathematical manipulation and graphs. Macroscopic

representations involve qualitative observations or descriptions made by chemists

using their five senses: colour changes, changes in state (bubbles or precipitates),

odours, sounds, heat changes, etc. Symbolic representations involve the use of

symbols to represent objects that are often too abstract to see or touch. Microscopic

representations describe chemical processes in terms of atoms, molecules and ions

and their interactions. These microscopic representations often pose a problem for

students because they cannot directly see or touch atoms and molecules in the

classroom.

According to Johnstone (2000), to understand chemistry fully, a student has

to move to the sub-micro situation where the behaviour of the substances is

interpreted in terms of the unseen and molecular and recorded in some

representational language and notation. Johnstone (1993a) also stated that when

chemists and also the chemistry students think about chemical processes, they

should think in three interrelated but distinct representational levels: macroscopic,

symbolic and microscopic representations.

Ware (2001) also supported Johnstone by pointing out that to understand

chemistry, students must go across these three forms of perception. Stieff and

Wilensky (2002) documented that due to the sub-microscopic level of molecular

interactions, chemists must use symbols to refer to the atomic objects and processes

within their domain which they cannot observe directly. Moreover, aggregations of

30

molecules result in phenomena on a macroscopic level such as when water freezes

or ice melts (Stieff & Wilensky, 2002). On the symbolic level, where most teaching

and learning take place in the traditional chemistry classroom, instructors use

multiple representations for the same phenomena (Kozma & Russell, 1997). Stieff

and Wilensky (2002) suggested that a particular chemical reaction might be shown

on the blackboard with letters, molecular diagrams, or plots of concentration over

time. They also stated that in the laboratories, students are further expected to

connect the symbolic representations in texts to the actual physical substances they

use in an experiment and the numerical measurements they take from laboratory

instruments. Banerjee (1995) concluded that although chemists may easily

distinguish the relationships between chemical phenomena at the sub-microscopic,

microscopic and macroscopic levels and fluidly move between various symbolic

representations of the phenomena, students definitely encounter more difficulties.

Chemistry also contains many abstract concepts that are difficult to

understand (Herron, 1975; Muth & Guzman, 2000; Staver & Lumpe, 1993; Stieff &

Wilensky, 2002). Moreover, the “abstract” concepts of chemistry are often confined

to the chemistry classroom and not applicable outside the school (Stieff & Wilensky,

2002). Students must be able to conceptualise abstract concepts in chemistry in

order to understand it. It is important to design an excellent instructional system to

enhance the teaching and learning of difficult and abstract topics such as the mole

concept.

The mole concept has long been reported as one of the most difficult topics in

chemistry (Duncan & Johnstone, 1973; Friedel & Maloney, 1992; Gabel & Sherwood,

1984; Gabel et al., 1984; Herron & Greenbowe, 1986; Krishnan & Howe, 1994;

Yalcinalp et al., 1995). In a study designed by Cervellati et al. (1982), it was stated

that the mole and the related concepts are essential topics in secondary school

chemistry courses, but to teach and to learn these concepts appear to be remarkably

difficult. Besides that, the mole concept has been recognised as a fundamental

31

concept in quantitative chemistry (Case & Fraser, 1999; Staver & Lumpe, 1993) and

is usually taught at the beginning of the first year of upper secondary schools in

Malaysia. Gower, Daniels and Lloyd (1977) stated that the mole is an essential part

of the modern approach to chemistry teaching because it acts as a unifying concept,

linking many aspects of the subject throughout the syllabus. When a student fails to

understand the mole concept, it is likely that their chemical problem-solving ability will

be severely limited for other chemical concepts that are more complex.

It is essential for a student to know the mole concept thoroughly before

proceeding with the syllabus. Even in chemical engineering courses, the quantitative

chemistry concept is fundamental to all chemical engineering studies. In 1999, Case

and Fraser investigated into chemical engineering students’ understanding of the

mole and the use of concrete activities to promote conceptual change. They chose

the mole concept since this is the first idea introduced to students in high school and

then further dealt with in first year university courses in chemistry and chemical

engineering.

The mole concept has also been reported to be an abstract concept and is

taught at a level of abstraction (Chan, 1988; Gabel, 1999; Muth & Guzman, 2000;

Ware, 2001; Yalcinalp et al., 1995). Muth and Guzman (2000) researched the

conceptions and misconceptions in undergraduate science. They summarised that

chemistry is a complex, abstract subject that leads to many misconceptions.

Misconceptions are often internally consistent and coherent, and the lack of

particulate views of matter causes to common misconceptions about structure,

bonding and other chemical principles (Muth & Guzman, 2000). Misconceptions are

not easily replaced, even after students are exposed to correct mental models. They

also pointed out that correcting misconceptions requires learners be aware of the

misconception and dissatisfied with it and that a replacement concept be available

that is intelligible, plausible, and applicable. So, it is important for teachers to direct

students to construct correct conceptions of chemistry concepts from the beginning.

32

Muth and Guzman further stated that the behaviourist nature of science instruction

itself might be one reason that misconceptions occur and persist.

Therefore, since the mole concept is a difficult, abstract and formal concept

and the Form Four students are in the transitional position from concrete operations

to formal operations according to Piaget’s (1970) perspective on the thought

processes, it would be very appropriate to design and develop a multimedia

constructivist environment to enable them to learn the mole concept in a rich and

meaningful manner. This design is expected to impose a significant increase in the

students’ achievement towards chemistry and motivation towards chemistry and


2.2 Learning Theories and Instructional Design

2.2.1 Introduction

According to Duffy and Jonassen (1992), from an instructional design

perspective, the practice of instructional design comprises the conception of the

method and the meaning of learning. They also stated that from a learning theory

perspective, the value of learning theory lies on the ability to predict the impact of

alternative learning environments or instructional practices of what was learned. The

learning theory is the study of how people learn and the instructional design theory is

the study of how to best design instruction so that learning will take place. Thus,

Duffy and Jonassen (1992) concluded that the instructional design theory is drawn

from the learning theory and therefore explains the need for collaboration between

the learning theory and the instructional design.

Similarly, Moallem (2001) reported that a number of instructional design

models has been developed to help educators and instructional designers to

incorporate fundamental elements of the instructional design process and principles.

This process focuses on how to design and develop learning experiences, while the

principles focus on what learning experiences should be like after they have been

33

designed and developed. Moallem concluded that instructional design models are

guidelines or sets of strategies that are based on learning theories and best

practices.

Gros, Elen, Kerres, Merrienboer and Spector (1997) also documented in their

study that a new paradigm needs models that promote an iterative approach to

instructional design. Therefore, they stated that instructional design models have the

ability to provide a link between learning theories and the practice of building

instructional systems. They suggested that the instructional design theory inhabits

the gap between theory and practice.

There are two learning theories that were discussed extensively in the review

of literature, objectivism and constructivism. Objectivism is drawn from behaviourism

and cognitivism since both share the common entities. Objectivism supports the

practice of analysing a task and breaking it down into manageable chunks,

establishing objectives and measuring performance based on the objectives that

have been established. The objectivist feels that experience plays an insignificant

role in the structuring of the world.

2.2.2 Behaviourism and Instructional Design

The traditional teaching approach would reveal the powerful influence of

behaviourism. Behaviourism provides a clear goal for the learner who can respond

automatically to the cues of that goal. Behaviourism, being historically the older

learning theory, is observable evidence for learning to take place. Behaviourism was

the predominant school of thought in the first half of the 20th century (Hergehahn &

Olson, 1997). According to the behaviourist view, the only things worth studying

about human learning are observable behaviours. The theory of behaviourism

concentrates on the study of overt behaviours that can be observed and measured

(Good & Brophy, 1990). It views the mind as a “black box” in the sense that

response to stimulus can be observed quantitatively. While most behaviourists do

34

not deny the existence of mental activity, they do not speculate about these thinking

processes or other unobservable phenomena (McGriff, 2001).

Skinner (1974) proposed the stimulus-response pattern of conditional

behaviour. His theory dealt with changes in observable behaviour, ignoring the

possibility of any processes occurring in the mind. Skinner introduced operant

conditioning where the learner operates on the environment and receives reward for

certain behaviour. Eventually the bond between the operation and the reward

stimulus is established.

There are many incidents where the theoretical principles of behaviourism are

being applied to the learning environment. Ormrod (1995) documented that

behaviourist teachers expected learning to occur with a change in behaviour due to

past and present experience. Ormrod also provided some examples of

implementation of behaviour principles in the teaching and learning environment

such as writing, talking, demonstrating, higher test scores and improvement in the

performance of an activity.

Ormrod (1995) also elaborated on the drill and practice method that is used in

education and behaviourism. Learning and a change in behaviour are expected to

occur when there are repeated actions and practice between the stimulus and the

response. Another association between behaviourism and education introduced by

Ormrod is controlling behaviours by breaking habits. Ormrod suggested

strengthening desired behaviours in order to demolish the undesired ones.

“Weakening of the undesired behaviour by removing the reinforcement events that

maintain the behaviour” or “presenting the stimulus repeatedly until the individual

gets tired of it” are other methods recommended by Ormrod.

Another example that applies behaviourism in it is the concept of directed

instruction. The teacher provides knowledge to the students either directly or through

the set up of “contingencies” (Mergel, 1998). Being a prominent tool for teaching,

“computer-assisted instruction” is also designed from a behavioural perspective

35

(Mergel, 1998). Computer-assisted instruction uses drill and practice methods for

learning new concepts and skills. The questions act as the stimulus and elicit a

response from the user. Rewards may be provided for a response and this is

actually the approach of operant conditioning. Computer-assisted instruction is an

effective teaching approach because it allows for self-paced instruction and it

liberates teachers from the direct instruction of all their students so that they can

focus on those with particular needs.

Burton, Moore, and Magliaro (1996) noted that in a very real sense, the

behavioural theory is the basis for innovations such as teaching machines, computer-

assisted instruction, competency-based education (mastery learning), instructional

design, minimal competency testing, “educational accountability”, situated cognition

and even social constructivism. Other instructional strategies for behaviourism

include discrimination (recalling facts), generalisation (defining and illustrating

concepts), associations (applying explanation) and chaining (automatically

performing a specified procedure).

2.2.3 Cognitivism and Instructional Design

Another learning theory that contributed to objectivism is cognitivism.

Cognitivism appeared in the early part of the century as a drastic shift from

behaviourism because even though behaviourism encouraged observable and

measurable research in the field of psychology, it did not integrate mental events. In

the mid-20th century, cognitivism began to develop (McGriff, 2001).

As behaviourism, cognitive psychology can be traced back to the ancient

Greeks, Plato and Aristotle (Mergel, 1998). Then, cognitive psychology became

evident in American psychology during the 1950s (Saettler, 1990). Gestalt

psychologists of Germany and Edward Chase Tolman of the United States imposed

a remarkable influence on psychology and the shift from behaviourism to cognitivism

(Hergehahn & Olson, 1997).

36

Mergel (1998) noted that although cognitive psychology emerged in the late

1950s and began to take over as the dominant theory of learning, it was only in the

late 1970s that cognitive science began to have its influence on instructional design.

Cognitive science shifted from behaviouristic practices that emphasised external

behaviour, to a concern with the internal mental processes of the mind and how they

could be utilised in promoting effective learning (Mergel, 1998). Saettler (1990)

documented that the design models in the behaviourist tradition were not rejected

totally but the “task analysis” and “learner analysis” parts of the models were

embellished. The new models addressed component processes of learning such as

knowledge coding and presentation, information storage and retrieval as well as the

incorporation and integration of new knowledge with previous information.

Behaviourists claimed that mental events were impossible to observe and

measure and could not be studied objectively. Therefore, behaviourists could not

describe the ways learners attempt to make sense of what they learn. Conversely,

cognitivists were able to explain cognitive processes that produce responses through

empirical research and observation inferences. Cognitivists believed that human

knowledge is structured and organised. Human actively process information and

learning takes place through the efforts of the students as they organise, store and

then find relationships between information, linking new to old knowledge, schemas

and scripts. Cognitive approaches emphasise how information is processed.

Gagné (1985) built upon cognitive theories to recommend approaches to

instruction. He suggested that a task would be best learned by following a specific

sequence of nine events termed as the conditions of learning. These are the

following:

Gaining attention

Informing the learner of the objective

Stimulating recall of prerequisite learning

37

Presenting new material

Providing learning guidance

Eliciting performance

Providing feedback about correctness

Assessing performance and

Enhancing retention and recall

In cognitivism, knowledge acquisition is measured by what learners know

beyond behaviouristic concepts. Cognitive theorists view learning as involving the

acquisition or reorganisation of the cognitive structures through which human beings

process and store information.

At present, cognitive learning theories are the dominant influences on

instructional design practice (McGriff, 2001). McGriff further commented that

cognitivists place more emphasis on factors within the learner and less emphasis on

factors within the environment. Cognitive learning theories themselves do not offer

guidance on how to teach but the instructional design theory describes the specific

events in the environment or methods of instruction to facilitate learning. McGriff

suggested that it is important for an instruction designer to be thoroughly versed in

the theories of learning and human behaviour to support the field.

The goal of instruction remains the communication or transfer of knowledge to

learners in the most efficient, effective manner possible (Bednar, Cunningham, Duffy

& Perry, 1995). For example, they stated that the breaking down of a task into small

steps works for a behaviourist who is trying to find the most efficient and fail-safe

method of shaping a learner’s behaviour. Similarly, according to Bednar et al., the

cognitive scientist would analyse a task, break it down into smaller steps or chunks

and use that information to develop instruction that moves from simple to complex

building on prior schemas.

38

Instructional design in cognitivism is about promoting the cognitive processes

that lead to learning whereas learning theories are of critical interest to instructional

designers, namely, because those theories are descriptive attempting to describe,

explain and predict learning (McGriff, 2001).

The instructional strategies that employ cognitivism include the information

processing model, explanations, demonstrations, illustrative examples, corrective

feedback, outlining, chunking information, repetition, concept mapping, analogies,

summaries, Keller’s (1987) ARCS model of motivation, interactivity, synthesis, the

schema theory, metaphors, generative learning, organisational strategies,

elaboration theory and links to prior knowledge. Although computers are designed

from a behaviourist perspective, they process information similarly to how humans

process information: receive, store and retrieve.

2.2.4 Constructivism and Instructional Design

Constructivism is one of the most influential learning theories in contemporary

education and has had great influence in science education. Constructivism is also

currently enjoying popularity as a “new paradigm” in education. Constructivists

believe that learners construct their own reality or at least interpret it based upon their

perceptions of experiences, so an individual’s knowledge as a function of one’s prior

experiences, mental structures, and beliefs that are used to interpret objects and

events (Jonassen, 1991). Similarly, Perkins (1991) noted that learners elaborate on

and interpret information of the world based on their past experience and interactions

with the world.

Constructivists believe learning is infinite and not subject to scientific

measures. Learning that occurs in isolation is “inert”; the learner has the information

in memory but never recognises when it is relevant (Cunningham, 1991).

Besides this, Cunningham (1991) felt that how the student interprets the world

is as powerful of an influence on what is learned as any characteristic with the world.

39

He stated that constructivists emphasise an active role for the student in the learning

process. According to Cunningham (1991), constructivist learning promotes

collaboration with others to help students develop multiple perspectives that can be

used together to solve problems; all constructions are not created equal but there is

no one correct perspective.

Merrill (1991) offered the following similar assumptions of constructivism:

Knowledge is constructed from experience

Learning is a personal interpretation of the world

Learning is an active process in which meaning is developed on the basis

of experience

Conceptual growth comes from the negotiation of meaning, the sharing of

multiple perspectives and the changing of our internal representations

through collaborative learning

Learning should be situated in realistic settings; testing should be

integrated with the task and not a separate activity.

Bruner’s (1960) constructivist theory is a general framework for instruction

based upon the study of cognition; his theory is linked to child development research.

Bruner’s theoretical framework portrays learning as an active process in which,

students construct new ideas or concepts based upon their current or past

knowledge. The learner selects and transforms information, constructs hypotheses,

and makes decisions based on a cognitive structure. The cognitive structure such as

schemas and mental models gives meaning and organisation to experiences and

also allows the individual to go beyond the information given.

According to Bruner, the instructor should encourage students to discover

principles on their own. The instructor and student should engage in an active

dialog, i.e. socratic learning. The instructor will manipulate the information to be

learned into a format appropriate to the student’s current state of understanding.

40

Curriculum should be organised in a spiral manner and instruction must be structured

so that it can easily grasped by the students. Bruner (1966) emphasises that the

theory of instruction consists of predisposition towards learning, the ways in which a

body of knowledge can be structured so that it can be most readily grasped by the

learner, the most effective sequences in material presentation and the nature and

pacing of rewards and punishments. He maintained that people interpret the world

in terms of its similarities and differences and suggested a coding system in which

they have a hierarchal arrangement of related categories. Each successively higher

level of categories becomes more specific.

Savery and Duffy (1995) explained constructivism as a philosophical view on

how we come to understand and what is understood includes a function of the

content, the context and the activity of the learner. Perhaps the most important is the

goals of the learner. Understanding cannot be shared but can be tested whether it is

compatible or not. An implication of this proposition is that cognition is not just within

the individual, but rather a part of the entire context. Savery and Duffy further stated

that the goal for learning is a stimulus for learning and also a primary factor in

determining what the learner brings to bear in constructing an understanding, and

basically what understanding is eventually constructed.

In 1998, Mergel wrote that in a constructivist learning approach, the learner is

able to interpret multiple realities and to deal with real life situations. If the learner

can solve a problem, he/she may better apply his/her existing knowledge to a novel

situation. However, in a situation where conformity is essential, divergent thinking

and action may cause problems.

Jonassen (1999) also stated that the constructivist conceptions of learning

assumed that knowledge is individually constructed and socially co-constructed by

learners based on their interpretations of experiences in the world. He felt that since

knowledge cannot be transmitted, instruction should consist of experiences that

facilitate knowledge construction.

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McGriff (2001) documented that Dewey (1859-1952), an American

philosopher and educator, is considered the forefather of constructivism. Dewey

(1965) proposed that students learn by “directed living with an emphasis on

workshop-type projects so that learning is combined with concrete activity and

practical relevance”.

Similarly, Piaget’s (1970) research comprised constructivist components such

as discovery learning. In 1970, the developmental psychologist suggested that

children learn by directly acting upon their environment, manipulating objects and

constructing schemas based upon their experiences. Schemas are packets of

knowledge relating to one particular experience, and schema networks can be

constructed so that knowledge can be generalised and widened. Piaget proposed

assimilation and accommodation as two means by which learning takes place.

Assimilation is a situation in which new information is taken in and absorbed into a

previously existing schema and accommodation is a situation in which old

information in an existing schema is modified to make way for new information.

Piaget (1970) proposed that children progress through an invariant sequence

of four stages: sensorimotor (0-2 years) where intelligence is demonstrated through

motor activity, pre-operational (3-7 years) where intelligence is demonstrated through

the use of symbols, language use matures, and memory and imagination are

developed, but thinking is done in a non-logical and non-reversible manner, concrete

operations (8-11 years) where intelligence is demonstrated through a logical and

systematic manipulation of symbols related to concrete objects and formal operations

(12 years and above) where intelligence is demonstrated through the logical use of

symbols related to abstract concepts. Piaget proposed that children grow and

develop through each of these stages until they can reason logically.

Piaget (1985) also suggested that the learning process is iterative, in which

new information is shaped to fit with the learner’s existing knowledge and existing

knowledge is itself modified to accommodate the new information. Two major

42

principles that guide intellectual growth and biological development are adaptation

and organisation. A child must adapt to physical and mental stimuli to survive in an

environment. The adaptation process consists of assimilation, which is, fitting new

information into existing cognitive structures and accommodation, that is, modifying

existing cognitive structures based upon new information. Both of these processes

are used throughout life as the person adapts to the environment in a more complex

manner.

Piaget’s second principle is organisation that refers to the nature of these

adaptive mental structures. Piaget also believed that cognitive development in

children is contingent on four factors: biological maturation, experience with the

physical environment, experience with the social environment and equilibrium.

Equilibrium refers to the master developmental process, encompassing both

assimilation and accommodation.

Piaget also defined a schema as the mental representation of an associated

set of perceptions, ideas and/or actions. The schema can be discrete and specific,

or sequential and elaborate. Piaget’s cognitive development is a complex process

comprising three principal concepts affecting the development process: assimilation,

accommodation and equilibrium. These are associated with the formation of

schematas and their modification in order to attain a balanced sense of

understanding of the external world.

Vygotsky (1978) proposed a social development theory since this is a critical

factor that relates to the learning process and construction of knowledge. The

constructivists expressed the opinion that sociocultural development is one of the

significant factors that contributes to the construction of knowledge. Vygotsky

documented that social interaction plays a fundamental role in the development of

cognition, and that cultural development is divided into two levels: social and

interpersonal. The students recognise a new knowledge during the social interaction

and internalise it.

43

Vygotsky stated that students can be guided by explanation and

demonstration, and can attain to higher levels of thinking if they are guided by more

capable and competent adults. This conception is known as the Zone of Proximal

Development (ZPD). The ZPD is a gap between what is known and what is not

known, that indicates higher levels of knowing. Through increased interaction and

involvement, students are able to extend themselves to higher levels of cognition.

Vygotsky defines the ZPD as “the distance between the actual development level as

determined by independent problem solving under the guidance or in collaboration

with more capable peers”. Therefore the ZPD is the difference between what

students can accomplish independently and what they can achieve in conjunction or

in cooperation with another, more competent person. Vygotskian socio-cultural

psychology and the ZPD have been commonly referred to as the theoretical

underpinnings of scaffolding. The concept of scaffolding represents any kind of

support for cognitive activities that is provided by an adult when a child and adult are

performing the task together (Wood & Middleton, 1975).

Wood, Bruner and Ross (1976) invented the term “scaffolding” to describe

tutorial interaction between an adult and a child. This was used to investigate the

nature of aid provided by an adult for children learning to undertake a task that they

could not do on their own. They also described scaffolding during problem solving

as recruiting the child’s interest, simplifying the task, motivating the child and

demonstrating the correct performance. In the Constructivist Learning Environment,

scaffolding represents some manipulation of the task itself by the system (Jonassen,

1999).

The behavioural strategies can be part of a constructivist learning situation, if

the learner chooses and finds that type of learning suitable to his/her experiences

and learning styles. The cognitive approach also has a place in constructivism, since

constructivism recognises the concept of schemas and building upon prior

knowledge and experience. Perhaps the greatest difference is evaluation. In

44

behaviourism and cognitivism, evaluation is based on meeting specific objectives,

whereas in constructivism, evaluation is much more subjective (Mergel, 1998).

Although behaviourism and constructivism are very different in their

theoretical perspectives, cognitivism shares some similarities with constructivism.

Anyway despite these similarities between cognitivism and constructivism, the

objective side of cognitivism has supported the use of models in the systems

approach of instructional design (Mergel, 1998).

Constructivist instruction seeks to provide learners with their own means of

constructing their personal interpretation of a problem (Cunningham, 1991).

Constructivism begins by selecting a task relevant to the learner’s lived experience.

Cunningham (1991) said that the instructional strategy provides tools for inquiring

into a problem and various means for collecting information about the problem in

order to understand or construct solutions to the problem. He suggested that group

work would provide a better task accomplishment. The instructor helps the learner

see multiple perspectives. Since successful completion of the task indicates

successful learning, no separate test is required. The emphasis is on showing

learners how to construct a plausible interpretation, not on requiring students to know

certain things (Cunningham, 1991).

Dick (1991) documented that some agreement exists between constructivism

and instructional design assumptions. Dick (1991) and Cunningham (1991) agreed

that transfer of knowledge is vital and the present system of education is not

adequately preparing students to function successfully in the real world. Both also

agreed that learning must actively involve the learner. Instructional designers agreed

learners construct mental models based on their experience. Learners need a

variety of experiences to construct an adequate mental model and models are

modified based on the their experience (Merrill, 1991).

Constructivists argued there is no shared reality and learning is a personal

interpretation of the world. Instructional designers said the content of mental models

45

is unique to each person but all minds function the same way and it is possible to

know which way guides instruction (Merrill, 1991). Constructivists do not place

emphasis on the skills learners must possess whereas designers must determine

what learners know or are able to do before instruction begins because without

knowing existing skills, the instruction might not lead to new ones (Dick, 1991).

Instructional designers argue that pre-specification of knowledge does not mean it is

static, linear or that all possible learner response can be anticipated (Merrill, 1991).

Constructivism insists assessment is not a separate activity carried out after

instruction using an objective measurement instrument “scientifically proven to reveal

the learner’s success or failure”. Instead, constructivists argue assessment arises

naturally from the situation in which the instruction is embedded. Teachers play a

major role in the assessment process in constructivist learning and assessment

emerges naturally from authentic tasks.

Instructional strategies that employ the constructivist learning theory include

modelling, collaborative learning, coaching, scaffolding, problem-based learning,

authentic learning, anchored instruction, cognitive flexibility hypertexts and project-

based learning.

The influence of cognitive science in instructional design is evidenced by the

use of advance organisers, mnemonic devices, metaphors, chunking into meaningful

parts and the careful organisation of instructional materials from simple to complex

(Mergel, 1998).

Good and Brophy (1990) stated that cognitive theorists recognise that much

learning involves associations established through contiguity and repetition. They

also acknowledged the importance of reinforcement, although they stressed its role

providing feedback about the correctness of responses over its role as a motivator.

Mergel (1998) stated that the shift of instructional design from behaviourism

to cognitivism is not as drastic as the move into constructivism appears to be. Both

behaviourism and cognitivism are objective in nature and support the practice of

46

analysing a task and breaking it down into manageable chunks, establishing

objectives and measuring performance based on those objectives. Constructivism,

on the other hand, promotes a more open-ended learning experience where the

methods and results are not easily measured and may not be the same for each

learner.

Jonassen (1991) pointed out that the difference between constructivist and

objective (behavioural and cognitive) instructional design is that objective

instructional design has a predetermined outcome and intervenes in the learning

process to map a pre-determined concept of reality into the learner’s mind.

Constructivism, however, maintains that because learning outcomes are not always

predictable, instruction should foster, not control, learning.

Constructivism and objectivism are viewed as two extremes on a continuum

with most designers and theorists assuming positions that fall somewhere between

the two views (Jonassen, 1991). Merrill (1991) expressed that constructivism

continues to face many hurdles and constructivist interventions are currently viewed

as labour intensive, costly to develop, requiring expensive technology and difficult to

evaluate. On the other hand, Jonassen (1991) concluded that constructivist views

conflict with socially accepted goals for schools and could require a larger revolution

that would re-conceptualise the outcomes of education from a societal perspective so

that alternate goals, processes and perspectives of the world are accepted. Duffy

and Jonassen (1991) were among those who presented an optimistic future for

constructivism when they said, “Viewed as an alternative perspective that includes a

wide range of instructional strategies, constructivism is a pedagogical view that can

be applied to most if not all learning goals”.

Although instructional design emerged from a behaviourist learning theory,

new insights to the learning process will alter, change and replace the design. This is

due to the information age that rapidly increases and changes information.

47

Numerous technological advances have made the shift of paradigm from objectivism

to constructivism in instructional design possible.

Since chemistry has been reported as a difficult, abstract and formal subject,

investigation would provide insight into how relevant learning difficulties can be

overcome in a learning environment based on constructivism.

2.3 The Learning Environments

There are two major learning environments, the Objectivist Learning

Environment (OLE) and the Constructivist Learning Environment (CLE) recognised in

the review of literature. The OLE employs the traditional instructional design model

and principles, such as Gagne, Wager & Briggs’ Principles of instructional design

(1992) whereas the CLE can be represented with Spiro’s Cognitive Flexibility Theory

(1992), Jonassen’s Constructivist Learning Environment (1999) and Hannafin, Land

and Oliver’s Open Learning Environment (1999). The traditional models are

associated with behaviourism and cognitive science (Moallem, 2001). Behaviourism

is said to influence the traditional design models by providing prescriptions about the

correlation between learning conditions and learning outcomes. Moallem asserted

that cognitive science has also contributed to traditional models by emphasising the

learner’s schema as an organised knowledge structure. On the other hand,

constructivism is said to have many roots in social psychology and social learning

paradigms.

2.3.1 The Objectivist Learning Environment

The objectivist approach is essentially pouring information into the learner.

Learners may be told about the world and be expected to replicate its content and

structure in their thinking (Jonassen, 1991). Vrasidas (2000) identified the models of

instructional design based on the objectivist philosophy as the input-process-output

model. Before designing the instruction for a topic, the educator identifies the

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knowledge that he/she intends to transfer into the minds of the learners. All the

learners are expected to achieve the learning objectives in the same manner. The

evaluation procedure entails using an objective evaluation method to determine

whether the objectives have been met and to what degree. For the objectivist

instructional designer, learning would be shown by observable behaviour. It is a

direct expression of behaviourism and the assumption that learners respond to

stimuli with a certain level of predictability (Dalgarno, 1996). Dalgarno further stated

that the design has drill and practice sequences that are based on programmed

behavioural instruction.

Schon (1983) included a certain level of technical rationality, the development

of means to address predetermined goals in a traditionally computer-mediated

instructional design. Similarly, Wilson (1997) quoted that in an Objectivist Learning

Environment, the learning process is expressed as a clear demarcation between

theory and practice, moving the learner from basic to applied knowledge and finally,

the practice of this knowledge. In an objectivist environment, the learners use the

computer as a tool to complete a task or to get something done and inevitably, do not

concentrate on the broader environmental context of the individual (Jonassen, 1991).

Mergel (1998) stated that cognitivism and behaviourism are both governed by

an objective view of the nature of knowledge and what it means to know something.

The transition from behavioural instructional design principles to those of a cognitive

style was not entirely difficult.

For the purpose of this study, the courseware for the Objectivist Learning

Environment was designed and developed based on the above discussion.

Skinner’s stimulus-response pattern of conditional behaviour was used as a

guideline. His theory dealt with changes in observable behaviour; ignoring the

possibility of any processes occurring in the mind. The learner operates on the

environment and receives a reward for certain behaviour. Eventually, the bond

between the operation and the reward stimulus is established. The courseware

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also consisted of components for e-learning suggested by Clark and Mayer (2003)

and multimedia design and development by Alessi and Trollip (2001). The nine

events of instruction by Gagne were also used to design the courseware.

2.3.2 The Constructivist Learning Environment

Duffy and Jonassen (1992) documented that constructivists believe that

knowledge and truth are constructed by the learner and do not exist outside his/her

mind. Therefore, Moallem believed that according to constructivists, learners

construct their own knowledge by actively participating in the learning process.

Mergel (1998) expressed that the technology advances of the 1980s and 1990s

enabled designers to move toward a more constructivist approach to design

instruction. The hypermedia and hypertext are said to be among the most useful

tools for the constructive designer since they allow for a branched design rather than

a linear format of instruction.

Mergel (1998) noted that a novice learner might wander aimlessly through

hypermedia if he/she fails to establish an “anchor” in a hypermedia environment and

becomes completely disorientated. Therefore, students must possess some

background knowledge and be given some instruction in developing their own

metacognitive strategies and some way to return along the path they have taken,

should they become “lost” (Davidson, 1998).

Since most literature on constructivist design suggest that learners should be

provided with a guided instruction, instruction and learning should be designed in an

environment with some collaboration between the objectivist and constructivist

designs.

Jonassen (n.d.) suggested that a constructivist design process should be

concerned with designing environments that support the construction of knowledge.

Mergel (1998) documented that to design from a constructivist approach requires the

designer to produce a product that is much more facilitative in nature than

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prescriptive. The content is not pre-specified, direction is determined by the learner

and assessment is much more subjective because it does not depend on specific

quantitative criteria, but rather on the process and self-evaluation of the learner. The

standard pencil-and-paper tests of mastery learning are not used in constructivist

design; instead evaluation is based on notes, early drafts, final products and journals.

Dunlap (1999) pointed out that constructivist environments are interactive,

collaborative, student-centred, active, based on authentic content and allow

intentional learning. The five attributes of student-centred learning are intentional

learning, applying dynamic learning activities, utilising authentic learning contexts,

encouraging collaboration and reinforcing reflection. Dunlap elaborated that

intentional learning is purposeful, effortful, self-regulated and active. Intentional

learning gives learners ownership, the ability to find the content more relevant and

offer life-long learning skills. Dunlap said that intentional learning should allow the

learners to manage their own learning by identifying their learning needs, select

learning strategies and assess their own learning.

In the constructivist setting, learners are also expected to apply dynamic

learning activities by engaging in activities rather than being observers. Learners

must be able to access the Internet resources during their learning process. In

authentic learning, students need to establish goals that are meaningful to them.

This refers to the selection of topics relevant to their learning and of meaning or

interest to them. Collaboration should also be encouraged since it exposes learners

to multiple perspectives, refines their knowledge through argumentation and

structured controversy, shares and tests their knowledge, produces gains and

appreciation for the value of individual strengths and makes the learner more willing

to take risks during authentic situations.

In this study, the multimedia constructivist learning environment was designed

based on the Jonassen’s (1999) Constructivist Learning Environment model. Alessi

and Trollip’s (2001) model was used to design and develop the courseware. The

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courseware also consisted of components from e-learning proposed by Clark and

Mayer (2003).

2.4 Literature Review on Variables

There are three types of variables in this study, the independent variables, the

dependent variables and the moderator variables. The independent variables are the

approaches to teach “Chemical Formulae and Equation”. The two approaches

employed here are the MCI and MOI. The dependent variables are the achievement

score based on the pretest and posttest results and the IMMS score from the

implementation of the MCI and MOI. This study employs ability level, cognitive styles

and gender as the moderator variables. In the following sub-sections, a brief

literature review will be given on studies by other researchers related to these

variables.

2.4.1 Multimedia Learning and Achievement

Barron and Orwig (1995) defined multimedia as a combination of some or all

of the elements of text, graphics, animation, sound and video, using a computerised

platform. However, Clark and Mayer’s (2003) definition of multimedia is more

comprehensive and precise. They defined multimedia as a combination of content

and instructional methods that encourages learners to engage in active learning by

mentally representing the materials in words and in pictures, making connections

between the pictorial and verbal representations.

The infusion of multimedia into teaching and learning has altered

considerably the instructional strategy in our educational institutions and changed the

way teachers teach and students learn (Neo, 2003). Bowyer and Blanchard (2003)

stated that multimedia is becoming more widely recognised as an effective

enhancement mechanism for education, in concert with the continual expansion of

the technological arena. With the increased dissemination of information

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technologies in education, learning from multimedia has emerged as a significant

medium of instruction. Jonassen (1992) and Rieber (1996) stated that the field of

instructional technology has witnessed tremendous growth in the research and

development of interactive multimedia learning environments, especially computer-

based environments.

Bowyer and Blanchard (2003) investigated multimedia regarding its utility as

an enhancement mechanism, primarily for distance education students in the first

year university course. 80% of the students were able to make use of the multimedia

files to prepare for the practical component of the course before on-campus

attendance. When surveyed regarding the value of this learning resource

enhancement, the vast majority of these students agreed that effective learning,

understanding and notably relaxation were all significantly enhanced.

Harwood and McMahon (1997) explored the effects of an integrated video

media curriculum enhancement on students’ achievement and attitudes in a first–

year high school general chemistry course within a multi-culturally diverse

metropolitan school district. Their research revealed that the integrated video media

curriculum intervention could positively affect students’ chemistry achievement

across all levels and across a diverse multicultural population.

Chang (2000) investigated the relative effectiveness of a problem-solving

based computer-assisted instruction and a lecture-internet-discussion instruction on

Taiwan senior high school students’ science achievement and attitudes toward

science. The findings showed that students taught using the problem-solving based

computer-assisted instruction scored higher but not significantly higher than the

students taught via a lecture-Internet-discussion instruction. However, there were

statistically significant differences in favour of the problem-solving based computer-

assisted instruction on students’ attitudes toward the subject matter.

Geban, Askar and Ozkan (1992) also reported similar results. They explored

the effects of the computer-simulated experiment and the problem-solving approach

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on students’ achievement, science process skills and attitudes towards chemistry

among students in Turkey at the high school level. They found that the computer

simulated experiment approach and the problem-solving approach produced

significantly greater achievements in chemistry and science process skills than the

conventional approach.

Cracolice and Abraham (1996) performed an investigation on the computer-

assisted instruction and semi-programmed instruction as replacements for traditional

recitation/discussion in general chemistry. Student performance on problem-solving,

and their attitudes and the effect of formal reasoning as a covariate were other

variables that were examined. Their study pointed out that all methods of the

teaching recitation/discussion sessions were equally effective for simple exercises.

However, for more difficult exercises, the semi-programmed instruction was more

effective. Students had more positive attitudes using the semi-programmed

instruction than when attending traditional recitation/discussion sessions.

Fong and Ng (1999) investigated the effects of animation using multimedia

computer-based learning on the learning of mitosis among students of different

psychological profiles. They reported that learners using the animated graphics had

a higher gain score but was not significant and the field-independent students did not

attain greater gain scores that those who are field-dependent.

Neo (2003) focussed on designing a course which is oriented towards a

constructivist based paradigm by using multimedia as an instructional tool, and

students were active learners, involved in constructing their own knowledge in the

learning process and determining how to reach their own learning outcomes. The

findings showed that multimedia technology could be used as an efficient

instructional tool in creating a constructivist based learning environment in a

Malaysian classroom, whereby students can learn to inculcate interpersonal and

collaborative learning skills in a learning community. The multimedia mediated

constructivist learning model was able to enhance student learning and a learning

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process in which students participated actively in a media rich environment and in an

innovative manner.

Chen (2005) designed, developed and evaluated a virtual reality (VR)-based

learning environment with the aim to investigate the various issues that are related to

the use of this technology in teaching and learning. Chen conducted this study using

multimedia embedded with a constructivist learning environment and reported that

the Guided VR had significant positive effect on learning, as measured by the

learners’ performance (gain scores) in the VR-based test. Learners exposed to the

Guided VR mode significantly outperformed the learners exposed to the Non-Guided

VR. Learners exposed to the Guided VR mode also significantly outperformed their

Non VR counterparts. However, the performance of the learners exposed to the Non-

Guided VR mode and their Non VR counterparts did not differ significantly.

Another local study conducted by Kong (2006) investigated the contribution of

two different instructional strategies, the Constructivist-Strategies Instruction (CSI)

and Direct Instruction (DI), using similar validated multimedia materials on learning at

different levels of knowledge tasks for learners with different psychological profiles on

the chemistry topic of the Periodic Table. It was concluded that CSI was more

effective than DI for higher order knowledge tasks, and the effects of CSI were

stronger for high ability and high internal LOC learners. However, it was found that DI

and CSI were equally effective for lower order knowledge tasks. According to Kong,

by integrating multimedia resources into the lessons protocol prescribed for the

study, both modes of instruction have potentials in promoting learning, depending

upon the inherent nature of the topic in chemistry.

In contrast, Wainwright (1989) designed a study to evaluate the attributes of a

microcomputer software package used as a supplement to chemistry instruction

within the setting of a traditional public high school. During a unit of study of writing

and naming formulae and balancing chemical equations, the experimental group

received reinforcement via the computer while the control group used parallel

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worksheet exercises over a period of three weeks for concept reinforcement.

Analysis of achievement scores indicated significantly higher scores among the

students in the control group. The study also investigated the relationship between

the treatment (CAI versus worksheet) and the development stage to ascertain any

possible differential effects favouring either method for students at varying cognitive

development levels. This showed no significant interactions.

From the above review of literature, a majority number of studies supported

the implementation of multimedia instruction or computer-assisted instruction in

enhancing students’ achievement. However, as far as the researcher is aware, there

are very few international studies and hardly any local studies have investigated the

effects of a multimedia constructivist environment in the learning of chemistry.

Therefore, it is worthwhile to investigate and compare the effect of different

multimedia instructions especially in chemistry in a typical Malaysian secondary

school.

2.4.2 Multimedia Learning and Motivation

The huge volume of information that is increasing tremendously and changing

rapidly with technology capabilities has created a new paradigm in education. Some

of the pedagogical innovations include instruction delivered through CD-ROMs,

intranets and the Internet. Pulist (2001) documented that learner motivation in the

online learning environment becomes crucial since motivation and learner

achievement have a positive and robust correlation. Therefore, in this new paradigm

of education, students should be provided with a conducive learning environment

such as the constructivist environment with well-designed multimedia instruction.

Malone (1984) identified challenge, fantasy and curiosity as the three

important characteristics of learner motivation. Challenge involves providing goals

for those whose attainment is uncertain but is not so lofty that failure is predominant.

Fantasy is a state where mental images of things not presented to the senses are

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evoked or within the actual experience of the person involved and may include those

of objects, situations or events. Curiosity is similar to challenges but does not involve

self-esteem. Computers can easily enhance sensory curiosity through the use of

colour, sound and graphics. Cognitive curiosity is generated because people are

motivated to bring to all their cognitive structures, completeness, consistency and

parsimony.

In another study, motivation is defined as a hypothetical construct that broadly

refers to those internal and external conditions that influence the arousal, direction

and maintenance of behaviour (Martin & Briggs, 1986). They explained that

motivation is an umbrella term that encompasses a myriad of terms and concepts

such as interest, curiosity, attribution, level of aspiration, locus of control etc. Ford

(1992) defined the concept of motivation as “the organised patterning of an

individual’s personal goals, emotions and personal agency beliefs”.

Duchastel (1997) explained motivation as a goal and a means within

education. The goal is to stimulate interest in what is considered to be the social

values of the day and the means is to attain other educational goals such as

conceptual and skill development.

According to Pulist (2001), motivation can be seen as a product of interest in

the content, supportive and enjoyable social settings and personal engagement in

meaningful tasks with clear relevant information. Pulist also felt that challenge,

fantasy and curiosity are the main characteristics of an intrinsically motivating

instruction and therefore the learner’s interest, relevance, expectancy and

satisfaction must be included. Pulist also mentioned that motivation is a goal-

directed behaviour instigated and sustained by expectations concerning the

anticipated outcomes of actions and self-efficacy for performing these actions.

However, multimedia instruction is a new paradigm in education that allows

meaningful learning. The factors that might hinder the students to learn in a new

environment include the unrestricted learner control of sequencing and lack of

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learner ability to integrate unstructured information meaningfully (Pulist, 2001).

Therefore, an environment that is capable of overcoming the unrestricted learner

control of sequencing and integrating unstructured information meaningfully must be

designed to increase the students’ perceived motivation towards the instructional

design and the subject matter as well. Small (1997) stated that the major goal of

education is to develop learners who are intrinsically motivated, display intellectual

curiosity, find learning enjoyable and continue seeking knowledge at the end of their

formal instruction.

Many learning-motivational models are available to enhance the teaching-

learning environment. One such model is the ARCS Model of Motivational Design

developed by John M. Keller of Florida State University (Keller, 1987) that is a well-

known and widely applied model of instructional design. Keller’s model suggests

strategies for stimulating the motivation to learn. Small (1997) felt this model is

simple, yet powerful and is rooted in a number of motivational theories and concepts

with the most notable theory, the expectancy-value theory. Keller (1983) identified

“effort” as the major measurable motivational outcome. The two necessary

prerequisites specified for “effort” to occur are that the person must value the task

and believe that he/she can succeed at it. Therefore, Small (1997) believed that in

an instructional situation, the learning task needs to be presented in a way that is

engaging and meaningful to the student and promote positive expectations for a

successful achievement of learning objectives.

Keller (1993) documented that our understanding of how to arouse and

maintain student interest in learning is far behind our knowledge of how to facilitate

learning once he/she has the desire to achieve. He proposed the ARCS model with

four essential strategy components for motivating objectives. Attention refers to

whether the learner’s curiosity is aroused and whether this arousal is sustained

appropriately overtime. Relevance refers to the learner’s perception of personal

need satisfaction in relation to the instruction or whether a highly desired goal is

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related to the instructional activity. Confidence refers to a positive expectation for

success achievement, and the extent to which success is under learner’s control.

Satisfaction refers to the combination of extrinsic rewards and instrinsic motivation,

and whether these are compatible with the learner’s anticipations.

Mistler-Jackson and Songer (2000) presented a case study of one class

participating in the Kids as Global Scientists (KGS) Programme, a project that

engages students in the study of atmospheric science through the use of authentic

images and online communication. The authors examined the motivational effect of

KGS through an in-depth study of six students representing three levels of

motivation. The findings indicated that they made significant gains in weather

content knowledge (as measured by written assessments) and showed a high level

of motivation.

Beal, Walles, Arroyo and Woolf (2007) conducted a study of a controlled

evaluation for high school mathematics problem solving. The experimental group

was assigned an interactive online tutoring system whereas the control group was

assigned regular classroom instruction. The tutoring group students improved in the

posttest but the effect was limited to problem-solving skills tutored via the online

system. However, the control group students showed no improvement. Therefore,

the use of interactive multimedia hints at a predicted a pretest to posttest

improvement. In addition, the students with the weakest mathematics skills appear

to benefit most from interactive tutoring.

Toh (1998) conducted a study on cognitive and motivational effects of two

multimedia simulation presentation modes on science learning. The study found that

the students showed higher motivation when presented with concurrent simulation

presentation mode.

The above reviewed studies showed that the perceived motivation of

students towards instruction has improved with multimedia instruction compared to

classroom instruction. This study investigated whether students improve in their

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perceived motivation towards instruction as well as the subject matter using

multimedia instruction in a constructivist environment.

2.4.3 Ability Levels

Several investigators have suggested that computers in education may

provide their greatest potential for disadvantaged students, those of low ability and

concrete operational students. Cavin and Lagowski (1978) investigated the effects of

computer simulated or laboratory experiments and student aptitude on achievement

and time in a college general chemistry laboratory course. They reported that

computer simulated experiments are especially useful for low-aptitude students, both

as a laboratory experiment substitute and as supplement, while being a satisfactory

means of instruction for higher-aptitude students.

Kong (2006) investigated the contribution of two different instructional

strategies, the Constructivist-Strategies Instruction (CSI) and Direct Instruction (DI),

using similar validated multimedia materials on learning at different levels of

knowledge tasks for learners with different psychological profiles on the chemistry

topic of the Periodic Table. It was concluded that CSI was more effective than DI for

higher order knowledge tasks, and the effects of CSI were stronger for high ability

and high internal LOC learners. However, it was found that DI and CSI were equally

effective for lower order knowledge tasks.

This finding is supported by Yea-Ru Chuang (1999), who investigated the

presentation effects of text, oral narration and computer animation implemented in an

instructional lesson. The students with low mathematics achievement performed

significantly better on the posttest. Similarly, Huppert, Lomask and Lazarowitz

(2002) discovered that students with low reasoning abilities performed better than

those with high reasoning abilities. Huppert et al. explored the impact of a biology

simulation on high school students’ academic achievement and science process

skills in Israel.

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However, Ardac and Sezen (2002) stated in their study that computer-based

instruction (with or without guidance) was observed to be more effective than regular

instruction in improving process skills particularly for students with high chemistry

achievement.

From the review of literature, it is documented that each individual (low ability

and high ability) performed differently using computers in education. However, it is

vital to design and develop a multimedia instruction that will benefit both the low-

ability students as well as high-ability students. Therefore, this study made attempt

to design a multimedia instruction in a constructivist environment to improve the

achievement of students with low ability as well as high ability and their perceived

motivation towards the subject matter and the mode of instruction.

2.4.4 Cognitive Styles

Cognitive styles have an impact on individual, vocational and organisational

learning. The field dependence-field independence domain is one of the cognitive

styles that refers to the relatively persuasive way individual learners acquire structure

and process information (Pithers, 2002). It also reflects on how people perceive,

think, solve problems and learn. Witkin and Goodenough (1981) defined field

dependence-field independence as an indication of the degree to which an individual

uses external or internal cues.

Many studies have documented the differences in learning styles that exist in

the formal education setting. Canino and Cicchelli (1988) proposed that learning

achievement for field-independent students would be highest when their learning

activities offer minimal guidance and encourage discovery methods. On the other

hand, they found that learning achievement for field-dependence students would be

highest when they are placed in an environment with more guidance, including

instructional techniques like teacher-centred presentations.

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Field independence versus field dependence is probably the most well-known

style. At a perceptual level, field-independent personalities are able to distinguish

figures as discrete from their backgrounds compared to field-dependent individuals

who experience events in an undifferentiated way. It is also said that field-dependent

individuals have greater social orientation relative to field-independent personalities.

Studies have identified a number of connections between this cognitive style

and learning. Field-independent individuals are likely to learn more effectively under

conditions of intrinsic motivation (e.g., self-study) and are influenced less by social

reinforcement (Messick, 1978). This result was also supported by Yea-Ru Chuang

(1999), who stated that their subjects performed significantly better on the posttest

“animation + text + voice” multimedia computer environment.

Meshot (1991) investigated the effects of real-time motion versus the still

frame presentation mode and cognitive styles on an interactive hypermedia

knowledge task amongst second grade students. The results showed that the field-

independent students scored higher than the field-dependent students. Similar

results were produced by Chang (1995), who studied the effects of the hypertext

document design and cognitive styles on information searching performance among

undergraduate students.

In another study, Hepner (1994) also proved that field-independent students

scored significantly higher than field-dependent students on the achievement test.

The researcher examined the effects of varying levels of visual complexity in

computer-animated graphic presentations. Similarly, Myers (1997) contributed more

strength to the above citations. Myers investigated the interaction between cognitive

styles and different visual presentation formats used in a computer-based tutorial on

human tissue samples.

Liu and Reed (1994) conducted a study in hypermedia-assisted language

learning and reported that field-independent students tended to create their own

structure while working with the hypermedia setting whereas field-dependent

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students were more prone to follow the structure imposed by the software. The field-

dependent students also developed a more spectator and social approach to

learning.

However, Wey (1992) revealed that field-dependent college students

performed slightly higher in the treatment using the text with a graphic interface than

the same type of learners in the text-only interface treatment. According to the

researcher, this finding indicated that the presentation of text with a graphic interface

might be somewhat beneficial to field-dependent learners as the graphic interface

provides external cues for them and results in better performance.

Summerville (1999) also reported a different result when no significant

differences were found in achievement and satisfaction but interviews disclosed that

the field-dependent students preferred more step-by-step instructions with more

human direction.

Although many studies have been conducted to investigate the effects of

field-dependence cognitive styles in various education settings, only a few of them

were carried out in a multimedia environment. The above citations gave evidence

that in general, field-independent students show significant achievement than the

field-dependent students.

2.4.5 Gender

The role of education in creating achievement difference on the basis of

gender has been a source of controversy in schools. Shashaani (1995) expressed

that concerns exist regarding the extent to which the differences identified in the 70s

and 80s associated with the achievement of women in mathematics are recurring in

the area of information technology.

Allen (2004) expressed that science has been a traditionally male-dominated

field. Frenkel (1990) revealed that many women who are opting for careers in

technology are moving away from academia, or do not pursue advanced degrees,

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but rather enter industry jobs. Feldman (2002) stated that the purpose of an

education is to empower individuals with the tools necessary to flourish in an ever-

changing world, to allow them to dream and to enable them to accomplish their goals

in life. The author further commented that females are unable to achieve this

because the educational system puts them at a disadvantage by marginalizing them

and placing their needs in learning below those of males.

Similarly, Yea-Ru Chuang (1999) documented that male students performed

significantly better on the posttest in an “animation + text + voice” version of a

multimedia computer environment in Taiwan.

On the contrary, Kumar and Helgeson (2000) reported no significant

difference between male and female high school students solving stoichiometric

chemistry problems using the hyper-equation software on Macintosh computers

when conducting an investigation on the effects of gender on computer-based

chemistry problem solving. Similarly, Puhan (2002) also indicated that cognitive

processing differences between males and females did not lead to performance

differences in different content areas in science. Another study that offered no

significant difference was by Huppert et al. (2002), who experimented computer

simulations in high school.

However, Bain et al. (1999) reported that the technological competency of

female participants in high access integrated computer programmes exceeded that of

male counterparts who participated in a programme of reduced access and

integration.

A number of studies show that males outperformed females when exposed to

the multimedia instruction environment. Since no similar study has been conducted

in Malaysia, it is worthwhile investigating whether there are any differences between

male and female students achievement towards chemistry. As far as the researcher

is aware, no local or international studies that have been conducted to compare the

effects of gender in a MCI or MOI setting. So this study would provide some insight

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into whether multimedia instruction is suitable for both males and females. It will also

provide information on the instruction, the MCI or MOI that is more suitable for both

males and females.

2.5 The Theoretical Framework

The theoretical framework of this research is based on the following theories

and models. They are the Constructivist Learning Environment by Jonassen (1999),

e-learning and the Science of Instruction by Clark and Mayer (2003) and the ARCS

Model of Motivational Design developed by Keller (1987).

2.5.1 Model for Designing the Constructivist Learning Environment (CLE)

Many instructional designers are working to develop more constructivist

environments and instructional prescriptions to convert the constructivist learning

theory into practice. In an Educational Technology Convention (2003), Toh stated

that Jonassen (1999) presented the most articulate and comprehensive model that

represents an integration and crystallisation of much work in the constructivist arena

into a coherent instructional and prescriptive framework (Figure 2.1).

The model conceives of a problem, question or project as the central focus of

the environment. The instructional designer has developed various interpretative and

intellectual support systems to guide the learner to solve a problem. The goal of the

learner is to interpret and solve a problem or complete the project. The five

components developed by the designer as support tools or systems for this model

are related cases, information resources, cognitive tools, conversation/collaboration

tools and social/contextual support.

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6. Social

5. Conversation A. modelling 4. Cognitive 3. Information 2. Related Problem/project 1.1 Context

Problem/Project 1.2 Representation

C.Scaffolding Problem/Project

1.3 Manipulation space Cases

Resources B. Coaching

Tools

Collaboration Tools

Contextual Support

Figure 2.1 The Constructivist Learning Environment Model (Jonassen, 1999)

2.5.1.1 The Question/Case/Problem/Project

The learner’s main focus in this CLE model is the question, case, problem or

project that he/she attempts to solve or resolve. In this constructivist approach, the

problem drives the learning, rather than acting as an example of the concepts and

principles previously taught; the students learn domain content in order to solve the

problem, rather than solving the problem as an application of learning. The problems

identified should not be topical as in the textbooks, but what the practitioners do. The

problems and issues that need to be resolved can be found in the newspapers and

magazines. The problems need to include three integrated components, namely, (1)

the problem context, (2) the problem representation or simulation and (3) the problem

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manipulation space. Each problem should be represented in the environment in

order to develop a CLE.

(a) The Problem Context

The description of the context in which a problem occurs is essential for a

problem representation. The same problem will differ in various social or work

contexts. The learner must be led onto the performance environment information.

Description on the situation, such as the physical, socio-cultural and organisational

climate surrounding a problem, should be made available for the learners to

understand the problem.

The CLE must also give details on the community of practitioners, performers

and stakeholders. The information on the values, beliefs, socio-cultural expectations

and the customs of the people involved, the sense of social or political efficacy felt by

the members of a setting or organisation, the skills and performance backgrounds of

performers, must be provided. The learner should also be told of the experience,

hobbies, traits and beliefs involved in a certain community. Such information can be

conveyed in stories or interviews of key personnel in the form of audio or video clips.

(b) Problem Representation/Simulation

The problem context and problem representation become a story about a set

of events that leads up to a problem that needs to be resolved. The problem has to

be interesting, appealing and engaging for a learner to be simulated and perturbed.

Problems can be introduced on high quality video scenarios, virtual reality or

narratives in the form of text, audio or video. The CLE recommends the learner to be

engaged in authentic problems. Some designers suggest that authenticity refers to

supporting the performance of specific real-world tasks. Most educators believe that

“authentic” means the learners should engage in activities which present the same

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type of cognitive challenges as those in the real world; the tasks which replicate the

particular activity structures of a context (Savery & Duffy, 1995).

(c) Problem Manipulation Space

Mindful activity is a critical characteristic of learning in which an active learner

must manipulate something such as to construct a product, manipulate parameters

and make decisions to affect the environment in some way. According to the activity

theory, there are transformational interactions among the learner, the object that the

learner is acting on, and the signs and tools that mediate that interaction. The

problem manipulation space provides the objects, signs and tools required for the

learner to manipulate the environment.

The form of the problem manipulation space depends on the nature of the

activity structures the CLE is engaged in which should provide a physical simulation

of the real-world task environment, the phenomenaria (Perkins, 1991). According to

Jonassen (1996), the phenomenaria or microworld is a simplified model along with

observation and manipulation tools necessary for testing learner’ hypotheses about

their problems. Learner are directly engaged by the world they explore, because

they can obtain the results of their experiments immediately.

Problem manipulation spaces are causal models that enable learner to test

the effects of their manipulations, receiving feedback through changes in the

appearance of the physical objects they are manipulating or in the representation of

their actions, such as charts, graphs and numerical output. The situation must allow

learners to manipulate object or activities (manipulable), ensure the environment

responds in the realistic ways to learner manipulation (sensitive), have high fidelity of

simulation (realistic) and provide relevant feedback (informative).

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2.5.1.2 Related Cases

Learners require experience to construct mental models and to solve a

problem. Since novice learners lack experience, it will be helpful to present of related

experiences to which they can refer to understand the issues implicit in the problem

representation. The related cases in CLEs support learning by (1) scaffolding

student memory and (2) enhancing cognitive flexibility.

Through scaffolding, the learner’ involvement can be compared although not

replaceable. They will compare the past experiences with current problems. In order

to provide a rich set of related cases, it is necessary to collect those that are

representative of current ones, identify the lessons that each can teach, characterise

the situations in which each case presents its lesson and develop an index and

represent its features in a way that allow cases to be recalled. Another alternative is

to provide worked examples of problems.

To enhance cognitive flexibility, it is vital that related cases provide a variety

of viewpoints and perspectives on the case or project being resolved. The cognitive

flexibility theory provides multiple representations of content in order to convey the

complexity that is inherent in the knowledge domain. Multiple representations of

content emphasise the conceptual interrelatedness of ideas and their

interconnectedness. This allows learners to construct their own interpretations by

contrasting the cases.

2.5.1.3 Information Resources

CLEs assume that information makes the most sense in the context of a

problem or application; thus, it is vital to determine what information learners need to

interpret a problem. Learners require information to construct their mental models

and formulate hypotheses in the process of problem investigation. CLEs should

provide rich sources of information and learner selectable information promptly.

Information can be either naturally included in the problem or linked to the

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environment in the form of text documents, graphics, sound resources, video and

animations.

Although the World Wide Web is a powerful new source of multimedia

resources from the Net, learners may lack sophisticated literacy skills to evaluate the

quality of information provided and filter it. Therefore, the information included in, or

linked to, a CLE should be evaluated for its relevance and organised for ready

access in ways that support the kind of thinking expected from the learners.

2.5.1.4 Cognitive Tools

Cognitive tools are expected to assist learners, especially those who lack

required skills to scaffold their ability to perform complex, novel and authentic tasks.

Cognitive tools are generalisable computer tools that intended to engage and

facilitate specific kinds of cognitive processing (Kommers, Jonassen & Mayers,

1992). They are intellectual devices that are used to visualise, organise, automate or

supplant thinking skills. Some cognitive tools replace thinking while others engage

learners in the general processing of information that would take place in the

presence of a tool.

Cognitive tools assist learners in better presenting problems or tasks, their

knowledge and what they are learning. They may offload some of the cognitive

activity by automating low-level tasks or by supplanting some tasks. They include (1)

problem/tasks representation tools, such as visualisation tools including Geometry

Tutor, Weather Visualiser, Climate Watcher, Mathematica and Mathlab, (2) static and

dynamic knowledge modelling tools, articulating knowledge domains such as

databases, spreadsheets, semantic networks, hypermedia construction Stella and

PowerSim, (3) performance support tools, such as algorithmic tasks with calculator,

database shells and note-taking to assist learners in the information collection, and

(4) information gathering tools, such as sophisticated search engines and intelligent

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agents to seek and filter information sources on the Web that are relevant to the

user.

2.5.1.5 Conversation and Collaboration Tools

Scardamalia, Bereiter and Lamon (1994) stated that contemporary

conceptions of technology-supported learning environments assume the use of a

variety of computer-mediated communications to support collaboration among

communities of learners. People learn when they work with a group, not in isolation.

CLEs therefore must be able to help learners to collaboratively construct socially

shared knowledge by providing access to shared information and shared knowledge-

building tools. When a group of people works towards developing a common

conception of a problem, its energies will be focused on solving it. Discourse

communities, knowledge building communities and communities of learners may

support conversation. The medium of communication can be newsletters,

magazines, television shows and computer networks (list-serfs, electronic mail,

bulletin boards, Net-News services, chats, multi-user dimensions and multi-user

dimensions object oriented).

2.5.1.6 Social/Contextual Support

It is important to accommodate contextual factors such as the physical,

organisational and cultural aspects of the environment while designing and

implementing CLEs for successful implementation. Training will be necessary for

teachers and personnel who will be supporting the learning and students who will be

learning from the environments.

2.5.1.7 Supporting Learning In CLEs

In CLEs, learners need to explore, articulate what they know and what they

have learned, speculate (conjecture, hypothesise, test) and manipulate the

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environment in order to construct and test their theories and models; reflect on what

they do, why something works or does not and what they have learned from the

activities.

Exploring attributes of a problem includes investigating related cases for

similarities, and perusing information resources to find evidence to support solution of

a problem or completion of a project that the CLEs focus on. The cognitive

components involved are goal setting and managing the pursuits of these goals

(Collins, 1991). The cognitive activities engaged while exploring CLEs are

speculating and conjecturing about effects, manipulating the environment, observing

and gathering evidence and drawing conclusions about those effects. Most of the

activities require reflection-in-action (Schon, 1983).

CLEs also need to articulate and reflect the learner’s performance.

Articulation on what the learner is doing in the environment and the reasons for

his/her action and strategies used supports knowledge construction and

metacognition. The learning activities specify the goal for providing instructional

supports such as modelling, coaching and scaffolding in CLEs.

The two types of modelling involved are the behavioural modelling of the

overt performance and the cognitive modelling of the covert cognitive processes.

Behavioural modelling in the CLEs guides the performance of activities identified in

the activity structure whereas cognitive modelling articulates the reasoning that

learners should use while engaging in the activities. Modelling provides learners with

an example of the desired performance. Each discreet action and decision involved

in a performance will be shown to help the learner to solve a problem step by step.

The use of worked examples is a widely recognised method for problem

solving and this enhanced the development of problem schemas and the recognition

of different types of problems based on them. Worked examples move toward

problem-state configurations and their associated moves. These examples should

be augmented by articulation of reasoning (reflection-in-action) by the performer,

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such as thinking aloud while performing. Learners need to know how to develop

arguments to support their solutions to a problem. Performers should overtly model

the kinds of arguments necessary to solve a problem, such as providing reasoning-

congruent visual representations by skilled performers and cognitive tools to

represent the understanding, or reasoning through, of a problem.

Usually a learner will attempt to perform like a “model” by crude imitation.

Later, they will improve by articulating and habituating performance to the creation of

skilled and original performances. Coaching will provide improvements during each

stage of modelling and the role of a coach is complex and inexact. A good coach will

motivate the learner, analyse his/her performances, provide feedback and advice on

the performances and how to learn about how to perform, and provoke reflection on

and articulation of what is learned. The learner may solicit coaching or the coach

might observe this/her performance and provide encouragement, diagnosis,

directions and feedback. A coach can provide motivational prompts, monitor and

regulate the learner’s performance, provoke reflections and stimulate the learner’s

models.

Scaffolding is a more systematic approach to support the learner, focusing on

the task, the environment, the teacher and the leaner. Scaffolding provides

temporary frameworks to support learning and student performance beyond the

learner capacities. Scaffolding represents some manipulation of the task itself by the

system. The system performs part of the task for the student, supplants the student’s

ability to perform some part of the task by changing the nature of the task or

imposing the use of cognitive tools that help him/her to perform, or adjust the nature

of the task. The approaches for scaffolding involve adjusting task difficulty,

restructuring a task to supplant knowledge or providing alternative assessments.

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2.5.2 e-Learning and Instruction

The instruction delivered on a computer by way of CD-ROMs and the Internet

was defined as e-learning by Clark and Mayer (2003). This e-learning consists of the

Usage of instructional methods, such as examples and practices to help

learning

Usage of media elements, such as words and pictures to deliver content and

methods

Build-up of new knowledge linked to learning goals

Clark and Mayer (2003) designed this micro theoretical framework for

education. e-Learning courses include both content (that is, information) and

instructional methods (that is, techniques) that help people learn the content. e-

Learning courses are delivered via computer using words in the form of spoken or

printed text and pictures such as illustrations, photos, animation or video. e-Learning

courses are intended to help learners reach personal learning objectives.

GOAL Performance Analysis

Content Analysis

Design

Development

Testing and Implementation Job Tasks & Training + Instructional Knowledge Content Methods Media Elements

Figure 2.2 The e-Learning Model by Clark and Mayer (2003)

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All e-learning projects should begin with a performance analysis to determine

(a) that training will help meet important goals by filling a gap in knowledge, and (b)

that e-learning is the best delivery solution (Figure 2.2). Both the MCI and MOI

courseware developed for this study consists of training. In the MCI, the training is

embedded indirectly and there are no clearly defined objectives, whereas in the MOI,

the training and the objectives are clearly underlined.

Following the performance analysis, the design of the course is continued by

defining the content needed to achieve the educational objective. Content analysis is

conducted to define the major topics and related sub-topics to be included. The

content of an e-lesson is categorised into facts, concepts, processes, procedures and

principles (Table 2.1). At the completion of the content analysis, the designers will

create a course blueprint that includes outlines and learning objectives. This will

include the writing of a detailed course script and the selection of specific

instructional methods to support learning.

Table 2.1 Five Types of Contents in e-Learning

Content Type

Definition

Example

Fact

Specific and unique data or instances

The school log-on screen

Concept

A category that includes multiple examples

Web-page password

Process

A flow of events or activities

Performance assessment process

Procedure

Task performance with step-by-step actions

How to go about the system

Principle

Task performed by adapting guidelines

How to assess achievement

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Instructional methods are techniques, such as examples, practice exercises

and feedback, which support the learning of the content. The media elements

supporting the instructional methods are the audio and visual techniques used to

present words and illustrations. They include text, narration, music, still graphics,

photographs and animation.

e-Learning is designed to achieve two types of goals, that is the “inform

programmes” and “perform programmes” (Table 2.2). Lessons that are designed

primarily to build awareness or provide information are “inform programmes”. An

introduction towards the “Periodical Table” that reviews the history of the periodical

table development is an example of an “inform programme”. The primary goal of

such a programme is to share information with no specific expectations of new skills

to be acquired. In contrast, lessons that are designed to build specific skills such as

problem-solving skills are “perform programmes”. Some relevant examples are the

experiments on a substance to list their criteria. In this study, the MCI is designed

according to the “perform programmes” whereas the MOI is designed according to

the “inform programmes”.

Table 2.2 Inform and Perform e-Learning Goals

Goal

Definition

Example

Inform

Lessons that communicate information

History

Perform-Procedure

Lessons that build procedural skills (also called “near transfer”)

Case studies

Perform-Principle

Lessons that build principle-based skills (also called “far transfer”)

Problem solving

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The perform goals consists of procedural steps or tasks, also known as the

“near transfer” and principle-based ones, also known as “far transfer”. Procedural

lessons are designed to teach step-by-step tasks and are performed more or less the

same way each time. The steps that are learned are identical or very similar to the

steps required to solve problems. This type of learning is called “near transfer”

because the transfer from learning and application is near. This is similar to

application process in the cognitive model (Bloom, 1979).

Principle-based lessons are designed to teach tasks that comprise multiple

approaches or outcomes. Therefore, the situations presented in the learning process

may not be exactly the same as the situations that occur in a given problem. The

learner is expected to adapt guidelines from the principle-based lessons to various

problem solving.

According to Clark and Mayer (2003), both cognitive theory and research

results encourage the multimedia principle and the usage of both words and pictures

in instructional presentations. The MCI and MOI designed for this study include

multimedia presentations to encourage learners to engage in active learning by

mentally making connections between pictorial and verbal representations.

In designing e-learning courseware, it is also evident that learning gains are

obtained from presenting text and graphics in an integrated fashion on the screen,

compared to the same information presented separately. This is called the contiguity

principle (spatial contiguity) in which corresponding graphics and printed words are

placed near each other on the screen. For example, when the graphic is a diagram

showing parts of an object, the printed names of the parts should be placed near the

corresponding parts of the diagram, using the pointing line to connect the name to

the part. Similarly, when the text describes an action or state depicted in an

illustration, the text can appear as a small pop-up message that appears when the

mouse touches the graphic. This contiguity principle is used in designing the

glossary for both MCI and MOI courseware.

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Clark and Mayer (2003) recommended the modality principle, the use of

audio rather than onscreen text to describe the graphics that is evident to produce

significant learning gains. The rationale for this recommendation is that learners may

experience an overload of their visual/pictorial channel when they simultaneously

process graphics and the printed words that refer to them. Since the crucial initial

step in e-learning is to attend to relevant words and pictures, e-learning courses

should be designed to minimise the chances of overloading the learners’

visual/pictorial channel.

Multimedia Sensory Memory Working Memory

Spoken Ears Phonetic Words Processing Pictures Eyes Visual Processing

Figure 2.3 Access of Visual and Auditory Channels with Presentation of Narration and Graphics (Clark & Mayer, 2003, p. 91)

According to the cognitive theory of learning, people have separate

information processing channels for visual/pictorial processing and for auditory/verbal

processing (Figure 2.3). When learners face concurrent graphics and onscreen text,

both will be processed initially in the visual/pictorial channel. Since the capacity of

each channel is limited, both must compete for the same limited visual attention. In

contrast, when the verbal explanation is in speech form, it enters the cognitive

system through the ears and is processed via the auditory/verbal channel.

Simultaneously, the graphics enter the cognitive system through the eyes and are

processed in the visual/pictorial channel. Thus, neither channel is overloaded but

both words and pictures are processed.

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Some e-learning utilises the redundant onscreen text technique that illustrates

graphics using words in both onscreen text and audio narration in which the audio

repeats the text. It is evident that the learners will learn more deeply from multimedia

presentations excluding redundant onscreen text rather than including it. This is due

to the limited capacity of the human information processing system that is better

presented with less material than more material. Therefore, the MOI utilises the

modality principle in presenting the learning objectives to avoid an overload of the

students’ visual/pictorial channel. However, this principle was not used in the MCI

since the objectives were not presented clearly in the form of statements.

e-Learning should promote a psychological engagement between learners

and the lesson content in ways to help learners to select, integrate and retrieve new

knowledge. First, they must select the important information and then integrate the

new information into existing knowledge in the long-term memory. Finally, they must

be able to retrieve new knowledge from the long-term memory when they are solving

a new problem. Effective e-learning will support these three processes by providing

practice exercises with features that mirror the physical and psychological

environment of a problem. These practices should be interspersed throughout a

lesson rather than be placed completely at the end. This psychological engagement

is employed in designing the MCI courseware.

In applying the contiguity principle to the design of practice exercises,

directions for the exercises must be clearly distinguished and feedback is linked to

the response close to the response area and to the question. The correct options

should be highlighted in multiple choice or multiple select items. After reading the

feedback and reviewing the correct options, learners will be able to see whether their

response was or was not appropriate for the question. This contiguity principle was

used to design the MCI and MOI.

According to the modality principle, audio should be used to explain graphics

in a lesson, but the former is too transient for practice exercises. Directions in the

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form of instructions or information should be presented in the text on the screen while

the learners decide on a response. Feedback should also be accessible in the text

format to enable the learners review their responses in relationship to the question.

Based on the redundancy principle, e-learning should use text alone instead of

narrated onscreen text directions or practice exercises. Practice exercises also

should include the personalisation principle, that is, the conversational language and

virtual coaches called agents to assist in the form of hints, worked examples and

demonstrations.

Recent research provided evidence that substituting worked examples with

practices exercise would save learning time and effectively teach lessons. A worked

example is a step-by-step demonstration of how to solve a problem. It is believed

that these worked examples reduce mental work and are among the most powerful

methods one can use to build new and rich knowledge in the long-term memory; they

are popular with learners. Although traditional wisdom dictates that the best way to

learn to solve problems is to practise solving numerous problems, replacing some

practice problems with worked examples might reduce mental overload. This applies

primarily to courses for novice learners who are most susceptible to cognitive

overload.

The most efficient way to present material in a lesson is to present an

example followed by a similar problem to solve immediately. In applying the

contiguity principle for worked examples, the text should be placed close to the

graphics it is explaining to assist with integration. In the MCI courseware, there are

case studies on the top-menu bar for every problem displayed. The students should

be able to access similar examples on the problem. However, it is better to avoid

adding duplicate text to audio narration to avoid overloading the visual channel of the

working memory. According to the modality principle, audio should be used to

explain graphics in the lesson. Learning will also be improved by imposing the

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personalisation principle on working examples that are to include the conversational

text and virtual coaches (the agents).

Studying examples that are different is more effective than studying examples

that are similar because the former support transfer better. Both MCI and MOI give

sets of examples that are different but the objectives are similar. The objectives in

the MCI is in inductive approach whereas, the objectives in the MOI is in deductive

approach. The examples in the MCI were embedded within the problem solving

process whereas the examples in the MOI were given as a separate activity. Thus,

the multiple varied worked examples are utilised to solve problems. Some learners

make better use of worked examples than others, and the techniques of those

successful learners can be taught to others. In e-courseware, look for lessons that

help students learn from worked examples should be highlighted as they teach them

how to self-explain.

In e-learning, learner control, in which navigational features allow learners to

select the topics and instructional elements they prefer, has always been popular.

The navigational options are designed based on learner preferences, production

costs and time, criticality of skill attainment and the profile of the learning audience.

When e-learning allows learners to select topics, controls the pace at which they

progress and decides whether to bypass some lesson elements such as examples or

practice exercises, it is said the instruction offers learner control. In contrast, e-

learning is under programme control when the course and lesson offer few learner

choices. Programme control gives better results during initial learning, whereas

learner control is more effective at later stages. Learners with high prior knowledge

or high meta-cognitive skills will be able to follow e-learning with learner control very

effectively. There are many navigational features to enable students to select their

preferences better in the MCI courseware whereas there are a few navigational

features in the MOI courseware (not for preference selection).

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When designing programmes with high learner control, navigation controls

should be set such that critical aspects of the programmes (such as examples or

practice exercises) are the default options. If the learner is not responding correctly

to questions, the programme automatically provides more information. Adaptive

control is a process in which the content of the training is adjusted by the instructional

programme based on an evaluation of how learners are responding. Advisement is a

variation of adaptive control that leaves learner control in place. In advisement the

computer programme assesses learner needs based on their responses. Adaptive

instruction with advisement leads to better learning outcomes than straight learner

control, especially among learning audiences with mixed background knowledge and

skills. Popular learner-control options can be maintained and time can be saved. In

the MOI courseware, the navigational buttons help the students to go along with the

tutorials whereas, in the MCI courseware, there are navigational buttons (in default

options) for the students to solve problems by gaining information and looking into

case studies.

In designing e-learning with problem-solving skills, opportunities are provided

to the learners on how to solve a problem by making mental processes as well as the

products of problem solving explicit. Success in problem solving relies on the

following:

Cognitive skills – the facts, concepts and procedure unique to a skill field

Meta-skills – the ability to plan, monitor and assess actions associated with

problem solving

Motivation – an investment of effort to persist and solve the problem

Four guidelines can be applied in designing e-learning to support problem solving:

Use real contexts to build specific problem-solving skills

Provide expert models of problem-solving actions and thoughts

Promote learner awareness on their problem solving actions and thoughts

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Base the lesson on a detailed analysis of problem-solving processes

Both the MCI and MOI designed for this study employed almost all the

features recommended by Clark and Mayer’s (2003) e-learning and instructional

model. However, the MCI and MOI might differ on certain features since the

approach is different. Some of the features suggested by Clark and Mayer are only

suitable for the MCI in the Constructivist Learning Environment whereas others are

only suitable for the MOI in the Objectivist Learning Environment.

2.5.3 The Model for Motivation

The ARCS model of motivation developed by Keller (1987) was used in this

study as a measure of motivation among the Form Four students to investigate their

motivation towards chemistry and multimedia instruction. Keller’s model suggests

strategies for increasing the motivation to learn. ARCS is an acronym for the four

essential strategy components for motivating instruction: Attention, Relevance,

Confidence and Satisfaction (Table 2.3).

Table 2.3 Details of ARCS Model (Keller, 1983) Components

Details

A

Attention strategies for arousing and sustaining curiosity and interest

R

Relevance strategies linked to learner’ needs, interests and motives

C

Confidence strategies that help students develop a positive expectation for successful achievement

S

Satisfaction strategies that provide extrinsic and intrinsic reinforcement for effort

Attention involves the arousal of interest in learners, the stimulation of an

attitude of inquiry and the maintenance of attention. Relevance refers to tying

instruction to make it relevant to the student’s personal interests or goals.

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Confidence refers to the students’ expectations for success and Satisfaction refers to

the process or results of the learning experience. Keller (1983) suggested that his

ARCS model could be used in multimedia environments and also in traditional

learning environments.

2.6 Evaluation Based on the Constructivist Framework

Lawson (1995) developed a taxonomy that is in line with the constructivist

framework. Test items can be classified as empirical inductive (EI) or hypothetical

deductive (HD) according to the thinking patterns required to response successfully

without guessing.

Items are EI if successful response requires the student to do the following:

EI1 classify observations; relate systems to subsystems, classes to sub-

classes;

EI2 apply conservation reasoning to objects;

EI3 establish one-to-one correspondences or serially order a set of

observations;

EI4 understand and apply descriptive concepts-that is, concepts defined in

terms of familiar objects, events, or situations; and

EI5 apply a memorised algorithm or formula.

Items are categorised as HD if successfully response requires the student to

do the following:

HD1 understand and apply theoretical concepts- that is, concepts that

derive their meaning from inferences rather than from direct

experience; use theories and idealised models to interpret data;

HD2 use combinatorial thinking;

HD3 identify functional relationships and apply proportional thinking;

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HD4 understand the general necessity for the control of variables and

recognise hidden assumptions; and

HD5 recognise the implications of probability for experimental design and

data analysis.

2.7 Conclusion

This literature review provides an insightful guidance for the researcher to

plan and conduct the study successfully. This chapter explained the collaboration

between the instructional design and the learning theories. The collaboration is

imperative in producing an effective multimedia instruction with an appropriate

learning environment.

The discussion also included the contribution towards the framework of this

study that was based on the Constructivist Learning Environment by Jonassen

(1999), e-learning and the Science of Instruction by Clark and Mayer (2003), the

ARCS Model of Motivational Design developed by Keller (1987) and evaluation of the

pretest and posttest using Lawson’s (1995) taxonomy.

In addition, the literature review also produced some support on the

moderator variables and also the dependent variables.

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CHAPTER 3

METHODOLOGY

3.0 Introduction

The purpose of the study was to investigate the effects of the multimedia

constructivist environment in solving the learning difficulties in “Chemical Formulae

and Equations” among Form Four students. The sample was chosen from two

schools in a suburban setting. The schools are classified as being suburban by the

Penang State Education Department by virtue of their location. It also attempted to

determine whether any significant difference occurred between the following

demographic variables:

(a) low-ability and high-ability

(b) the field-dependent and field-independent

(c) male and female

in the achievement and IMMS scores using the MCI and MOI. A problem solving-

based multimedia instruction with a constructivist environment courseware and a

tutorial-based multimedia instruction with an objectivist environment courseware

were developed in this study.

This chapter describes the research design, the variables involved and the

research samples and sampling. The various research instruments used are also

discussed, followed by the research procedures. This chapter also explains the data

collection method and data analysis. An overview of the statistical procedures to

analyse the quantitative data obtained from the implementation of the study is also

illustrated. Finally, the necessary assumptions and changes made to improve the

courseware are outlined as the conclusion of this chapter.

3.1 The Research Design

The research design for this study was a quasi-experimental design to

measure students’ achievement in chemistry and their motivation towards chemistry

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and multimedia instruction. The study employed a 2 x 2 factorial design with

repeated measures of the moderator variables. It was designed to investigate the

effects of the independent variables on the dependent variables at each of the two

levels of the moderator variables.

Since the topic, “Chemical Formulae and Equations” will be taught around

March and April, not many schools could be used as the sample. There are also not

many science classes in a school compared to the social science classes. Due to

the time constraint and the small sample, it was not possible to employ the random

assignment of participants. The sampling method used was “cluster sampling” since

the subjects were chosen from an intact groups. The students from both the schools

were randomly divided into two groups. Thus, the non-equivalent control group

design was administered since it involved assignment of intact groups to treatments.

The advantage of using the intact group is to avoid any disorder and any possible

effects from reactive arrangements were minimised (Gay, 1996).

The study employed the pretest and posttest control-group design to measure

the achievement score. This design was tested on two groups, the experimental and

control groups. Both groups were administered with a pretest and each group

received a different treatment. The experimental group was administered with the

MCI whereas the control group was administered with the MOI. At the end of the

study, both groups were given the posttest. The difference between the pretest and

the posttest scores was compared to determine the effectiveness of the treatment.

The research design is illustrated in Figure 3.1.

O1 X1 O2

O1 X2 O2

O1 - Pretest (Achievement) X1 - Multimedia Constructivist Instruction (MCI) O2 - Posttest (Achievement) X2 - Multimedia Objectivist Instruction (MOI)

Figure 3.1 The Achievement Score and Experimental and Control Group Design

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The pretest and posttest measures were the achievement score for

achievement in the topic “Chemical Formulae and Equations”. It was further

designed to investigate the effects of the independent variables on the dependent

variables at each of the two levels of the moderator variables. Analyses for the

moderator effects in the factorial design are shown in Figure 3.2.

Instructional Approach MCI MOI MCI MOI MCI MOI

High FD Male

Low FI Female

High - High-Ability FD - Field-Dependent Low - Low-Ability FI - Field-Independent

Figure 3.2 Experimental and Control Group Designs by Low-Ability and High-Ability, Field-Dependent and Field-Independent and Male and Female

According to Gay and Airasian (2003), the presence of a pretest-posttest

treatment group design is the most effective design to control threats to internal

reliability. The difference between the posttest and the pretest scores was therefore

useful for this study. In the second part of this study, the motivational effects of the

two treatment groups, the MCI and MOI of learners, were studied.

3.2 Variables

There were three types of variables in this study, the independent variables,

the dependent variables and the moderator variables. The independent variables

were the multimedia instruction employed to teach “Chemical Formulae and

Equations”. The two instruction methods employed were the MCI and MOI. The MCI

was developed based on the Constructivist Learning Environment whereas the MOI

was developed based on the Objectivist Learning Environment. The independent

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variables were measured using a nominal scale because the measurement was

without any order.

The dependent variables were the achievement score and the IMMS score.

The achievement score was obtained from the difference between the pretest and

the posttest scores. The IMMS score was obtained from the IMMS implemented

after the treatment. All the dependent variables were measured using the interval

scale since the measurement of the variable was not only with order but also

established equal distance on its scale (Wiersma, 2000). These variables were

summarised in Figure 3.3.

Independent Variables Dependent Variables

Multimedia Constructivist High Score Instruction (MCI) Achievement Score e-learning Moderator Variables Motivation Score

1. High-Ability/Low-Ability 2. Field-Dependent/Field-Independent

3. Gender (Male/Female)

Multimedia Objectivist Low Score Instruction (MOI)

Figure 3.3 The Relationship between Variables

The subjects of the study were divided into groups based on two levels of

science ability following their Cattell “Cullture Fair” Intelligence Test results. Students

scoring above the group mean in the Cattell “Culture Fair” Intelligence Test were

classified as the high-ability students and while those scoring below the group mean

in the Cattell “Culture Fair” Intelligence Test were classified as the low-ability

students. The field-dependent and field-independent students were determined

using the Group Embedded Figures Test (GEFT, translated by Toh, 1998). The

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grades in science were measured using the ordinal scale whereas field

dependent/independent and gender variables were measured using the nominal

scale.

3.3 The Research Sample and Sampling

This research aimed to investigate the effects of the MCI and MOI on Form

Four chemistry students at the secondary school level. The courseware was

developed for the third chapter in the Form Four chemistry, “Chemical Formulae and

Equations”, taught at the beginning of the year. Therefore, not many schools could

be chosen for this study. The chosen schools, however, had to provide many of their

classes for this research in order to have enough subjects to be experimented.

Furthermore, only schools that possessed a computer laboratory could be selected.

A total of 190 Form Four students participated in the research. 98 students

were in the experimental group and 92 students were in control group. However, 18

students from the MCI group and 3 students from the MOI group were later omitted

because of attrition. Some of the students had left school while the others were

absent during the sessions. Therefore, the subjects comprised 169 Form Four

science stream students from two suburban secondary schools in Butterworth,

Penang. Only two schools were selected because of time constraint. Since, the

topic “Chemical Formulae and Equations” will only be taught around March to April

every year, this study could only be conducted in two schools. Both random

sampling and random assignment were impossible in this study due to time

constraints.

There were five classes in School A and two classes in School B. The

students from both the schools were divided into the experimental group and the

control group. The students from the experimental group were administered with the

MCI whereas the students from the control group were administered with the MOI.

The students in School A were not streamed according to their ability levels (by the

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school administration). Therefore, two classes were randomly chosen for the

experimental group and three classes were randomly chosen for the control group.

The students from School B were streamed according to their ability levels (by the

school administration). Thus, the students from each class in School B were

randomly divided for the experimental and control group. There were two computer

laboratories in School B. The MCI and the MOI were conducted in different computer

laboratory to ensure that there is no interaction between the students from the

experimental group and control group. The MCI and the MOI were assigned for both

the experimental and control groups respectively and simultaneously in the computer

laboratory.

3.4 Instructional Materials

The instructional materials in this study were based on the third chapter in the

chemistry textbook designed by the Ministry of Education for all Form Four (level

four) students in secondary schools in Malaysia. The curriculum specifications

(Appendix F) show that this chapter is divided into the following seven sub-topics:

(a) Relative atomic mass and relative molecular mass

(b) Relationship between the number of moles and the number of particles

(c) Relationship between the number of moles of a substance and its mass

(d) Relationship between the number of moles of a gas and its volume

(e) Chemical formulae

(f) Chemical equations

The conceptual map in Appendix G shows the relationships among the

elements.

3.5 Research Instruments

This study employed four instruments. The instruments were the pretest and

posttest to measure achievement in chemistry, the Cattell “Culture Fair” Intelligence

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Test to divide the students into high-ability and low-ability categories, the Instructional

Material Motivational Scale (IMMS) to measure the students’ motivation and the

Group Embedded Figures Test (GEFT) to measure the students’ field-dependent and

field-independent cognitive styles.

3.5.1 Pretest and Posttest

The pretest (Appendix H) and posttest (Appendix I) were conducted to

measure the students’ achievement in the “Chemical Formulae and Equations”. Both

tests consisted of four objective questions and eight structural questions. The

duration of each test was 60 minutes. The questions in both tests were similar but

were arranged in a randomised order to avoid any possible interaction between

them. Some of the questions were modified but the contents were still the same.

However, the duration between the pretest and posttest sessions was four weeks,

which was long enough to minimise any possible interaction between them. Toh

(1998) documented that to minimise the “test-wise” effect, the pretest questions were

rearranged and the posttest was given four weeks later so that the students might not

remember the questions given in the pretest.

The tests were developed by the researcher and reviewed by two chemistry

teachers, each with more than ten years of teaching experience in chemistry. This

was to ensure the content and construct validity of the test. The items were

classified according to Lawson’s (1985) Taxonomy (Appendix D and Appendix E).

The classification was undertaken by the researcher and reviewed by two senior

teachers and a senior lecturer. The test items were classified as empirical inductive

or hypothetical deductive according to the thinking patterns required in the


3.5.2 The Cattell “Culture Fair” Intelligence Test

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The Cattell “Culture Fair” Intelligence Test (Appendix J) was developed by

Cattell and Cattell (1973) from the Institute for Personality and Ability Testing, United

States of America. This test measures the general intelligence of students. It

consists of 50 items organised into four different non-verbal tasks that are primarily

figural reasoning tasks of identifying series, classification, matrices and conditions.

Toh (1998) reported an internal consistency reliability coefficient of 0.85 for

Malaysian students based on the Malaysian national language (the Malay language)

version of the instrument.

3.5.3 The Instructional Material Motivational Scale (IMMS)

The Instructional Material Motivational Scale (IMMS) comprised 36 Likert-type

statements that are based on opinion and attitude. Thus, there were no correct or

wrong answers. This questionnaire (Appendix K) measured the subjects’ motivation

towards chemistry and multimedia instruction. The questionnaire was taken from

Keller (1987) and translated by Toh (1998).

Subjects responded to both positively and negatively worded statements by

marking their opinions on a scale of 1 (strongly disagree), 2 (disagree), 3 (neither

agree nor disagree), 4 (agree) and 5 (strongly agree). For the purpose of analysis,

scores of negatively stated items were reversed to maintain a unified direction on the

scale where high scores indicate a positive motivation and low scores indicate a

negative motivation.

3.5.4 The Group Embedded Figures Test (GEFT)

The Group Embedded Figures Test (GEFT) is a common measure of field

dependence-field independence (Tinajero & Paramo, 1997). The GEFT (Appendix L)

was developed by Witkin et al. (1971) and translated by Toh (1998). The GEFT is

designed to determine the extent students are able to overcome the effect of

background distractions while staying focused on the issue at hand. The GEFT

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score is based on the students’ success in locating 18 simple figures embedded in

complex figures (Witkin et al., 1971).

The GEFT was administered on the students at the beginning of the study

and the average mean on the GEFT was calculated. Individuals scoring below the

calculated mean were considered field dependent. These individuals tended to have

highly developed social skills, favoured a spectator approach to learning and needed

structured learning environments. Individuals scoring above the calculated mean

were considered field independent. Field-independent individuals were more

accomplished at logical reasoning, might have inferior social skills and could provide

their own structure to facilitate learning.

3.6 Research Procedures

The subjects of the study completed the first two chapters in Form Four

chemistry before the implementation of this study. Then, they were given the pretest

and GEFT before the treatment. Seven classes from two schools were divided into

two groups. The first group was the experimental group and the second group was

the control group. The subjects were brought to the computer laboratory, 20

students per session. Each subject was provided with one personal computer.

Then, they were taught how to “switch on” the computer and proceed with the

courseware in a session. The facilitators guided them at the beginning on how to use

the courseware, eliminating potential effects of novelty.

The treatment was carried out from the second lesson onwards. The

facilitators were provided with a protocol (Appendix M and N) on how to assist the

subjects. The timetable of the laboratory usage to conduct this study was the same

as the timetable of the chemistry classes. Whenever there was a chemistry class,

the subjects proceeded to the computer laboratory. The subjects were exposed to

chemistry lessons twice a week and each lesson was for 80 minutes. The chemistry

teachers of the classes acted as facilitators for the whole session. They assisted the

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subjects when they encountered problems with the hardware as well as the

courseware. At the beginning, the instructor also provided some guidance to the

chemistry teachers. The duration of the treatment was four weeks. Both the

experimental and control groups underwent the posttest and the IMMS after the

treatment.

3.7 Procedures to Ensure External and Internal Validity of Courseware

The quasi-experimental study was carried out for a period of four weeks to

minimise the threat of internal validity due to maturation or history of the subjects.

The questions in the pretest and posttest were similar but were arranged in a

randomised order to avoid any possible interaction between the two tests. Some of

the questions were modified but the content was still the same. However, the

“Chemical Formulae and Equations is a new topic and the implementation of the test

at the beginning of the session will not them any constructive experience. As the

interval between the pretest and the posttest was four weeks, it was long enough to

minimise the threat of a “test-wise” effect.

The students were randomly assigned into two treatment groups to minimise

the effect of differential selection. Both the schools chosen for this study were similar

in terms of racial composition, academic achievement, school culture and the socio-

economic status of the students.

Each class was randomly assigned either the MCI or the MOI in order to

avoid the interaction of students. The facilitator (teacher) of each class was provided

with detailed descriptions of procedures as indicated in the protocols (Appendix M

and Appendix N) to maintain the consistency in instructions and procedures.

The students were also provided with a session on the basic skills needed to

operate a computer and to use the courseware to minimise problems of effects from

technologically challenged students. Finally, to ensure the validity of the results, the

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researcher eliminated the students who had left school or were absent during the

sessions.

External validity is the degree to which the results are generalisable or

applicable, to groups and environments outside the experimental setting (Gay &

Airasian, 2003). Explicit description of the experimental setting was provided in the

form of protocol as shown in Appendix M and Appendix N to generalise this study to

other settings. This study could be conducted in another setting with the given

protocol.

The Hawthorn Effect could be reduced by minimising the special attention on

the participating students and teachers as this may cause a change in their

behaviour (Borg & Gall, 1989). The students should also not be informed about the

implementation or implications of the treatment in order to minimise the Hawthorn

Effect.

3.8 Data Analysis

This study utilised descriptive and inferential statistics to gather information.

Data collected from this study were analysed using the Statistical Packages for the

Social Sciences (SPSS version 12.0 for windows) software and the level of

significance was set at 0.05 (p<0.05). Descriptive statistics for gender were collected

together with the pretest and posttest.

The ability of the students was determined from the Cattell “Culture Fair”

Intelligence Test. Students scoring below the group mean in this test were

categorised as having low ability and students scoring above the group mean were

categorised as of high ability.

First, descriptive statistics were computed for the field-independent and field-

dependent students and male and female students using the MCI and MOI approach.

Then, the data yielded by the experimental design for the achievement score and

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motivation score were analysed using an analysis of variance (ANOVA) to determine

whether there was a significant difference between two means.

The two-way analysis of variance (ANOVA) was used to find out the

interaction between the independent variable (MCI and MOI) and the

moderator variables (ability levels, cognitive styles and gender) on the

subjects’ achievement scores.

The two-way analysis of variance (ANOVA) was used to find out the

interaction between the independent variables (MCI and MOI) and the

moderator variables (ability levels, cognitive styles and gender) on the

subjects’ IMMS scores.

3.9 Conclusion

Details of the research methodologies on subjects and sampling, research

design, the relationship between independent variables and dependent variables and

the type of instruments were discussed. This chapter also described the

development, validation protocol and the content validation for the pretest and

posttest. Along with these, data collection procedures of the study, eventual

analyses of the data in relation to the research questions and hypotheses were

described.

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CHAPTER 4

COURSEWARE DEVELOPMENT

4.0 Introduction

This chapter describes the development of the courseware for this study. It

involves the model used as a guidance for courseware development, collection for

teaching materials and evaluation of the effectiveness of the courseware. At the end

of this chapter, the results from the pilot study to establish the internal reliability of the

MCI and MOI is documented.

4.1 Courseware Development Model for Multimedia Instruction

The approach to design the multimedia courseware is based on the

instructional systems design by Alessi and Trollip (2001), the nine conditions of

learning by Gagné (1985) and the Constructivist Learning Environment proposed by

Jonassen (1999). All the three models were appropriate for the development of this

courseware.

e-Learning includes instruction delivered via CD-ROM, intranets and the

Internet. Approximately 40% of computer-delivered training uses CD-ROM, while

22% uses the Internet and 30% uses intranets (Galvin, 2001). According to Clark

and Mayer (2003), limitations in bandwidth may limit the use of memory-intensive

media (such as audio) elements for Internet delivery. In contrast, CD-ROM provides

considerably more memory than the Internet but will be more difficult to update and

disseminate to users. This study used video, media, sound and animation that

involved a great deal of bandwidth. Thus, the CD-ROM is used since it provided

sufficient memory to deliver instruction for a topic in chemistry.

4.1.1 Models of Instructional Systems Design

The model for the instructional systems design was based on the model

developed by Alessi and Trollip (2001) as shown in Figure 4.1. This model is flexible

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and one can mould it according to individual needs and styles of work. There are

several important features. First, it is standards-based. Both the client and the

developer can come to a consensus on the standards of the final product and

throughout the project, everyone will know what they are striving for. As for the MCI

and MOI developed for this study, the standards of the final product and throughout

the courseware were based on the SPM syllabus determined by the curriculum

department in the Ministry of Education of Malaysia.

STANDARDS

PLANNING Define the scope

Identify learner characteristics Establish the constraints

Cost the project Produce a planning document

Produce a style manual Determine and collect resources

Conduct initial brainstorming Define the look and feel

Obtain client sign-off

DESIGN Develop initial content ideas

Conduct task and concept analysis Do a preliminary programme description

Prepare a prototype Create flow charts and story boards

Prepare scripts Obtain client sign-off

DEVELOPMENT Prepare the text

Write programme code Create the graphics

Produce audio and video Assemble the pieces

Prepare support materials Do an alpha test Make revisions Obtain sign-off

Validate

Figure 4.1 The Model for the Design and Development of the MCI and the MOI

by Alessi and Trollip (2001, p. 410)

O N G O I N G E V A L U A T I O N

P R O J E C T M A N A G E M E N T

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Second, it is an empirical approach. Development is based on the iterative

cycle of drafting, evaluating and revising until the product works. This model

incorporates ongoing evaluation throughout the design and development phases,

preventing costly surprises from surfacing near the end of the project.

The third feature is that a project must be well managed from the beginning to

the end. It is common for multimedia projects to get off track and to end up

consuming more time to produce than planned and costing more than budgeted.

With good project management such slippage can be contained while still

maintaining desired standards.

The fourth important feature is that this model is driven by principles of

cognitive psychology. These principles include perception and attention, encoding,

memory, comprehension, active learning and individual differences. Some

constructivist principles are also central such as anchored instruction, collaborative

learning and reflective learning.

The fifth feature is the progression from discussion to ideas to

implementation. With the profusion of “integrated” tools such as Authorware and

ToolBook, it is tempting to start developing tools soon. Designers are encouraged to

discuss and plan with others before drafting ideas and then implementing the plans.

The sixth important feature is an emphasis, especially early in the

development process, on creativity. Creativity is necessary in a new field to

ascertain the capabilities of electronic technology to be fully realised. This will also

encourage people to use the instruction and be more engaging.

The final feature is promotion of a team-oriented approach. Collaboration will

involve more skills and knowledge in the development effort. The team should

acquire expertise in instructional design, programming, graphic arts and the subject

matter. Collaboration also increases the expected standard of acceptable quality. A

team usually possesses more creative ideas than an individual and is more

demanding of high standards.

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On the whole, based on these criteria, Alessi and Trollip (2001) created a

model for developing interactive materials that has three attributes that are always

present and three phases, each comprising a variety of issues to be addressed and

actions to be taken. The three attributes are standards, ongoing evaluation and

project management. The three phases are planning, design and development. The

model is illustrated in Figure 4.1.

The researcher consulted two senior lecturers during the process of planning,

designing and developing the courseware to make sure that it would be well

managed, creative and team oriented. They also provided the expertise in

instructional design, programming, graphic arts and the subject matter. The

integrated tools used to design the courseware were Authorware 6.0 (Macromedia

Inc., 2001), Flash MX 6.0 (Macromedia Inc. 2002) and Adobe Photoshop 7.0 (Adobe

Systems Inc., 2000). Authorware is chosen because it is a robust authoring tool

whereas Flash MX consists of vector graphics that is very small and can be used for

attention gutter. Similarly, Photoshop is very popular and easy to use, very

compatible and can be imported into Authorware easily.

4.1.2 Gagné’s Nine Events of Instruction

Gagné’s book, The Conditions of Learning, first published in 1965, identified

the mental conditions for learning. These were based on the information-processing

model of the mental events that occur when adults are presented with various stimuli.

Gagne created a nine-step process called the events of instruction, which correlate

to, and address, the conditions of learning (Table 4.1).

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Table 4.1 The Conditions of Learning (Gagne, 1985)

4.1.2.1 Gaining Attention

A lesson should begin with the intention to capture the attention of the

students. A multimedia programme that begins with an animated title screen

sequence accompanied by sound effects or music startles the senses with auditory

or visual stimuli. An even better way to capture students’ attention is to begin each

lesson with a thought-provoking question or interesting fact. Curiosity motivates

students to learn.

Instructional event

Internal Mental Process

1. Gains attention

Stimuli activate receptors

2. Informs learners of objectives

Creates level of expectation for learning

3. Stimulates recall of prior learning

Retrieval and activation of the short-term memory

4. Presents the content

Selective perception of content

5. Provides “learning guidance”

Semantic encoding for storage in the long-term Memory

6. Elicits performance (practice)

Responds to questions to enhance encoding and verification

7. Provides feedback

Reinforcement and assessment of correct performance

8. Assesses performance

Retrieval and reinforcement of content as final evaluation

9. Enhances retention and transfer of learning.

Retrieval and generalisation of learned skills to new situation

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Both the MCI and MOI are designed with an animated title screen sequence

and sound effect to capture student’s attention. The screen is shown in Figure 4.2.

Figure 4.2 The Animated Title Screen with Sound Effects

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4.1.2.2 Informing Learners of Objectives

Early in each lesson in the MOI, students should be provided a list of learning

objectives. This initiates the internal process of expectancy and helps motivate the

learner to complete the lesson. The objectives should form the basis for assessment

and possible certification as well. Typically, learning objectives are presented in the

form of “Upon completing this lesson you will be able to…”.

The MOI designed for this study present the objectives at the beginning of

each tutorial in the narration form. This is in line with the Clark and Mayer (2003)

model that suggested certain information should be in narration form only to avoid

overloading of the visual channel.

4.1.2.3 Stimulating Recall of Prior Learning

Associating new information with prior knowledge can facilitate the learning

process. It is easier for learners to encode and store information in the long-term

memory when there are links to personal experience and knowledge. A simple way

to stimulate recall is to ask questions about previous experiences and understanding

previous concepts or a body of content.

Figure 4.3 Simulating Recall of Prior Learning

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The MCI designed in this study will stimulate recall by guiding students to

support tools such as information resources and cognitive tools that are available in

the courseware (Figure 4.3).

4.1.2.4 Presentation of Content

This stage in the instruction is where a new content is actually presented to

the learner. Content should be chunked and organised meaningfully, explained and

then demonstrated. To appeal to different learning modalities, a variety of media

should be used if possible, including text, graphics, audio, narration and video. The

MCI and MOI are always designed with different learning modalities. An example is

shown in Figure 4.4.

Figure 4.4 Video Presentation - An Example of a Different Learning Modality

4.1.2.5 Providing Learning Guidance

Additional guidance provided along with the presentation of new content will

help learners encode information for long-term storage. Guidance strategies include

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the use of examples, non-examples, case studies, graphical representations,

mnemonics and analogies.

The MCI presents examples in the form of related cases (Figure 4.5) whereas

the MOI presents examples in the form of tutorial as exercises.

Figure 4.5 Case Studies as Guidance Strategies in MCI

4.1.2.6 Eliciting Performance (Practice)

At this stage, the learner is required to practice new skills or behaviour.

Eliciting performance provides an opportunity for learners to confirm their correct

understanding and the repetition further increases the likelihood of retention.

The repetition mentioned in this section is designed in the form of practice for

both MCI and MOI. The practice in MCI is designed in such a way that it is

embedded within the instruction. There are problem statements with questions that

induce practice among the students with related cases as examples that produce

more indirect practice and self-assessment as shown in Figure 4.6. The practice in

MOI is in the form of sample exercises as shown in Figure 4.7.

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Figure 4.6 Practice in the MCI as Problem Statement Questions and Related Cases

Figure 4.7 Practice in the MOI as Sample Exercises

4.1.2.7 Providing Feedback

As learners practice new behaviour, it is important to provide specific and

immediate feedback of their performance. Unlike the questions in a posttest,

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exercises within tutorials should be used for comprehension and encoding purposes,

not for formal scoring. This additional guidance and answers provided are called

formative feedback. The examples of feedback in MCI and MOI are illustrated in

Figure 4.8 and Figure 4.9 respectively.

Figure 4.8 Feedback in the MCI

Figure 4.9 Feedback in the MOI

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4.1.2.8 Assessing Performance

Upon completion of the instructional modules, students should be given the

opportunity to take (or be required to take) a posttest or final assessment. This

assessment should be completed without additional coaching, feedback or hints.

Mastery of material, or certification, is typically granted after achieving a certain score

or correct percent. A commonly accepted level of mastery is 80% to 90% correct.

Both MCI and MOI are designed with this summative exercise and

achievement score as illustrated in Figure 4.10 and Figure 4.11 respectively. MCI

uses self-assessment as their final assessment whereas MOI uses evaluation as

their final assessment.

Figure 4.10 Assessing Performance in the MCI

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Figure 4.11 Evaluation in the MOI

4.1.2.9 Enhancing Retention and Transfer of Learning

Determining whether or not the skills learned from a training programme are

ever re-applied in learning often remains a mystery. Effective designers have

“performance focus”, incorporating design and media that facilitate retention and

transfer of learning. The repetition of learned concepts is a tried and true means of

aiding retention.

As for the MCI, this transfer of knowledge is designed at the beginning as a

problem statement. The students are expected to go through the problem and find

ways to solve the problem. Some guidance is provided along the problem-solving

process. This is illustrated in Figure 4.12.

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Figure 4.12 Problem Statement with Guidance

4.2 The Development of the Courseware

Two types of courseware were developed for this study. The MCI

courseware was developed for the Constructivist Learning Environment and the MOI

courseware for the Objectivist Learning Environment. The authoring system

Authorware 6.0 (Macromedia Inc., 2001) is an icon-oriented system (Alessi & Trollip,

1991) used to develop the courseware. The Adobe Photoshop 7.0 (Adobe Systems

Inc., 2000) and Flash MX 6.0 (Macromedia Inc. 2002) were used together as

supporting software.

4.2.1 The MCI Courseware

The MCI courseware was designed based on the Constructivist Learning

Environment by Jonassen (1999), which was well elaborated in Chapter Two. It was

also designed according to the e-learning structure suggested by Clark and Mayer

(2003).

The main page (Figure 4.13) displayed the main menu with buttons as

follows:

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(a) Problem statement

(b) Glossary

(c) Media

(d) Conceptual map

(e) Exercises

(f) End

Figure 4.13 Main Menu Buttons

The “help menu” (Figure 4.14) has a top menu with five support tools such as:

(a) Related cases

(b) Information resources

(c) Cognitive tools

(d) Conversation and collaboration tools

(e) Social and contextual support

This top menu was placed in “help” screens to allow the user to have access through

these tools whenever there is a need.

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Figure 4.14 Five Support Tools and Music Button

There are buttons for “music on”, “music off” and “main menu” on all the

screens. This would enable the user to switch the music on or off or go back to the

“main menu” whenever he/she decided to do so. Also, there will be an indication on

each screen to notify the users which screen they are working on.

The problem statement menu (Figure 4.15) displayed only the main menu

button, “music on” button and “music off” button. This menu allowed navigations to

previous/next sub-problems and also to previous/next problem statements. On this

screen, the user is expected to try the problems and key in the answers in the given

spaces.

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Figure 4.15 The Problem Statement Menu

There is a pop-up of feedback for the answer the user provided. The positive

feedback would be “Good, well done!” and another button “next” (Figure 4.16) would

pop-up for the user to go on. The negative feedback was “Wrong, try again!” and the

same screen would reappear to give the user another chance to try (Figure 4.17).

The users could try to solve the problems as many times as they desired. There was

a “help” button to assist the user to solve the problems. From this menu, the user

would be able to go back to the problem statement screen. If with the “help” button,

the user still faced difficulty, he/she could use the solution button to look into the

solution of the problem. From the solution screen, the user could also go back to the

problem statement screen.

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Figure 4.16 Positive Feedback and “Next” Button

Figure 4.17 Negative Feedback

If the user still found it difficult to solve the problem, he/she could try the related

cases from the top menu. Some guidelines were available on where to go for more

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help in the “help” screen. The related menu, as shown in Figure 4.18, consisted of

the sub-titles in the formulae and chemical equation as follows:

(a) Relative atomic mass

(b) Relative molecular mass

(c) Mole number and particle number

(d) Mole number and mass

(e) Mole number and volume

(f) Chemical formulae

(g) Chemical equation

Figure 4.18 Related Cases

Each of the above sub-titles displayed a few related cases as shown in Figure

4.19. The user could choose any sub-title especially the ones related to the problem.

There was a solution button to help the user to solve the cases. The user could also

use the information resources tool from the top menu to look into additional

information, definition, historical background, formulae and media. The information

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resources would help the user to better understand related cases. Cognitive tools

from the top menu provided some additional formulas that the user needed to know.

The conversation and collaboration tool in the top menu would encourage the user to

use the networking system to seek help from other users in case he/she encountered

any problem. The user could also surf the net by using the conversation and

collaboration tool in the top menu.

Figure 4.19 Related Cases for Sub-Titles

The main menu provided buttons such as the glossary, media, conceptual

map, exercise and end. The glossary menu provided the synonyms in Bahasa

Melayu (the Malaysian national language) whereas the media displayed the

animation for the relative atomic mass, relative molecular mass, mole number/mass

and mole number/particle number. There were exercises for the user to evaluate

his/her understanding on each of the sub-topics in the “Chemical Formulae and

Equations”. These exercises were the formative evaluation to test the students’

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understanding of the concepts. However, these exercises do not appear in the

posttest. The user could use the end button to exit from the programme.

4.2.2 The MOI Courseware

The MOI courseware was developed based on the Objectivist Learning

Environment elaborated in Chapter two. The main page displayed the main menu

(Figure 4.20) with buttons as follows:

(a) Conceptual map

(b) Tutorial

(c) Media

(d) Evaluation

(e) Glossary

(f) Exit

Figure 4.20 The Main Menu for the MOI Courseware

There were no problem statements and the five support tools as in the MCI

courseware. The approach was more of a linear method. The user was expected to

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follow the order in the main menu. The conceptual map provided the user the

concepts that were involved in this chapter. The user could go through the tutorial

and the tutorial menu consisting of the sub-titles of the chapter. Each sub-title menu

provided details on definition, information, historical background, formulae, media

and examples of cases in sequence. The user would be able to try the examples

and feedback will appear as in the MCI courseware.

In the main menu, the user could also gain access to the media, glossary and

exercise. He/she could try the exercises after going through the tutorial.

The major difference between the MCI courseware and MOI courseware was

the problem statement. The MCI courseware users would try the problems first

before exploring the programme. Then they might resort to look into cases,

information, media or others before solving the problem. There were also five

support tools in the MCI courseware not present in the MOI courseware.

4.2.3 Differences between the MCI and MOI

MCI is a courseware designed and developed based on Jonassen’s

Constructivist Learning Environment (1999) model, Clark and Mayer’s (2003)

multimedia learning and Allessi and Trollip’s (1991) instructional systems design and

development for multimedia. The discerning attributes are:

(a) The model consists of a problem as the central focus of the environment.

(b) Students are not guided on how to go about solving the problem as they are

expected to explore the courseware on their own to solve the given problem.

(c) The problem statement is loosely defined.

(d) A variety of tools are provided for the students to construct knowledge such

as:

(i) Related cases

(ii) Information resources

(iii) Cognitive tools

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(iv) Conversation collaboration tools

(v) Social contextual support.

(e) The learning activities that provide instructional support in this courseware

and enable students to construct knowledge are modelling, coaching and

scaffolding.

Multimedia Objectivist Instruction (MOI) is a courseware designed and

developed based on the tutorial approach, Clark and Mayer’s (2003) multimedia

learning, Allessi and Trollip’s (1991) instructional systems design and development

for multimedia and Gagne’s (1985) nine events of instruction. The discerning

attributes are:

(a) The courseware is designed in a highly linear manner. The students are

given tutorials with definition, information, historical background, formulae,

media and examples of cases in sequence according to the sub-topics.

(b) There is also summative evaluation at the end of each lesson.

(c) The students are guided and forced to follow the flow given in the courseware

according to the sub-topics.

(d) The objectives are presented clearly in a behavioural manner.

The design and development process described the activities that were

carried out in the exact sequence. The learning environment was iterative revised

based on the feedback provided by experts and potential learners.

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The differences between MCI and MOI are tabulated briefly in the Table 4.2.

Table 4.2 Differences between MCI and MOI

MCI MOI

Prior preparation on designing

The educator seeks to provide learners with their own means of constructing their own interpretation of a problem. The instructional strategy is to provide tools for inquiring into the problem and various means for collecting information in order to understand or construct solutions to the problem.

The educator identifies the knowledge that needs to be transferred into the minds of the learners.

The objectives Loosely-defined. The content is not prespecified.

Clearly defined. All learners are expected to achieve the objectives in the same manner.

Method Problem based. Tutorial based.

Approach Non-linear and branched. Linear.

Attribute Knowledge are constructed by learners and do not exist outside their mind. Learners construct their own knowledge by actively participating in the learning process.

Pouring information into the learner. Learners will be told about the contents and are expected to replicate the contents and structure in their thinking.

Instruction design Based on Jonassen’s Constructivist Learning Environment that is interactive, collaborative, student-centred, active, based on authentic content and allow intentional learning.

Based on objectivist philosophy as the input-process-output model.

Evaluation The evaluation is not a separate activity and assessment arises naturally from the situation in which the instruction is embedded.

Entails using an objective evaluation method to determine whether the objectives have been met and to what degree.

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4.3 Evaluation of the Courseware

The final step of the development process was the evaluation. In the

formative evaluation of the courseware, two senior chemistry teachers and a senior

chemistry lecturer reviewed its content. Similarly, two senior lecturers in the field of

multimedia instructional design evaluated the screen design of the courseware. The

courseware was revised in terms of the contents and the instructional design based

on the recommendations from the experts.

Then, the courseware was pilot tested (Pilot Test 1) on one-to-one basis on

five students from a school in the year 2005. Only five students were chosen to

enable the researcher to focus on the problems faced by these students as they go

along the courseware. The students recorded some navigational problems that they

faced while proceeding through the courseware. The researcher also observed the

students while they were working through the courseware. At the end of the session,

the five students were interviewed to obtain further feedback on the courseware. The

students asked the researcher some questions regarding outline of the courseware,

suggested some changes to improve it and expressed their reactions. The students

suggested that more linkages should be included to enable them to go back to the

question worksheet. The researcher also discovered that the students were not sure

on how to go about the courseware. Based on the above pilot testing, minor

changes such as a protocol and added navigational buttons were made to improve

the courseware.

This was followed by the small-group evaluation in the form of a pilot study

(Pilot Test 2). The Pilot Test 2 was conducted on 20 students in a different school in

the same year, 2005. Only 20 students were chosen because there were only 20

computers in the computer laboratory. These students were given a pretest before

the execution of the courseware. The students studied “Chemical Formula and

Equations” for about four weeks. After the treatment they were given the posttest.

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They proceeded with the courseware without any problem, revealing their positive

attitude.

4.4 The Pilot Test

Prior to the actual study, Pilot Test 2 (2005), Pilot Test 3 (2005) and Pilot Test

4(2006) were conducted on 20 Form Four students from a suburban school in

Butterworth in the year 2006. The duration of the pilot test was four weeks. Its aim

was to ensure that the students were able to use the courseware and posttest

without any difficulties . It was also intended to find out whether the students had any

difficulty handling the computer. The facilitator provided information on how to get

started and use the courseware. The students were encouraged to ask questions

and get the facilitators’ help if any problems existed while handling the courseware.

Any ambiguity detected in the courseware was rectified.

The pilot test was also carried out to serve as a useful trial run of the

courseware and to provide the researcher with information on any unexpected

problems that might arise from the usage of the computer or the contents of the

courseware.

From the pilot testing, it was found that the students could go along with the

courseware without any problems. The students also consulted the facilitator when

they encountered problems regarding the subject matter or the courseware. At the

beginning, it was quite difficult for them to even handle the computer but with

guidance, they managed to proceed with the courseware without much assistance

from the facilitator.

The pretest and posttest consisted of 50 items respectively. This was in line

with the actual examination in chemistry. In the Pilot Study 2, many could not answer

all the questions and some just filled up the objective paper without even trying the

questions. The reliability of the test was estimated by using the Cronbach procedure

and the Cronbach Alpha coefficient was determined at 0.63. This was because the

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chapter involved great amount of calculations and it consumed more time than other

chapters that did not require much calculation. Therefore, the number of items in the

pretest and posttest were reduced to 30 after the Pilot Study 2. The items that were

deleted consist of the similar contents being tested. After the elimination of 20 items,

Pilot Test 3 was conducted on another 20 students (a different class) from the same

school (2005). The reliability of the test improved with a Cronbach Alpha coefficient

of 0.74.

However, after the presentation of the research proposal, the panel members

of the committee suggested that higher order tasks would be more appropriate in a

Constructivist Learning Environment. Thus, the pretest and posttest were adapted

and modified accordingly. The final pretest and posttest consisted of 12 questions

each, eight structural-based items and four objective-based items as shown in

Appendix H and Appendix I. These 12 questions were formulated based on all the

contents in the chapter. Two chemistry teachers reviewed the new pretest and

posttest, each with more than ten years of teaching experience. This was conducted

to ensure the content and construct validity of the test.

Pilot Test 4 was conducted to determine the reliability of the modified pretest

and posttest. The new sample also consisted of 20 Form Four students (2006) from

a suburban school in Butterworth. The Cronbach Alpha coefficient was determined

at 0.76. Therefore, the tests were deemed suitable to be used for the purpose of this

study.

4.5 Conclusion

This chapter describes the development of the courseware from determining

the needs and goals to the evaluation and revision of the final product. However,

evaluation and revision were carried out throughout the development. The chapter

also described the pilot study conducted to determine the validity and reliability of the

pretest, posttest and the courseware.

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CHAPTER 5

RESULTS

5.0 Introduction

This chapter presents the results of the study from the analyses of data

collected. This study investigated the effects of the MCI courseware compared to the

MOI courseware in solving learning difficulties in “Chemical Formulae and Equations”

among Form Four students. The independent variables were MCI (Multimedia

Constructivist Instruction) and MOI (Multimedia Objectivist Instruction). The

dependent variables were the achievement score (the posttest score minus the

pretest score) and the students’ perceived motivation towards the instructional

materials as measured by the IMMS score. The moderator variables were the ability

levels (low-ability and high-ability as measured by the Cattell “Culture Fair”

Intelligence Test), cognitive styles (field-dependent and field-independent) and

gender (male and female).

The data analyses were carried out using the descriptive and inferential

statistics to gather information. The descriptive statistics for cognitive styles and the

ability levels of the students were determined using the GEFT test and Cattell

“Culture Fair” Intelligence Test respectively.

The inferential statistics utilised in this study were the analysis of variance

(ANOVA). The two-way analysis of covariance (ANCOVA) was not conducted

because the subjects were not significantly different in terms of the school type and

multimedia approach in their pretest scores. ANCOVA is conducted only if there are

initial group differences on variables used in causal-comparative and experimental

research studies (Gay & Airasian, 2003). The data were compiled and analysed

using the Statistical Packages for the Social Sciences (SPSS) for Windows computer

software version 12.0.

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5.1 Descriptive Statistics of the Variables

This section contains the descriptive statistics of the variables involved in the

study. These variables were:

(a) courseware – MCI and MOI

(b) ability levels – low-ability and high-ability

(c) cognitive styles – field-dependent and field-independent

(d) gender – male and female

In order to categorise the low-ability and high-ability students as well as field-

dependent and field-independent students, the means of the variables were

determined as in Table 5.1. The students with a mean lower than 32.90 were

classified as low-ability students and students with a mean higher than 32.90 were

classified as high-ability students.

The mean GEFT score for male is greater than for female by one point. For

analysis purposes, both genders are considered. Hence, to offset the difference in

means (male mean = 9.90, female mean = 8.23), the female students are given one

point extra in the FD/FI scale (Fong, 1996). The means for female is now almost the

same as for the male with the additional one point. This is done by involving ‘if’

command and reassigning one point gain to the female students in the SPSS

program. Therefore, the students with a mean lower than 9.56 were categorised as

field-dependent and the students with a mean higher than 9.56 were categorised as

field-independent.

Table 5.1 Mean Scores and Standard Deviations of Cognitive Styles and Ability Levels

Variable Minimum Maximum Mean Standard (N= 169) Deviation

Cognitive Style 0 18 9.56 4.30

Ability Level 20 42 32.90 3.93

Note: N denotes the number of students.

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A total of 190 Form Four students participated in the research. However, 21

students were later omitted because of attrition. Some of the students had left school

while others were absent during the sessions. Thus, only 169 were considered for

data collection. 80 (47.3%) students learned “Chemical Formulae and Equations”

using a Multimedia Constructivist Instruction (MCI) courseware while 89 (52.7%)

students learned “Chemical Formulae and Equations” utilising a Multimedia

Objectivist Instruction (MOI) courseware.

Table 5.2

Descriptive Statistics for Independent and Moderator Variables Variable Frequency Percent (N= 169) (%) Ability Level Low-Ability 71 42.0 High-Ability 98 58.0 Cognitive Style Field-Dependent 86 50.9 Field-Independent 83 49.1 Gender Male 82 48.5 Female 87 51.5 Multimedia MCI 80 47.3 Approach MOI 89 52.7 Notes: N denotes the number of students.

The field-dependent students scored lower than 9.56 whereas the field-independent students scored higher than 9.56. The low-ability students scored lower than 32.90 whereas the high-ability students scored higher than 32.90.

As shown in Table 5.2, the subjects of the study comprised 71 (42.0%) low-

ability students and 98 (58.0%) high-ability students. The sample consisted of more

high-ability students than low-ability students because these students were chosen

for the science stream according to their academic achievement in the Penilaian

Menengah Rendah (PMR) examination.

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Table 5.2 also shows that the subjects of the study comprised 86 (50.9%)

field-dependent students and 83 (49.1%) field-independent students. There were 82

(48.5%) male students and 87 (51.5%) female students in this study.

As shown in Table 5.3, 38 low-ability and 42 high-ability students utilised the

MCI courseware while 33 low-ability and 56 high-ability students used the MOI

courseware in this study. 41 field-dependent and 39 field-independent students

followed the MCI courseware whereas 45 field-dependent and 44 field-independent

students used the MOI courseware. The MCI courseware was also used by 47 male

and 33 female students whereas the MOI courseware was used by 35 male and 54

female students.

Table 5.3

Descriptive Statistics for the Multimedia Approaches Moderator Variable Independent Variable MCI MOI N N Ability Level Low-Ability 38 33 High-Ability 42 56 Cognitive Style Field-Dependent 41 45 Field-Independent 39 44 Gender Male 47 35 Female 33 54 Note: N denotes the number of students.

5.2 Inferential Statistics

Inferential statistics were conducted for the pretest score, achievement score

(the posttest score minus the pretest score) and IMMS score. The purpose for the

pretest score analysis was to test whether the subjects of the study across two

schools were equivalent in their achievement prior to the treatment session. The

pretest score analysis also attempted to show that the students who learned using

MCI and MOI were also equivalent.

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5.2.1 Pretest Score Analysis for Independent and Moderator Variables

Analysis of variance was conducted on the pretest achievement score to

ascertain the equivalence of the subjects for both the schools and also for the two

treatment groups, in terms of prior knowledge. The pretest score was the dependent

variable, whereas the mode of treatment (MCI and MOI) and the type of schools

were the independent variables.

Table 5.4 ANOVA of Achievement Mean Scores for School Type and Multimedia

Approaches

Source Sum Square df Mean F-value p-value Square School Type 6.787 1 6.787 0.000 0.983 Multimedia Approach (MCI/MOI) 8.526 1 8.526 0.552 0.459 Note: * denotes significance at p<0.05 level.

Table 5.4 shows that there was no significant difference in ability (as

measured by the Cattell “Culture Fair” Intelligence Test) between the students from

School A and the students from School B (F = 0.000, p = 0.983). There was also no

significant difference in ability between the students using the MCI approach and the

students using the MOI approach (F = 0.552, p = 0.459).

Thus, it can be concluded that the subjects from the two schools were

homogenous in terms of ability and were distributed randomly between the two

multimedia approaches.

5.2.2 Achievement Score Analyses for the Independent and Moderator Variables The first hypothesis of this study was:

H11: The students who are using the Multimedia Constructivist Instruction (MCI)

approach will show a significant difference compared to the students who are

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using the (MOI) Multimedia Objectivist Instruction approach in their

achievement score.

As shown in Table 5.5, there was a significant difference in the achievement

mean score between the students using the MCI and MOI approaches (F = 16.991, p

= 0.000). The descriptive analysis in Table 5.6 reveals that the students using the

MCI approach performed significantly better than the students using the MOI

approach (MCI mean = 64.613, MOI mean = 51.730). Thus, the first hypothesis of

this study was accepted since the students who used the MCI approach showed a

significant difference in their achievement score compared to the students who used

the MOI approach.

Table 5.5 ANOVA for Multimedia Approaches and Moderator Variables

Source Sum Square df Mean F-value p-value Square Treatment (MCI/MOI) 4013.851 1 4013.851 16.991 0.000* Ability Level (LA/HA) 1048.824 1 1048.824 4.440 0.037* Cognitive Style (FD/FI) 776.205 1 776.205 3.286 0.072 Gender (Male/Female) 153.703 1 153.703 0.663 0.417 Note: * denotes significance at p<0.05 level. The second hypothesis was:



achievement score.

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Table 5.6 Mean Scores and Standard Deviations of Achievement Score for Independent

and Moderator Variables Variable N Mean Standard Deviation Approach MCI 80 64.613 17.517 MOI 89 51.730 15.217 Ability Level Low-ability 71 53.972 15.218 High-ability 98 60.622 18.605 Cognitive Style FD 86 54.930 16.846 FI 83 60.831 17.813 Gender Male 82 58.390 18.808 Female 87 57.299 16.320 Note: N denotes the number of students.

Table 5.5 also shows that there was a significant difference in the

achievement mean score between the low-ability students and high-ability students

(F = 4.440, p = 0.037). As presented in Table 5.6, the descriptive analysis displayed

that the achievement mean score for high-ability students was significantly higher

than the low-ability students (HA mean = 60.622, LA mean = 53.972). Therefore, the

second hypothesis of this study was also accepted since the high-ability students

showed a significant difference in their achievement score compared to the low-

ability students who used the multimedia instruction approach.

The third hypothesis was:


the field-dependent students who are using the multimedia instruction approach in

their achievement score (as measured by the posttest minus the pretest).

The data in Table 5.6 indicated that the field-independent students’

achievement mean score was higher than the field-dependent students’ achievement

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mean score (FD mean = 54.930, FI mean = 60.831). However, as displayed in Table

5.5, there was no significant difference in the achievement mean score between the

field-dependent students and field-independent students (F = 3.286, p =0.072). As a

result, the third hypothesis was rejected because the field-independent students did

not show a significant difference in their achievement score compared to the field-

dependent students who used the multimedia instruction approach.

The fourth hypothesis was:

H14: The male students will show for a significant difference compared to the

female students using the multimedia instruction approach in their achievement

score.

As shown in Table 5.6, the male students’ achievement mean score was

higher when compared to the female students’ achievement mean score (male mean

= 58.390, female mean = 57.299). However, Table 5.5 indicated that the

achievement mean score of the male students was not significantly higher than the

achievement mean score of the female students (F = 0.663, p = 0.417). Hence, the

fourth hypothesis was rejected since the male students did not show a significant

difference in their achievement score compared to the female students who used the

multimedia approach.

5.2.3 ANOVA of Achievement Mean Scores for the Moderator Variables on the Multimedia Approaches

There were three sub-hypotheses for each of the moderator variables. Two-

way ANOVA was conducted to assess whether the means on the achievement score

were significantly different among the three groups. The study compared the means

of group one with group two, group one with group three and group two with group

three.

Three assumptions are essential when using two-way ANOVA (Green,

Salkind & Akey, 1997). First, the dependent variable is normally distributed for each

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group as defined by the different levels of the factor. As shown in Table 5.7, there

were more than 15 low-ability students in the MCI approach (N = 38), high-ability

students in the MCI approach (N = 42), low-ability students in the MOI approach (N =

33), and high-ability students in the MOI approach (N = 56). Table 5.8 indicated that

there were also more than 15 field-dependent students using the MCI approach (N =

41), field-independent students using the MCI approach (N =39), field-dependent

students using the MOI approach (N = 45) and field-independent students using the

MOI approach (N = 44).

Table 5.7 Descriptive Statistics of Achievement Mean Scores of the Ability Levels on

Multimedia Approaches Approach Ability Level N Mean Standard

Deviation MCI Low-Ability 38 57.368 15.296 High-Ability 42 71.167 16.955 MOI Low-Ability 33 50.061 14.374 High-Ability 56 52.714 14.374 Note: N denotes the number of students.

Table 5.8 Descriptive Statistics of Achievement Mean Scores of the Cognitive Styles on

Multimedia Approaches Approach Cognitive Style N Mean Standard

Deviation MCI Field-Dependent 41 58.780 16.985 Field-Independent 39 70.744 16.093 MOI Field-Dependent 45 51.422 16.111 Field-Independent 44 52.045 14.425 Note: N denotes the number of students.

It can drawn from Table 5.9 that there were more than 15 male students using

the MCI approach (N = 47), female students using the MOI approach (N = 33), male

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students using the MOI approach (N = 35) and female students using the MOI

approach (N = 54). Since all the groups consisted of more than 15 students each, the

sample size was large enough to yield fairly accurate p-values. Therefore, the

assumption of the dependent variables being normally distributed for each group as

defined by the different levels of the factor was fulfilled.

Table 5.9 Descriptive Statistics of Achievement Mean Scores of Gender in Multimedia

Approaches Approach Gender N Mean Standard

Deviation MCI Male 47 65.340 18.139 Female 33 63.576 16.813 MOI Male 35 49.057 15.530 Female 54 53.463 14.899 Note: N denotes the number of students.

Second, the variances of the dependent variables were the same for all

populations. The Levene Test in Table 5.10 indicates that the variance for the ability

level variables was not significant (p = 0.814). Thus, the variance of dependent

variables was homogenous in terms of the ability levels.

Table 5.10

The Levene Test – Test of Homogeneity of Variances in Ability Level Variable Levene Statistics, F df1 df2 Sig. Ability Level 0.316 3 165 0.814 Cognitive Style 0.859 3 165 0.464 Gender 1.512 3 165 0.213 Note: sig. denotes significance at p<0.05 level.

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The Levene Test in Table 5.10 also shows that the variance for the cognitive

style variables was not significant (p = 0.464). Therefore, the variance of dependent

variables was homogenous in terms of the cognitive styles. Table 5.10 also reveals

that the variance for the gender variable was not significant (p = 0.213) using the

Levene Test. As a result, the variances of dependent variables were also

homogenous in terms of the gender.

Lastly, the cases representing random samples from the populations and the

scores on the test variable were independent of each other. The Form Four science

students were not streamed according to their ability levels. Three classes were

picked at random and these students were administered the MCI approach for the

topic “Chemical Formulae and Equations”. The remaining four classes were given

the MOI approach. Therefore, the sample for this study was randomly selected.

The three sub-hypotheses for the second hypothesis were as follows:

H12a: The high-ability students will show a significant difference compared to the

low-ability students who are using the MCI approach in their achievement

score.


significant difference compared to the high-ability students who are using the

MOI approach in their achievement score.


significant difference compared to the low-ability students who are using the

MOI approach in their achievement score.

Table 5.11 indicates that there was a significant difference between the high-

ability students and low-ability students using the MCI approach in their achievement

score (the mean difference = 13.798, p = 0.001). As shown in Table 5.7, the

achievement mean score for high-ability students using the MCI approach (mean =

71.167) was higher than the achievement mean score for low-ability students who

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were using the MCI approach (mean = 57.368) in their achievement score. Thus, the

first sub-hypothesis of the second hypothesis was accepted since the high-ability

students who used the MCI approach showed a significant difference in their

achievement score compared to the high-ability students who used the MCI

approach.

Table 5.11 ANOVA of Achievement Mean Scores of the Ability Levels on Multimedia

Approaches

I J Mean Std Sig. 95% Confidence (Approach/ (Approach/ Difference Error Level Ability Level) Ability Level) (I – J) Lower Upper Bound Bound MCI/HA MCI/LA 13.798 3.515 0.001* 4.411 23.185 MCI/HA MOI/HA 18.452 3.205 0.000* 9.894 27.011 MCI/LA MOI/LA 7.308 3.736 0.313 -2.669 7.284 Note: * denotes significance at p<0.05 level.

Table 5.7 also displays that the achievement mean score for the high-ability

students who used the MCI approach (mean = 71.167) was higher than the

achievement mean score for the high-ability students using the MOI approach (mean

= 52.714). As presented in Table 5.11, the high-ability students using the MCI

approach performed significantly higher than the high-ability students who were using

the MOI approach (the mean difference = 18.452, p = 0.000) and this supported the

second sub-hypothesis of the second hypothesis.

Table 5.7 shows that the achievement mean score for low-ability students

using the MCI approach (mean = 57.368) was higher than the achievement mean

score for low-ability students using the MOI approach (mean = 50.061). However, as

displayed in Table 5.11, the low-ability students using the MCI approach did not

perform significantly higher than the low-ability students using the MOI approach

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(mean difference = 7.308, p =0.313) and therefore, the third sub-hypothesis of the

second hypothesis was rejected.

The three sub-hypotheses for the third hypothesis were as follows:

H13a: The field-independent students will show a significant difference compared to

the field-dependent students who are using the MCI approach in their

achievement score.

H13b: The field-independent students who are using the MCI approach will show a

significant difference compared to the field-independent students who are


H13c: The field-dependent students who are using the MCI approach will show a

significant difference compared to the field-dependent students who are using


Table 5.12 ANOVA of Achievement Mean Scores of the Cognitive Styles on Multimedia

Approaches

I J Mean Std Sig. 95% Confidence (Approach/ (Cognitive Difference Error Level Cognitive Style)/ (I – J) Lower Upper Style) Approach Bound Bound MCI/FD FI/MCI -11.963 3.558 0.006* -21.464 -2.462 MCI/FI FI/MOI 18.698 3.498 0.000* 9.356 28.040 MCI/FD MOI/FD 7.358 3.434 0.202 - 1.813 16.529 Note: * denotes significance at p<0.05 level.

As presented in Table 5.8, the achievement mean score of field-independent

students (mean = 70.744) was higher than the achievement mean score of field-

dependent students (mean = 58.780) using the MCI approach. Table 5.12 shows that

the achievement mean score of the field-independent students was significantly

higher than the achievement mean score of the field-dependent students (mean

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difference = -11.963, p = 0.006). This supported the first sub-hypothesis of the third

hypothesis and the field-independent students therefore showed a significant

difference in their achievement score compared to the field-dependent students using

the MCI approach.

Table 5.8 also reveals that the achievement mean score for the field-

independent students using the MCI approach (mean = 70.744) was higher than the

achievement mean score for the field-independent students using the MOI approach

(mean = 52.045). As presented in Table 5.12, the field-independent students using

the MCI approach was significantly higher than the field-independent students using

the MOI approach (the mean difference = 18.698, p = 0.000) and thus, the second

sub-hypothesis of the third hypothesis was accepted.

Table 5.8 shows that the achievement mean score for field-dependent

students using the MCI approach (mean = 58.780) was higher than the achievement

mean score for field-dependent students using the MOI approach (mean = 51.422).

However, as illustrated in Table 5.12, the achievement score of the field-dependent

students using the MCI approach was not significantly higher than those of the field-

dependent students using the MOI approach (mean difference = 7.358, p = 0.202)

and as a result, the third sub-hypothesis of the third hypothesis was rejected.

The three sub-hypotheses of the fourth hypothesis were as follows:

H14a: The male students will show a significant difference compared to the female

students who are using the MCI approach in their achievement score.

H14b: The male students who are using the MCI approach will show a significant

difference compared to the male students who are using the MOI approach in

their achievement score.

H14c: The female students who are using the MCI approach will show a significant

difference compared to the female students who are using the MOI approach

in their achievement score.

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As shown in Table 5.9, the achievement mean score of male students (mean

= 65.340) was higher than the achievement mean score of female students (mean =

63.576) using the MCI approach. However, from Table 5.13, it is noted that the

achievement mean score of the male students was not significantly higher than the

achievement mean score of the female students (mean difference = 1.765, p =

1.000). Therefore, the first sub-hypothesis of the fourth hypothesis was rejected and

the male students did not show a significant difference in their achievement score

compared to the female students using the MCI approach.

As shown in Table 5.13, the achievement of male students using the MCI

approach were significantly different than the male students using the MOI approach

(the mean difference = 16.283, p = 0.000). It is noted from Table 5.9 that the

achievement mean score for the male students using the MCI approach (mean =

65.340) was higher than the achievement mean score for the male students using

the MOI approach (mean = 49.057). This finding indicated that the male students

using the MCI approach performed significantly higher in their achievement score

than the male students using the MOI approach and therefore, the second sub-

hypothesis of the fourth hypothesis was accepted.

Table 5.13 ANOVA of Achievement Mean Scores of Gender on Multimedia Approaches

I J Mean Std Sig. 95% Confidence (Approach/ (Gender/ Difference Error Level Gender Approach (I – J) Lower Upper Bound Bound MCI/Male Female/MCI 1.765 3.715 1.000 -8.156 11.685 MCI/Male MaleI/MOI 16.283 3.652 0.000* 6.531 26.036 MCI/Female MOI/Female 10.113 3.614 0.035* 0.461 19.764 Note: * denotes significance at p<0.05 level.

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Table 5.9 shows that the achievement mean score for the female students

using the MCI approach (mean = 63.576) was higher than the achievement mean

score for female students using the MOI approach (mean = 53.463). Table 5.13

reveals that the female students using the MCI approach performed significantly

higher in their achievement score than the female students using the MOI approach

(mean difference = 10.113, p = 0.035) and therefore, the third sub-hypothesis of the

fourth hypothesis was accepted.

5.2.4 IMMS Score Analysis for Independent and Moderator Variables

The fifth hypothesis of this study was:

H15: The students who are using the Multimedia Constructivist Instruction

approach (MCI) will show a significant difference compared to the students

who are using the Multimedia Objectivist Instruction approach (MOI) in their

IMMS score.

Table 5.14 ANOVA of IMMS Scores for Multimedia Approaches and Moderator Variables

Source Sum Square df Mean F-value p-value Square Approach (MCI/MOI) 647.901 1 647.901 6.545 0.011* Ability Level (LA/HA) 5085.212 1 5085.212 51.367 0.000* Cognitive Style (FD/FI) 626.174 1 626.174 6.325 0.013* Gender (Male/Female) 1984.605 1 1984.605 20.047 0.000* Note: * denotes significance at p<0.05 level.

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As presented in Table 5.14, there was a significant difference in the IMMS

mean score between the students using the MCI and MOI approaches (F = 6.545, p

= 0.011). The descriptive analysis in Table 5.15 displays that the students using the

MCI approach were significantly more motivated than the students using the MOI

approach (MCI mean = 126.631, MOI mean = 122.038). As a result, the fifth

hypothesis of this study was accepted since the students using the MCI approach

showed a significant difference in their IMMS score compared to the students using

the MOI approach.

The sixth hypothesis was:


low–ability students who are using the multimedia instruction approach in their IMMS

score.

Table 5.15 Mean Scores and Standard Deviations of Achievement Scores for

Independent and Moderator Variables Variable N Mean Std. Deviation Approach MCI 80 126.631 1.167 MOI 89 122.038 1.364 Ability Level Low-Ability 71 117.900 1.336 High-Ability 98 130.768 1.200 Cognitive Style FD 86 122.077 1.225 FI 83 126.592 1.313 Gender Male 82 128.353 1.361 Female 87 120.315 1.171 Note: N denotes the number of students.

Table 5.14 also indicates that there was a significant difference in the IMMS

score between the low-ability students and high-ability students (F = 51.367, p =

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0.000). As shown in Table 5.15, the descriptive analysis exhibited that the IMMS

mean score for high-ability students were significantly higher than the low-ability

students (LA mean =117.900, HA mean = 130.768). Thus, the sixth hypothesis of

this study was also accepted because the high-ability students showed a significant

difference in their IMMS score compared to the low-ability students using the

multimedia instruction approach.

The seventh hypothesis was:




As shown in Table 5.15, the field-independent students’ IMMS score was

higher than the field-dependent students’ IMMS score (FD mean = 122.077, FI mean

= 126.592). Table 5.14 reveals that the field-independent students were significantly

more motivated compared to the field-dependent students (F = 6.325, p = 0.013).

Therefore, the seventh hypothesis was also accepted since the field-independent

students showed a significant difference in the IMMS score as compared to the field-

dependent students using the multimedia instruction approach.

The eighth hypothesis was:


students who are using the multimedia instruction approach in their IMMS

score.

As drawn from Table 5.15, the male students’ IMMS score was higher than

the female students’ IMMS score (male mean = 128.353, female mean = 120.315).

Table 5.14 indicates that the male students were significantly more motivated than

the female students using the multimedia approach (F = 20.047, p = 0.000). Hence,

the eighth hypothesis was accepted as the male students exhibited a significant

difference in the IMMS score as compared to the female students using the

multimedia instruction approach.

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5.2.5 ANOVA of IMMS Mean Scores of the Moderator Variables on Multimedia Approaches

Similar to the achievement score analysis, there were also three sub-

hypothesis for each of the moderator variables designed in terms of the IMMS score

analysis. Two-way ANOVA was carried out to seek whether the means on the IMMS

score were significantly different among three groups. The means of group one and

group two, group one and group three and group two and group three were

compared in this section. As mentioned earlier, three conditions had to be fulfilled in

conducting two-way ANOVA. The first and third conditions were discussed in

Section 5.2.3. The subjects were large enough for each group to yield an accurate p-

value. The sample was randomly chosen since the students were not streamed

according to their ability levels.

The second condition was that variances of the dependent variables would be

the same for all populations. The Levene Test in Table 5.16 shows that the IMMS

score variance for ability levels was not significant (p = 0.067). Therefore, the

variance of the dependent variables was homogenous in terms of the ability levels.

Table 5.16

Levene Test – Test of Homogeneity of IMMS Score Variances for the Moderator Variables

Variable Levene Statistic, F df1 df2 sig. Ability Level 3.411 1 167 0.067 Cognitive Style 2.806 1 167 0.096 Gender 7.689 1 167 0.006* Note: * denotes significance at p<0.05 level.

The Levene Test in Table 5.16 also reveals that the variance of the IMMS

score for the cognitive styles was not significant (p = 0.096). As a result, the

variance of the IMMS score is homogenous in terms of the cognitive styles.

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Contrarily, the Levene Test for gender showed that the variance of the IMMS score is

significant (p = 0.006). Therefore, the variance of the IMMS score was not

homogenous in terms of gender. Therefore, Dunnett’s C procedure was used to

analyse the data since the variances were unequal (Gay & Airasian, 2003).

The sub-hypotheses for the sixth hypothesis were as follows:

H16a: The high-ability students will show a significant difference compared to the

low-ability students who are using the MCI approach in their IMMS score.


significant difference compared to the high-ability students who are using the

MOI approach in their IMMS score.


significant difference compared to the low-ability students who are using the


Table 5.17 indicates that there was a significant difference between the high-

ability students and the low-ability students using the MCI approach in their IMMS

score (the mean difference = 11.624, p = 0.000). As presented in Table 5.18, the

IMMS mean score for high-ability students using the MCI approach (mean =

133.571) was higher than the IMMS score for low-ability students using the MCI

approach (mean = 121.947). Thus, the first sub-hypothesis of the sixth hypothesis

was accepted since the high-ability students using the MCI approach showed a

significant difference compared to the high-ability students using the MCI approach in

their IMMS score.

Table 5.18 also displays that the IMMS mean score for the high-ability

students using the MCI approach (mean = 133.571) was higher than the IMMS mean

score for the high-ability students using the MOI approach (mean = 128.607).

However, as presented in Table 5.17, the high-ability students using the MCI

approach were not significantly more motivated than the high-ability students using

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the MOI approach (the mean difference = 4.964, p = 0.277) and thus, the second

sub-hypothesis of the sixth hypothesis was rejected.

Table 5.17

ANOVA of IMMS Mean Scores of the Ability Levels on Multimedia Approaches

I J Mean Std Sig. 95% Confidence (Approach/ (Ability Level/ Difference Error Level Ability Level) Approach) (I – J) Lower Upper Bound Bound MCI/HA LA/MCI 11.624 2.710 0.000* 4.388 18.860 MCI/HA HA/MOI 4.964 2.471 0.277 -1.633 11.562 MCI/LA MOI/LA 8.190 2.880 0.030* 0.499 15.881 Note: * denotes significance at p<0.05 level.

Table 5.18 Descriptive Statistics of IMMS Mean Scores of the Ability Levels on Multimedia

Approaches Approach Ability Level N Mean Standard

Deviation MCI Low-Ability 38 121.947 11.001 High-Ability 42 133.571 12.305 MOI Low-Ability 33 113.758 8.796 High-Ability 56 128.607 14.148 Note: N denotes the number of students.

Table 5.18 presents that the IMMS mean score for low-ability students using

the MCI approach (mean = 121.947) was higher than the IMMS mean score for low-

ability students using the MOI approach (mean = 113.758). As illustrated in Table

5.17, the low-ability students using the MCI approach were significantly more

motivated than the low-ability students using the MOI approach (mean difference =

8.190, p =0.030) and therefore, the third sub-hypothesis of the sixth hypothesis was

accepted.

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The three sub-hypotheses for the seventh hypothesis were:

H17a: The field-independent students will show a significant difference compared to

the field-dependent students who are using the MCI approach in their IMMS

score.

H17b: The field-independent students who are using the MCI approach will show a

significant difference compared to the field-independent students who are


H17c: The field-dependent students who are using the MCI approach will show a

significant difference compared to the field-dependent students who are using


From Table 5.19, it is noted that the IMMS mean score of the field-

independent students was significantly higher than the IMMS mean score of the field-

dependent students (mean difference = -8.558, p = 0.015). As shown in Table 5.20,

the IMMS mean score of field-independent students (mean = 132.436) was higher

than the IMMS mean score of field-dependent students (mean = 123.878) using the

MCI approach. This supported the first sub-hypothesis of the seventh hypothesis

and therefore, the field-independent students showed a significant difference in their

IMMS score compared to the field-dependent students using the MCI approach.

Table 5.19 ANOVA of IMMS Mean Scores of the Cognitive Styles on Multimedia

Approaches

I J Mean Std Sig. 95% Confidence (Approach/ (Cognitive Difference Error Level Cognitive Style)/ (I – J) Lower Upper Style) Approach Bound Bound MCI/FD FI/MCI -8.558 2.790 0.015* -16.009 -1.107 MCI/FI FI/MOI 2.300 2.743 1.000 -5.027 9.626

MCI/FD MOI/FD 7.656 2.693 0.030* 0.464 14.848 Note: * denotes significance at p<0.05 level.

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Table 5.20 also reveals that the IMMS mean score for the field-independent

students using the MCI approach (mean = 132.436) was higher than the IMMS mean

score for the field-independent students using the MOI approach (mean = 130.136).

As presented in Table 5.19, there was no significant difference in motivation between

the field-independent students using the MCI approach and the field-independent

students using the MOI approach (the mean difference = 2.300, p = 1.000), and thus,

the second sub-hypothesis of the seventh hypothesis was rejected.

Table 5.20 Descriptive Statistics of IMMS Mean Scores of the Cognitive Styles on

Multimedia Approaches Approach Cognitive Style N Mean Standard

Deviation MCI Field-Dependent 41 123.878 11.118 Field-Independent 39 132.436 13.547 MOI Field-Dependent 45 116.222 10.846 Field-Independent 44 130.136 14.127 Note: N denotes the number of students.

Table 5.20 shows that the IMMS mean score for field-dependent students

using the MCI approach (mean = 123.878) is higher than the IMMS mean score for

field-dependent students using the MOI approach (mean = 116.222). However, as

illustrated in Table 5.19, the field-dependent students using the MCI approach also

were significantly more motivated in their IMMS score than the field-dependent

students using the MOI approach (mean difference = 7.656, p = 0.030), and as a

result, the third sub-hypothesis of the seventh hypothesis was accepted.

The three sub-hypotheses for the eighth hypothesis are:

H18a: The male students will show a significant difference compared to the female

students who are using the MCI approach in their IMMS score.

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H18b: The male students who are using the MCI approach will show a significant

difference compared to the male students who are using the MOI approach in

their IMMS score.

H18c: The female students who are using the MCI approach will show a significant

difference compared to the female students who are using the MOI approach

in their IMMS score.

As shown in Table 5.22, the IMMS mean score of male students (mean =

132.191) was higher than the IMMS mean score of female students (mean =

122.152) using the MCI approach. However, Table 5.21, shows that the IMMS mean

score of the male students was significantly higher than the IMMS mean score of the

female students (mean difference = 10.040). Therefore, the first sub-hypothesis of

the eighth hypothesis was accepted and the male students showed a significant

difference in their IMMS score compared to the female students using the MCI

approach.

Table 5.21 ANOVA of IMMS Mean Scores of Gender on Multimedia Approaches

Dunnett C

I J Mean Std. 95% Confidence (Approach/ (Approach/ Difference Error Level Gender Gender (I – J) Lower Upper Bound Bound MCI/Male MCI/Female 10.040* 2.866 2.721 17.359 MCI/Male MOI/Male 1.620 2.817 -7.439 10.679

MCI/Female MOI/Female 3.892 2.788 -2.684 10.468

Note: * denotes significance at p<0.05 level.

As presented in Table 5.21, the male students using the MCI approach were

not significantly different in terms of motivation than the male students using the MOI

approach (the mean difference = 1.620). Although not significant, the IMMS mean

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score for the male students using the MCI approach (mean = 132.191) is higher than

the IMMS mean score for the male students using the MOI approach (mean =

130.571) as shown in Table 5.22. Thus, the second sub-hypothesis of the eighth

hypothesis was rejected.

Table 5.22 Descriptive Statistics of IMMS Mean Scores of Gender on Multimedia

Approaches Approach Gender N Mean Standard

Deviation MCI Male 47 132.191 12.388 Female 33 122.152 11.684 MOI Male 35 130.571 16.807 Female 54 118.259 9.944 Note: N denotes the number of students.

Table 5.22 exhibits that the IMMS mean score for female students using the

MCI approach (mean = 122.152) was higher than the IMMS mean score for female

students using the MOI approach (mean = 118.259). Table 5.21 reveals that there

was no significant difference between female students using the MCI approach and

the female students using the MOI approach (mean difference = 3.892) in their IMMS

score and therefore, the third sub-hypothesis of the eighth hypothesis was rejected.

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5.3 Summary of Findings

The findings of the research questions are summarised below:

Table 5.23 Summary on the Achievement Score Main Hypothesis/ Sub-Hypothesis

Hypothesis Results

Details

H11

_ _ XMCI > XMOI

Accepted

MCI mean= 64.613 MOI mean= 51.730 (F=16.991, p=0.000)

H22 (Multimedia Instruction)

_ _ XHA > XLA

Accepted

HA mean=60.622 LA mean=53.972 (F=4.440, p=0.037)

H22a (MCI)

_ _ XHA > XLA

Accepted

HA mean=71.167 LA mean=57.368 (mean dif=13.798, p=0.001)

H22b (HA)

_ _ XMCI > XMOI

Accepted

HA (MCI) mean=71.167 HA (MOI) mean=52.714 (mean dif=18.452, p=0.000)

H22c (LA)

_ _ XMCI > XMOI

Rejected

LA (MCI)mean=57.368 LA (MOI) mean=50.061 (mean dif=7.308, p=0.313)


_ _ XFI > XFD

Rejected

FI mean=60.831 FD mean=54.930 (F=3.286, p=0.072)

H23a (MCI)

_ _ XFI > XFD

Accepted

FI mean=70.744 FD mean=58.780 (mean dif=-11.963, p=0.006)

H23b (FI)

_ _ XMCI > XMOI

Accepted

FI (MCI) mean=70.744 FI (MOI) mean=52.045 (mean dif=18.698, p=0.000)

H23c (FD)

_ _ XMCI > XMOI

Rejected

FD (MCI) mean=58.780 FD (MOI) mean=51.422 (mean dif=7.358, p=0.202)


_ _ XMale > XFemale

Rejected

Male mean=58.390 Female mean=57.299 (F=0.663, p=0.417)

H24a (MCI)

_ _ XMale > XFemale

Rejected

Male mean=65.340 Female mean=63.576 (mean dif=1.765, p=1.000)

H24b (Male)

_ _ XMCI > XMOI

Accepted

Male (MCI) mean=65.340 Male (MOI) mean=49.057 (mean dif=16.283, p=0.000)

H24c (Female)

_ _ XMCI > XMOI

Accepted

Female (MCI) mean=63.576 Female (MOI) mean=53.463 (mean dif=10.113, p=0.035)

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Table 5.24 Summary on the Motivation Score (IMMS Score) Main hypothesis/ Sub-Hypothesis

Hypothesis Results

Details

H15

_ _ XMCI > XMOI

Accepted

MCI mean= 126.631 MOI mean= 122.038 (F=6.545, p=0.011)


_ _ XHA > XLA

Accepted

HA mean=130.768 LA mean=117.900 (F=51.367, p=0.000)

H26a (MCI)

_ _ XHA > XLA

Accepted

HA mean=133.571 LA mean=121.947 (mean dif=11.624, p=0.000)

H26b (HA)

_ _ XMCI > XMOI

Rejected

HA (MCI) mean=133.571 HA (MOI) mean=128.607 (mean dif=4.964, p=0.277)

H26c (LA)

_ _ XMCI > XMOI

Accepted

LA (MCI)mean=121.947 LA (MOI) mean=113.758 (mean dif=8.190, p=0.030)


_ _ XFI > XFD

Accepted

FI mean=126.592 FD mean=122.077 (F=6.325, p=0.013)

H27a (MCI)

_ _ XFI > XFD

Accepted

FI mean=132.436 FD mean=123.878 (mean dif=-8.558, p=0.015)

H27b (FI)

_ _ XMCI > XMOI

Rejected

FI (MCI) mean=132.436 FI (MOI) mean=130.136 (mean dif=2.300, p=1.000)

H27c (FD)

_ _ XMCI > XMOI

Accepted

FD (MCI) mean=123.878 FD (MOI) mean=116.222 (mean dif=7.656, p=0.030)


_ _ XMale > XFemale

Accepted

Male mean=128.353 Female mean=120.315 (F=20.047, p=0.000)

H28a (MCI)

_ _ XMale > XFemale

Accepted

Male mean=132.191 Female mean=122.152 (mean dif=10.040, p<0.05)

H28b (Male)

_ _ XMCI > XMOI

Rejected

Male (MCI) mean=132.191 Male (MOI) mean=130.571 (mean dif=1.620, p>0.05)

H28c (Female)

_ _ XMCI > XMOI

Rejected

Female (MCI) mean=122.152 Female (MOI) mean=118.259 (mean dif=3.892, p>0.05)

H11: The students who used the Multimedia Constructivist Instruction (MCI)

approach showed a significant difference compared to the students who used

the Multimedia Objectivist Instruction (MOI) approach in their achievement

score.

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H 12: The high-ability students showed a significant difference compared to the low-

ability students who used the multimedia instruction approach in their

achievement score.

H12a: The high-ability students showed a significant difference compared to

the low-ability students who used the MCI approach in their

achievement score.

H12b: The high-ability students who used the MCI approach showed a

significant difference compared to the high-ability students who used


H12c: The low-ability students who used the MCI approach did not show a

significant difference compared to the low-ability students who used


H13: The field-independent students did not show a significant difference

compared to the field-dependent students who used the multimedia

instruction approach in their achievement score.

H13a: The field-independent students showed a significant difference

compared to the field-dependent students who used the MCI


H13b: The field-independent students who used the MCI approach showed a

significant difference compared to the field-independent students who

used the MOI approach in their achievement score.

H13c: The field-dependent students who used the MCI approach did not


students who used the MOI approach in their achievement score.

H14: The male students did not show a significant difference compared to the

female students who used the multimedia instruction approach in their

achievement score.

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H14a: The male students did not show a significant difference compared to

the female students who used the MCI approach in their achievement

score.

H14b: The male students who used the MCI approach showed a significant

difference compared to the male students who used the MOI


H14c: The female students who used the MCI approach showed a significant

difference compared to the female students who used the MOI


H15: The students who used the MCI approach showed a significant difference

compared to the students who used the MOI approach in their IMMS score.

H16: The high-ability students showed a significant difference compared to the

low–ability students who used the multimedia instruction approach in their

IMMS score.

H16a: The high-ability students showed a significant difference compared to

the low-ability students who used the MCI approach in their IMMS

score.

H16b: The high-ability students who used the MCI approach did not show a

significant difference compared to the high-ability students who used


H16c: The low-ability students who used the MCI approach showed a

significant difference compared to the low-ability students who used


H17: The field-independent students showed a significant difference compared to

the field-dependent students who used the multimedia instruction approach in

their IMMS score.

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H17a: The field-independent students showed a significant difference

compared to the field-dependent students who used the MCI


H17b: The field-independent students who used the MCI approach did not


students who used the MOI approach in their IMMS score.

H17c: The field-dependent students who used the MCI approach showed a

significant difference compared to the field-dependent students who

used the MOI approach in their IMMS score.

H18: The male students showed a significant difference compared to the female

students who used the multimedia instruction approach in their IMMS score.

H18a: The male students showed a significant difference compared to the

female students who used the MCI approach in their IMMS score.

H18b: The male students who used the MCI approach did not show a

significant difference compared to the male students who used the


H18c: The female students who used the MCI approach did not show a

significant difference compared to the female students who used the


Implications of both the MCI and MOI treatments, the ability levels, cognitive

styles and gender towards the achievement score are discussed in chapter six. The

chapter also discusses the students’ perceived motivation towards the instructional

materials.

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CHAPTER 6

DISCUSSION, IMPLICATIONS AND CONCLUSION

6.0 Introduction

This study was designed to investigate the effects of a multimedia

constructivist environment on achievement and motivation in the learning of

chemistry among Form Four science students. Two types of courseware were

designed for the topic “Chemical Formulae and Equations” based on Constructivist

Learning Environment and Objectivist Learning Environment. Both courseware types

were similar in their content, but differed in the approach. The MCI was designed

using Jonassen’s Constructivist Learning Environment model (1999) while the MOI

was designed according to the objectivist linear approach. The study further

investigated the effects of MCI and MOI on high-ability and low-ability students, field-

dependent and field-independent students, as well as male and female students

achievement scores and IMMS scores.

The sample of this study comprised 169 Form Four science stream students

from two suburban secondary schools in Butterworth, Penang. They learned the

topic “Chemical Formulae and Equations” using the MCI courseware and MOI

courseware. Seven classes from both schools were involved. Since the classes

were not streamed, three classes were selected at random and were administered

the MCI approach while four classes were given the MOI approach. The

independent variables were the multimedia instruction approaches, the MCI and

MOI. The moderator variables were the ability levels (low-ability and high-ability),

cognitive styles (field-dependent and field-independent) and gender (male and

female). The dependent variables were the achievement score and the motivation

score (IMMS score).

The study was carried out between March and April 2006. The students were

given a pretest before the multimedia instructional treatment session. They were also

given the Cattell “Culture Fair” Intelligence Test and the GEFT test to categorise

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them in the low-ability and high-ability group and field-dependent and field-

independent group respectively. The students were then brought into the computer

laboratory and were randomly assigned the MCI and MOI approaches. During the

treatment sessions, the chemistry teacher became the facilitator. At the end of the

sessions, the students were given a posttest and the Instructional Materials

Motivation Scale (IMMS).

This chapter discusses the interpretations of the results. It focuses on the

effects of the multimedia instructional approaches on achievement and motivation in

general. It also discusses the effects of the multimedia instructional approaches on

the achievement and motivation based on their ability levels, cognitive styles and

gender. The summary and conclusions are subsequently discussed. The

implications of the findings for the educators and for future research are also

proposed.

6.1 Effects of the MCI and MOI Approaches on Achievement

The students who used the MCI approach to learn “Chemical Formulae and

Equations” significantly outperformed the students who used the MOI approach. This

finding supported the hypothesis that the students who used the MCI approach would

show a significant difference compared to the students who used the MOI approach

in their achievement score.

The MCI approach was designed in such a way that the students were trained

to construct their own means of interpreting a problem. This courseware applied a

problem-based method with a non-linear and branched approach. The objectives

were loosely defined and the content was not pre-specified in this courseware. The

result of this study supported Cunningham (1991) who expressed that constructivists

believe that learning is infinite and is not subject to scientific measures. The result

indicates that the students performed better to use this instructional strategy to obtain

tools for inquiring into a problem and various means for collecting information about a

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problem in order to understand or construct solutions to the problem. This is in line

with Jonassen’s (1991) view that constructivists believe that learner’s construct their

own reality and interpret it based upon their perceptions of experiences, so an

individual’s knowledge as a function of one’s prior experiences, mental structures,

and beliefs that are used to interpret objects and events.

The MCI courseware is designed in such a way that it challenges the

student’s thinking. When the students were provided with problems as in the MCI,

they felt challenged and started looking for ways to solve the problem. Along the

process, the students construct knowledge from experience. Therefore, learning

becomes a personal interpretation of the world and is an active process in which

meaning is developed on the basis of experience (Merrill, 1991).

The students also desired to construct their own knowledge by actively

participating in the learning process. Their learning environment was interactive,

collaborative, student-centred, active, based on authentic content and allowed

intentional learning. Students in the MCI approach conducted their evaluation within

the activity where the assessment came naturally from the situation in which the

instruction was embedded.

Contrastly, the MOI approach is based on behavioural and cognitive theories

of learning. The students in the MOI approach were provided with the knowledge

that needed to be transferred into their minds. The objectives were clearly defined

and all learners were expected to achieve the objectives in the same manner.

Therefore, they did not have the freedom to construct their knowledge and their

movement in the instruction was restricted. This could impose great boredom in

them and thus, their performance might not be up to the mark. Therefore, the

students who used the MOI approach did not perform as well as the students who

used the MCI approach.

This finding is in line with findings by Bowyer and Blanchard (2003) who

investigated multimedia regarding its utility as an enhancement mechanism. They

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reported that the majority of students agreed that effective learning, understanding

and notably relaxation were all significantly enhanced. Harwood and McMahon

(1997) who conducted a study on the effects of an integrated video media curriculum

enhancement on students’ achievement and attitudes also revealed a positive

students’ chemistry achievement across all levels and across a diverse multicultural

population.

Geban, Askar and Ozkan (1992) also found that the computer stimulated

experiment approach and the problem-solving approach produced significantly

greater achievements in chemistry and science process skills than the conventional

approach. Fong and Ng (2000) reported that learners using multimedia computer-

based learning on the learning of mitosis had a higher gain score although

insignificant.

The finding was also supported by Neo (2003) who designed a course that

was oriented towards a constructivist based paradigm by using multimedia as an

instructional tool. The multimedia mediated constructivist learning model was able to

enhance student learning and a learning process in which students participated

actively in a media rich environment and in an innovative manner.

Chen (2006) designed, developed and evaluated a virtual reality (VR)-based

learning environment with the aim to investigate the various issues that are related to

the use of this technology in teaching and learning. Learners exposed to the Guided

VR mode significantly outperformed their Non VR counterparts.

However, this finding is not consistent with the finding from Wainwright who

designed a microcomputer software package used as a supplement to chemistry

instruction. The study found that the group used parallel worksheet exercises

indicated a higher achievement score compared to the group received the

reinforcement via the computer.

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6.1.1 Effects of the MCI and MOI Approaches on Ability Levels in Achievement

The high-ability students performed significantly better than the low-ability

students who used the multimedia instruction to learn “Chemical Formulae and

Equations”. The high-ability students would usually have obtained a great deal of

content knowledge that is structured according to their individual understanding of a

subject matter. They also participate actively in their learning process and are more

confident in using the multimedia instruction. The students with high-ability found the

multimedia instruction very challenging since it had features that encouraged them to

explore their chemistry concepts. They were also able to go through the exercises

embedded in the courseware without much assistance from the teachers.

In contrast, the low-ability students were still struggling to organise their

knowledge structure and need more guidance on how to assimilate a new learning

tool as well as to understand the subject matter. Therefore, they could not participate

actively in their learning process and were lacking in confidence. They might not find

multimedia instruction to be as challenging compared to the high-ability students.

Furthermore, they depended on the teachers to guide them along the instruction.

The result of this study also indicates that high-ability students using the MCI

approach performed significantly better than the low-ability students using the MCI

courseware. This study also shows that the high-ability students who used the MCI

approach faired significantly better than the high-ability students who used the MOI

approach. From the results of this study, it was also determined that the low-ability

students using the MCI approach performed better but insignificant than the low-

ability students who used the MOI approach. Therefore, the high-ability students as

well as the low-ability students found MCI approach more challenging compared to

the MOI approach.

The high-ability students preferred the non-linear method with the objectives

that were not clearly defined. They were expected to explore the courseware on their

159

own and solve the given problems. Since the MCI was problem-based, the learning

activities were very challenging. This enabled the students to construct knowledge

using the modelling, coaching and scaffolding approaches as embedded in the

courseware.

However, the low-ability students performed better (although not at a

statistically significant level) in the MCI compared to the low-ability students in the

MOI. This indicated that the low-ability students also felt challenged when they were

given a problem and a learning tool to solve the problem. Although the high-ability

students might have performed significantly better due to their inner ability, it did not

discourage the low-ability students from doing better in the MCI approach compared

to the MOI approach. Therefore, it could be concluded that the MCI approach is

suitable for high-ability students as well as low-ability students.

Kong (2006) investigated the contribution of two different instructional

strategies, the Constructivist-Strategies Instruction (CSI) and Direct Instruction (DI),

using similar validated multimedia materials on learning and reported that the effects

of CSI were stronger for high ability and high internal LOC learners. However, the

result of this study contradicts the findings from Huppert et al. (2002), who

discovered that students with low reasoning abilities performed better that those with

high reasoning abilities in a multimedia setting. Yea-Ru Chuang (1999) and Cavin

and Lagowski (1978) also reported that the low-ability students performed better than

the high-ability students using multimedia instruction.

6.1.2 Effects of the MCI and MOI Approaches on Cognitive Styles in Achievement

The field-independent students performed better although insignificant than

the field-dependent students who used the multimedia instruction to learn “Chemical

Formulae and Equations”. This is because the multimedia instruction could provide

individualised instruction, teaching and problem solving and immediate feedback.

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These features are the desired properties that encourage the field-independent

students to perform better than the field-dependent students.

The field-dependent students relied on the teachers’ guidance and

assistance, and they preferred to work in groups. As such, they faced learning

difficulties in an individualised learning environment, as shown in this study.

Therefore, they might not excel if they are provided with individualised instruction.

This is in line with the findings by Canino and Cicchelli (1988) who expressed that the

field-dependent students preferred more guidance, including instructional techniques

like teacher-centred presentations.

The above rationalisation explains the outcome of this study and therefore,

the field-independent students performed better although insignificant than the field-

dependent students who learned using the multimedia instruction.

The result of this study also implies that field-independent students using the

MCI approach performed significantly better than field-dependent students using the

MCI courseware. The finding from this study also signifies that field-independent

students who used the MCI approach faired significantly better than field-

independent students who used the MOI approach. However, the result indicates

that the field-dependent students using the MCI approach did not perform

significantly better than the field-dependent students who used the MOI approach.

Compared to the field-dependent students, field-independent students were

able to establish a meaningful organisation of concepts when the MCI approach

provided them with an unstructured and non-linear environment. This clarifies that

the MCI approach was more appropriate for field-independent students rather than

field-dependent students since it was designed with an unstructured and non-linear

environment. The MCI approach also provided a more challenging approach where

the students were expected to construct knowledge at their own pace. The field-

independent students performed better than the field-dependent students because

the objectives were not clearly defined and the movement of the students along the

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courseware was not restricted. This learning environment was very suitable and

challenging for them.

As implied by Yea-Ru Chuang (1999), field-independent students also benefit

from greater media complexity as provided in the MCI approach. Liu and Reed

(1994) also reported that field-independent learners are more analytical in their

approach to processing information whereas field-dependent students are more likely

to employ a more global visual approach to learning. Therefore, this explains the

outcome of the results of this study. According to Witkin et al. (1971), the field-

independent students who are characterised by reliance on an external frame of

reference were discovered to be more capable at cognitive restructuring and

disembedding skills than field-dependent individuals who are characterised by

reliance on an external frame of reference. Canino and Cicchelli (1988) stated that

the achievement for field-independent students would be highest if they are given

activities that offer minimal guidance and encourage discovery methods as provided

in the MCI approach.

However, the field-dependent students preferred a linear sequencing of a

content that is clearly defined with sufficient assistance to go about the content.

They need guidance as in the MOI approach that gives a clear and detailed order of

instruction. Similarly, Summerville (1999) revealed that the field-dependent students

preferred more step-by-step instructions (social interaction) with more assistance in a

hypermedia environment.

This result is consistent with the findings by Yea-Ru Chuang (1999) who

reported that the field-independent students performed better in a multimedia

computer environment. Similarly, Meshot (1991), who investigated the effects of

real-time motion versus the still frame presentation mode and cognitive styles on an

interactive hypermedia knowledge task found that field-independent students scored

higher that the field-dependent students. Chang (1995) also documented that field-

162

independent students performed better that field-dependent students in the


This finding was also consistent with the findings by Hepner (1994) who

examined the effects of varying levels of visual complexity in computer-animated

graphic presentations. Myers (1997) also added more strength to the above result in

the investigation of the interaction between the cognitive styles and multimedia.

However, Summerville (1999) revealed that there is no significance difference

between the field-dependent students and field-independent students.

6.1.3 Effects of the MCI and MOI Approaches on Gender in Achievement

The finding from this research indicates that although the male students

performed better than the female students in the achievement score, the difference

was not significant. There is a substantial body of research that documents gender

differences in science and shows that males outperform females on science

achievement tests (Andersen & Nielsen, 2003; Turner & Lindsay, 2003; Yea-Ru

Chuang, 1999). However, the finding in this study indicates that the multimedia

instruction was consistent for both genders, resulting in improved performance for

both males and females. Therefore, the multimedia instruction that was designed

could reduce the gender gap in terms of learning achievement. It is important to

ensure that there is gender equity in the learning approach and the female students

should perform as well as their male counterparts.

The study also discovered that the male students did not perform significantly

better than the female students who used the MCI approach. Thus, this finding

suggests that the MCI approach was appropriate for both male and female students.

However, the male students who used the MCI approach scored significantly higher

than the male students who used the MOI approach. Similarly, the female students

who used the MCI approach performed significantly better than the female students

who used the MOI approach. The results indicate that both male and female

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students learned significantly better in a Constructivist Learning Environment

compared to the Objectivist Learning Environment.

The male students were expected to work independently and individually

whereas the female students were expected to show more social interaction, seek for

assistance and dependent (Allen, 2004). However, the result from this study shows

that the constructivist environment encouraged the female students to work as

independently and individually as the male students.

In the MCI courseware, the male and female students had to seek their own

means of constructing their own interpretation of a problem. They were given tools

and various means for inquiring into a problem in order to understand or construct

solutions to the problem. Therefore, both the male and female students felt

challenged when provided with a problem and were expected to find their way to

solve the problem.

In contrast, the linear approach and tutorial-based method in the MOI could

not increase both the male and female students’ achievement score significantly

better than the MCI approach. Learners were told about the content and were

expected to replicate its content and structure in their thinking. Since the MOI

instruction is based on the objectivist philosophy as the input-process-output model,

it is not challenging for the students. The male and female students might feel bored

with the clearly defined objectives in the MOI courseware.

Allen (2004) also documented that although science has been a traditionally

male-dominated field, there is a shift among the female students’ approach toward

leadership in the laboratory setting and in their beliefs about success in chemistry.

Therefore, from the results of this study, the Constructivist Learning Environment can

be considered to be the equalising treatment of male and female students.

The finding is consistent with findings by Kumar and Helgeson (2000) who

reported no significant difference between male and female high school students

solving stoichiometric chemistry problems using the hyper-equation software on

164

Macintosh computers when conducting an investigation on the effects of gender on

computer-based chemistry problem solving. Similarly, Puhan (2002) also indicated

that cognitive processing differences between males and females did not lead to

performance differences in different content areas in science. Another study that

offered no significant difference was by Huppert et al. (2002), who experimented

computer simulations in high school.

In contrast, Yea-Ru Chuang (1999) documented that male students

performed significantly better on the posttest in an “animation + text + voice” version

of a multimedia computer environment in Taiwan. Similarly, Bain et al. (1999)

reported that the technological competency of female participants in high access

integrated computer programmes exceeded that of male counterparts who

participated in a programme of reduced access and integration.

6.2 Effects of the MCI and MOI Approaches on the IMMS Score

The students who used the MCI approach to learn “Chemical Formulae and

Equations” were significantly more motivated than the students who used the MOI

approach. This finding supports the hypothesis that the students who are using the

MCI approach will show a significant difference compared to the students who used

the MOI approach in their IMMS score. This finding clearly indicates that students

are more motivated in a Constructivist Learning Environment.

The MCI courseware was designed according to an unstructured method and

non-linear environment. The objectives were loosely-defined and the students were

free to proceed with the courseware to construct their knowledge. They were

provided with tools for inquiring (such as help, examples, media) into the given

problems and various means of collecting information about the problem. These

features in the MCI encouraged the students to be more motivated. Conversely, the

MOI courseware was more structured with a linear approach. The objectives were

165

clearly defined and the students were more constrained since they had to follow the

flow of the instruction.

It is observed that new experience usually produces feelings of discomfort,

confusion, tension and anxiety to many students and eventually, negative feelings

hinder their motivation (Wood, 1994). However, this courseware has been designed

to stimulate positive feelings towards learning with the supporting tools that were

easily accessible. The students were able to navigate easily along the courseware

and assistance was provided in each exercise. Therefore, the finding exhibits that

the students were better motivated in the MCI approach.

This finding is in line with the results produced by Mistler-Jackson and Songer

(2000) who presented a case study on the use of authentic images and online

communication. The students showed a high level of motivation towards learning of

science. Similarly, Toh (1998), who conducted a study on cognitive and motivational

effects of two multimedia simulation presentation modes on science learning, also

found that the students showed higher motivation when presented with concurrent

simulation presentation mode. This result is also consistent with the results from

previous studies on motivation and design of instruction by Abdul Rahim (1990),

Rieber (1991) and Goh (1996).

6.2.1 Effects of the MCI and MOI Approaches on Ability Levels in Motivation

The high-ability students were significantly more motivated compared to the

low-ability students who used the multimedia instruction in learning “Chemical

Formulae and Equations”. This result indicates that multimedia instruction is more

effective than regular instruction in improving motivation, particularly for students with

high-ability. On the other hand, the low-ability students probably are still struggling to

adjust themselves and participate using a new instruction. Wood (1994) expressed

that something new typically produces negative feelings that might discourage

motivation.

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Certain aspects of the courseware such as complete learner control,

immediate feedback, and individual pace might have increased students’ motivation

towards the multimedia instruction. According to Yalcinalp, et al. (1995), motivation

reflects a high level of participation in the multimedia instruction and an individual’s

willingness to be taught by that strategy. Kearsly and Frost (1985) also found that

the kind of user control and employed instructional strategy involves a high level of

student involvement.

The multimedia instruction designed in this study managed to provide high-

ability students with learner control, freedom of movement, sufficient evaluation,

immediate feedback, adequate solution and individual pace that increased their

motivation towards multimedia instruction.

The results of this study also indicate that the high-ability students in the MCI

approach were better motivated than the high-ability students in the MOI approach

although the difference was not significant. The high-ability students were motivated

better when they were provided with multimedia instruction. Although they were

better motivated in the MCI approach, both the MCI and MOI elevated their interest in

the learning of chemistry concepts. The high-ability students possessed a great deal

of content knowledge that is structured according to their individual understanding of

a subject matter. Therefore they were easily motivated when given a new tool to

participate actively in their learning process.

The finding also revealed that there was a significant difference in motivation

between the low-ability students who were using the MCI approach with the low-

ability students who were using the MOI approach, with the former outperforming the

latter. This finding suggests that the MCI approach motivated the low-ability students

better compared to the MOI approach. This explains that although the low-ability

students were less motivated compared to the high-ability students, they were still

better motivated in a Constructivist Learning Environment. Therefore, the MCI

approach was beneficial for both the high-ability and low-ability students.

167

The MCI approach is very challenging and allows the students to explore

when solving problems. The design is interactive, collaborative, student-centred and

based on authentic content. Therefore, the high-ability students as well as the low-

ability students would find the MCI approach very exciting and motivating.

6.2.2 Effects of the MCI and MOI Approaches on Cognitive Styles in Motivation

The field-independent students are significantly more motivated compared to

the field-dependent students who used the multimedia instruction to learn chemistry.

This result indicates that multimedia instruction was more effective than regular

instruction in improving motivation particularly for field-independent students.

Hall (2000) documented that field-independent students are referred to as

“analytical, competitive, individualistic, task oriented, internally referent, intrinsically

motivated, hypothesis testing, self-structuring, linear, detail oriented, and visually

perceptive”. Most of these criteria are embedded in the multimedia instruction.

Conversely, the field-dependent students are referred to as “group-oriented,

global sensitive to social interactions and criticism, extrinsically motivated, externally

referential, not visually perceptive, non-verbal and passive learners who prefer

external information structures” (Hall, 2000). These criteria are basically not included

in a multimedia instruction. Therefore, this explains the reason for the field-

dependent students to be less motivated with the multimedia instruction. This

finding is in line with the findings reported by Lin and Davidson-Shivers (1996),

Messick (1978) and Yea-Ru Chuang (1999).

The finding from this study also showed that the field-independent students

were motivated significantly better than the field-dependent students who used the

MCI approach. In addition, the field-independent students who used the MCI

approach were not significantly more motivated than the field-independent students

who used the MOI approach. The result of this study also reveals that the field-

168

dependent students using the MCI approach were also more significantly motivated

than the field-dependent students using the MOI courseware.

These findings denote that the MCI approach was suitable to increase the

motivation for both field-independent students and field-dependent students. The

field-independent students were expected to perform and were motivated better in an

unstructured and non-linear environment whereas the field-dependent students were

expected to perform and were motivated better in a more structured and linear

environment. However, the results indicate that the field-dependent students also

preferred an unstructured and non-linear method but field-independent students were

far more motivated compared to field-dependent students due to the nature of their

dependence.

6.2.3 Effects of the MCI and MOI Approaches on Gender in Motivation

The male students who used the multimedia instruction were significantly

more motivated than the female students. Although there was no significant

difference in the achievement score between the male and female students who

used multimedia instruction, this finding indicates that the male students were

significantly more motivated in using the multimedia instruction compared to the

female students. Similarly, the male students were significantly more motivated than

the female students who used the MCI approach.

This finding also reveals that the male students who used the MCI approach

were not significantly more motivated than the male students who used the MOI

approach. Similarly, the female students who used the MCI approach were not

motivated more significantly than the female students who used the MOI approach.

However, the male students and female students were more motivated in the

Constructivist Learning Environment compared to the Objectivist Learning

Environment although they are not significantly more motivated.

169

6.3 Implications for Educators

Discussions on the findings of this research give evidence that the multimedia

constructivist learning environment is effective in enhancing students’ achievement in

the learning of chemistry especially for the topic “Chemical Formulae and Equations”.

This topic provides the fundamental chemistry concept for the Form Four students

and has a very abstract and formal nature (Chan, 1988; Gabel, 1999; Muth &

Guzman, 2000; Yalcinalp et al., 1995) and thus, it induces great influence on learning

difficulties (Yalcinalp et al., 1995). Therefore, to enable the students to understand

an abstract and formal topic, MCI was designed. This courseware was designed in a

constructivist environment that would be appropriate for a formal and abstract topic.

The findings also revealed that the multimedia constructivist environment is

effective in enhancing the students’ perceived motivation towards chemistry and

multimedia instruction. The students’ perceived motivation towards chemistry and

the MCI approach is significant compared to the students’ perceived motivation

towards chemistry and the MOI approach. The findings showed that the students’

perceived motivation towards chemistry and multimedia instruction increased when

exposed to self-learning multimedia instruction especially when they are expected to

construct their own knowledge. The students were able to arouse their interest in

chemistry as well as the MCI approach that stimulated an attitude of inquiry as

suggested by Keller (1983). The students were also able to link their needs, interests

and motives and make it relevant to their pesonal interests and goals.

In the traditional teaching and learning process, computers are used as a

teaching aid. Students are not usually provided with a courseware for teaching and

learning process. However, the existing courseware in schools that is used for the

teaching and learning process is designed for an Objectivist Learning Environment.

This courseware is designed according to a linear method and is tutorial-based.

Learners are told about the contents and are expected to replicate the contents and

structure in their thinking. The evaluation determines whether the objectives have

170

been met and to what degree. Since the findings from this study provide a positive

impact on the teaching and learning process, the MCI approach should be integrated

in a curriculum design or a Web-based design should be developed to improve

learning activities for Malaysian students. Therefore, there is an imperative need for

more courseware to be designed in a Constructivist Learning Environment to achieve

this objective.

The findings of the study also indicate that the male students did not perform

significantly better than their female counterparts. Therefore, the MCI approach is

suitable for both male and female students. The results also indicates that the field-

dependent students using the MCI approach performed better than field-dependent

students using the MOI approach; the low-ability students using the MCI approach

performed better than the low-ability students using the MOI approach although they

had not reached the level of significance. Thus, it would be appropriate to apply this

MCI approach in the development of instructional courseware.

There is a need to equip teachers with several authoring skills in order to

develop an effective and motivating courseware. Courseware designers ought to

learn “Authorware”, “Flash MX”, “Dreamweaver” and “Adobe Photoshop 6.0”

application software for this purpose. Therefore, courses on learning the software

should be introduced in teachers’ training programmes. Then, the teachers with

authoring knowledge could get together in workshops to design more Multimedia

Constructivist Instruction courseware.

The implementation of the MCI approach requires a computer laboratory.

The Education Ministry of Malaysian has installed computer laboratories in many

schools in Malaysia. Almost all these schools are now equipped with at least one

computer laboratory that consists of 20 computers. This should make the

implementation of MCI possible. However, more courseware and Web-based

learning materials designed in a Constructivist Learning Environment are needed.

171

6.4 Implications for Future Research

The findings from this study suggest that several factors have to be

considered for future research. There are many other approaches or methods of

constructing Constructivist Learning Environment that could be included in designing

multimedia instruction such as inquiry-based learning, discovery learning,

collaborative learning, cooperative learning and scaffolding. The constructivist

component that is appropriate for the topic “Chemical Fomulae and Equations” is a

problem-based learning. However, other topics in chemistry could be taught or

learned using different constructivist approaches or methods. The multimedia

instruction could be designed using more than one approach or method but these

should be appropriate for the topic.

The Multimedia Constructivist Instruction courseware designed in this study

employed the Jonassen’s (1999) Constructivist Learning Environment model that

consists of coaching, scaffolding and modelling embedded with support tools such as

related cases, information resources, cognitive tools, conversational and

collaboration tools, social and contextual support tools. Further research is needed

to determine the effect of each of the above components and support tools in

Jonassen’s Constructivist Learning Environment model in the achievement and

motivation of students.

The MCI approach could also be implemented for Form Five (fifth level of the

Malaysian secondary school – age between 17-18) and Form Six (pre-university –

age between 18-20) students. The effectiveness of this courseware might be

different due to the age differences. It would be worthwhile to investigate the

effectiveness of similar courseware among the Form Five and Form Six students.

The sample of the study was selected only from suburban schools due to time

constraints. However, this research could be repeated on urban as well as rural

schools to investigate the effectiveness of the MCI courseware. The students from

urban and rural areas might differ since the socio-economy background is said to

172

have strong influence over their performance in education (Hativa, 1989; Attewell &

Battle, 1997).

Further research could also be extended to mathematics and other science

subjects such as physics, biology, additional science, etc. This is to ensure that the

Constructivist Learning Environment benefits students from various fields of

sciences. The results from different science disciplines might vary according to the

subjects.

Another moderator variable that could be investigated is socio-economic

status. There are studies indicating that students from different socio-economic

background varied on their preferences towards computer-assisted instruction

(Hativa, 1989; Attewell & Battle, 1997).

6.5 Summary and Conclusion

Students are expected to deal with a huge volume of information that is

changing rapidly. It is imperative to provide them with skills to search for information,

use critical thinking to analyse the information and synthesise this information in a

meaningful manner and in an authentic situation. Thus, it is important to construct

lessons in an innovative and effective way to promote active learning and critical

thinking using the facilities that are currently available in schools. This study throws

light on how a Constructivist Learning Environment can provide a powerful

environment to promote learning especially in problem solving.

The ability levels and cognitive styles also need to be given emphasis since

the results in this study show differences in achievement between high-ability and

low-ability as well as field-dependent and field-independent students. These have an

important implication towards the development of the courseware that is adaptive

and customised to the psychological profile and ability levels of learners. The results

also indicate that both the male and female students learn significantly better in a

Constructivist Learning Environment compared to the Objectivist Learning

173

Environment. Therefore, the MCI designed using the Constructivist Learning

Environment can be considered to be a viable alternative for both male and female

students.

This study designed a multimedia courseware in a Constructivist Learning

Environment based on the model designed by Jonassen (1991). This courseware

consists of constructivist learning tools that seek to relate new ideas to experiences

and prior learning. The Constructivist Learning Environment employs conceptual

thinking and strategic learning, in contrast to reproductive learning (Jonassen, 1991).

However, the Constructivist Learning Environment might not be appropriate for all

learning outcomes. If the teachers intend to design learning environments to engage

learners in personal and/or collaborative knowledge construction and problem-

solving outcomes, the Constructivist Learning Environment offers a powerful

alternative.

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APPENDIX A

Mathematics and Science Achievement of Eighth-Graders in 1999

Mathematics

Science

Nation Average Nation Average

Singapore 604 Chinese Taipei 569

Korea, Republic of 587 Singapore 568

Chinese Taipei 585 Hungary 552

Hong Kong SAR 582 Japan 550

Japan 579 Korea, Republic of 549

Belgium-Flemish 558 Netherlands 545

Netherlands 540 Australia 540

Slovak Republic 534 Czech Republic 539

Hungary 532 England 538

Canada 531 Finland 535

Slovenia 530 Slovak Republic 535

Russian Federation 526 Belgium-Flemish 535

Australia 525 Slovenia 533

Finland 520 Canada 533

Czech Republic 520 Hong Kong SAR 530

Malaysia 519 Russian Federation 529

Bulgaria 511 Bulgaria 518

Latvia-LSS 505 United States 515

United States 502 New Zealand 510

England 496 Latvia-LSS 503

New Zealand 491 Italy 493

Lithuania 482 Malaysia 492

Italy 479 Lithuania 488

Cyprus 476 Thailand 482

Romania 472 Romania 472

Moldova 469 Israel 468

Thailand 467 Cyprus 460

Israel 466 Moldova 459

Tunisia 448 Macedonia, Republic of 458

Macedonia, Republic of 447 Jordan 450

Turkey 429 Iran, Islamic Republic of 448

Jordan 428 Indonesia 435

Iran, Islamic Republic of 422 Turkey 433

Indonesia 403 Tunisia 430

Chile 392 Chile 420

Philippines 345 Philippines 345

Morocco 337 Morocco 323

South Africa 275 South Africa 243

Average is significantly higher than the U.S. average

Average does not differ significantly from the U.S. average

Average is significantly lower than the U.S. average

187

APPENDIX B Average Mathematics Scale Scores of Eighth-Grade Students, by Country:

2003

Country Average score

International average1 466

Singapore 605

Korea, Republic of 589

Hong Kong SAR2,3 586

Chinese Taipei 585

Japan 570

Belgium-Flemish 537

Netherlands2 536

Estonia 531

Hungary 529

Malaysia 508

Latvia 508

Russian Federation 508

Slovak Republic 508

Australia 505

(United States) 504

Lithuania4 502

Sweden 499

Scotland2 498

(Israel) 496

New Zealand 494

Slovenia 493

Italy 484

Armenia 478

Serbia4 477

Bulgaria 476

Romania 475

Norway 461

Moldova, Republic of 460

Cyprus 459

(Macedonia, Republic of ) 435

Lebanon 433

Jordan 424

Iran, Islamic Republic of 411

Indonesia4 411

Tunisia 410

Egypt 406

Bahrain 401

Palestinian National Authority 390

Chile 387

(Morocco) 387

Philippines 378

Botswana 366

Saudi Arabia 332

Ghana 276

South Africa 264

Average is higher than the U.S. average

Average is not measurably different from the U.S.

Average is lower than the U.S. average

188

APPENDIX C

Average Science Scale Scores of Eighth-Grade Students, by Country: 2003

Country Average score

International average1 473

Singapore 578

Chinese Taipei 571

Korea, Republic of 558

Hong Kong SAR2,3 556

Estonia 552

Japan 552

Hungary 543

Netherlands2 536

(United States) 527

Australia 527

Sweden 524

Slovenia 520

New Zealand 520

Lithuania4 519

Slovak Republic 517

Belgium-Flemish 516

Russian Federation 514

Latvia 512

Scotland2 512

Malaysia 510

Norway 494

Italy 491

(Israel) 488

Bulgaria 479

Jordan 475

Moldova, Republic of 472

Romania 470

Serbia4 468

Armenia 461

Iran, Islamic Republic of 453

(Macedonia, Republic of) 449

Cyprus 441

Bahrain 438

Palestinian National Authority 435

Egypt 421

Indonesia4 420

Chile 413

Tunisia 404

Saudi Arabia 398

(Morocco) 396

Lebanon 393

Philippines 377

Botswana 365

Ghana 255

South Africa 244

Average is higher than the U.S. average

Average is not measurably different from the U.S.

Average is lower than the U.S. average

191

APPENDIX F

MINISTRY OF EDUCATION MALAYSIA INTEGRATED CURRICULUM FOR SECONDARY SCHOOLS

CURRICULUM SPECIFICATIONS

CHEMISTRY FORM FOUR

THEME : MATTER AROUND US LEARNING AREA : 3. CHEMICAL FORMULAE AND EQUATIONS

Learning Objectives

Suggested Learning Activities

Learning Outcomes Notes

3.1 Understanding and applying the concepts of relative atomic mass and relative molecular mass.

Collect and intepret data concerning relative atomic mass and relative molecular mass based on carbon-12 scale. Discuss the use of carbon-12 scale as a standard for determining relative atomic mass and relative molecular mass. Investigate the concepts of relative atomic mass and relative molecular mass using analogy or computer animation. Carry out a quiz to calculate the relative molecular mass of substances based on the given chemical formulae, for example HCl, CO2, Na2CO3, Al(NO3)3, CuSO.5H2O

A student is able to: state the

meaning of relative atomic mass based on carbon-12 scale,

state the meaning of relative molecular mass based on carbon-12 scale,

state why carbon-12 is used as a standard for determining relative atomic mass and relative molecular mass,

calculate the relative atomic mass and relative molecular mass.

Relative formula mass is introduced as the relative mass for ionic substances.

192

Learning Objectives



3.2 Analysing the relationship between the number of moles with the number of particles

Study the mole concept using analogy or computer simulation. Collect and interpret data on Avogadro constant. Discuss the relationship between the number of particles in one mole of a substance with the Avogadro constant. Carry out problem solving activities to convert the number of particles for a given substance and vice versa.

A student is able to: define a mole

as the amount of matter that contains as many particles as the number of atoms in 12 g of 12C,

state the meaning of Avogadro constant,

solve numerical problems to convert the number of moles to the number of particles of a given substance and vice versa.

Avogadro constant is also known as Avogadro number. 12C can also be representated as 12C or C-12 6

3.3 Analysing the relationship between the number of moles of a substance with its mass

Discuss the meaning of molar mass. Using analogy or computer simulation, discuss to relate:

a. molar mass with the Avogadro constant,

b. molar mass of a substance with its atomic mass or relative molecular mass.

Carry out problem solving activities to convert the number of moles of a given substance to its mass and vice versa.


meaning of molar mass,

relate molar mass to the Avogadro constant

relate molar mass of a substance to its relative atomic mass or relative molecular mass,

solve numerical problems to convert the number of moles of a given substance to its mass and vice versa.

Chemical formulae of substances are given for calculation.

193

Learning Objectives



3.4 Analysing the relationship between the number of moles of a gas with its volume

Collect and interpret data on molar volume of a gas. Using computer simulation or graphic representation, discuss:

a. the relationship between molar volume and Avogadro constant,

b. to make generalisation on the molar volume of a gas as s.t.p. or room conditions

Carry out an activity to calculate the volume of gases at s.t.p. or room conditions from the number of moles and vice versa. Construct a mind map to show the relationship between number of particles, number of moles, mass of substances and volume of gases at s.t.p. and room conditions. Carry out problem solving activities involving number of particles, number of moles, mass of substances and volume of gases at s.t.p. and room conditions.


meaning of molar volume of a gas

relate the molar volume of a gas to the Avogadro constant,

make generalisation on the molar volume of a gas at a given temperature and pressure

calculate the volume of gases at s.t.p. or room conditions from the number of moles and vice versa,

solve numerical problems involving number of particles, number of moles, mass of substances and volume of gases at s.t.p. or room conditions

s.t.p. – standard temperature and pressure

194

Learning Objectives



3.5 Synthesising chemical formulae

Collect and interpret data on chemical formula, empirical formula and molecular formula. Conduct an activity to:

a. determine the empirical formula of copper (II) oxide using computer simulation ,

b. determine the empirical formula of magnesium oxide

c. compare and contrast empirical formula with molecular formula

Carry out problem solving activities involving empirical and molecular formulae. Carry out exercises and quizzes in writing ionic formulae. Conduct activities to:

a. construct chemical formulae of compounds from a given ionic formula,

b. state names of chemical compounds using IUPAC nomenclature.


meaning of chemical formula,

state the meaning of empirical formula,

state the meaning of molecular formula

determine empirical and molecular formulae of substances,

compare and contrast empirical formula with molecular formula,

solve numerical problems involving empirical and molecular formulae,

write ionic formulae of ions,

construct chemical formulae of ionic compounds,

state names of chemical compounds using IUPAC nomenclature.

The use of symbols and chemical formulae should be widely encouraged and not restricted to writing chemical equations only. IUPAC –International Union of Pure and Applied Chemistry

195

Learning Objectives



3.6 Interpreting chemical equations

Discuss: a. the meaning of

chemical equations

b. the reactants and products in a chemical equation

Construct balanced chemical equations for the following reactions:

a. heating of copper (II) carbonate, CuCO3,

b. formation of ammonium chloride, NH4Cl

c. precipitation of lead (II) iodide, PbI2

Carry out the following activities:

a. write and balance chemical equations,

b. interpret chemical equations quantitatively and qualitatively,

c. solve numerical problems using chemical equations (stoichiometry)


meaning of chemical reaction

identify the reactants and products of a chemical equation

write and balance chemical equations

interpret chemical equations quantitatively and qualitatively

solve numerical problems using chemical equations

A computer spreadsheet can be used balancing chemical equation exercises.

196

Learning Objectives



3.7 Practising scientific attitudes and values in investigating matter.

Discuss the contributions of scientists for their research on relative atomic mass, relative molecular mass and chemical equations. Discuss to justify the need for scientists to practice scientific attitudes and positive values in doing their research on atomic structures, formulae and chemical equations. Discuss the role of chemical symbols, formulae and equations as tools of communication in chemistry.

A student is able to: identify positive

scientific attitudes and values practised by scientists in doing research on mole concept, chemical formulae and chemical equations

justify the need to practice positive scientific attitudes and good values in doing research on atomic structures, chemical formulae and chemical equations

use symbols, chemical formulae and equations for easy and systematic communication in the field of chemistry.

197

APPENDIX G

The Conceptual Map of “Chemical Formulae and Equations”

Relative Relative Avogadro Constant Atomic Mass Molecular Mass

Mass in grams Number of moles No. of particles Molar mass Volume of gas Chemical Equations

Empirical formula Molar volume of gas Chemical Formulae

Molecular Formula

198

APPENDIX H

Name:………………………………...……… Class:…………….……… School Name:……………………………….. Gender:…………………..

Pretest

CHEMISTRY

FORM FOUR


DO NOT OPEN THE EXAMINATION BOOKLET UNTIL YOU ARE TOLD SO Instructions 1. This booklet consists of 12 questions. 2. Answer ALL questions. 3. Please write your answer in the space provided for the subjective questions. 4. You can choose either one or more than one answer for the objective questions.

This paper consists of 6 printed pages.

199

1. Your friend needs your help to arrange four gases in order from the lowest

number of atoms to the highest number of atoms. Show her how you do it. [Relative atomic mass: H,1; O, 16; C, 12; N, 14; Ca, 40. Molar volume: 24

dm3 mol-1 at room conditions. Avogadro constant, NA, 6 x 1023 mol-1].

2 mol of ammonia, NH3 : _____________________________ atoms 2 mol of nitrogen gas, N2 : _____________________________atoms

2 mol of carbon dioxide, CO2 :__________________________atoms

1 mol of hydrogen gas, H2 :_____________________________atoms

_____________ ______________ ______________ _______________ lowest no. of atoms highest no. of atoms

[10m] 2. Chalcopyrite, CuFeS2 is a principal ore of copper. Show how you determine

the percentage composition by mass of copper in the ore. [Relative atomic mass: Cu, 64; Fe, 56; S, 32].

[8m] 3. New elements, X and Z are found. Show how you balance this equation. 2H2XO3 + AH3ZO3 BHX + CH3ZO4 + DH2O The coefficient C is: A. 2 B. 3 C. 4 D. 5

[10m]

Left Right H: H: X: X: O: O: Z: Z: H2XO3 + H3ZO3 HX + H3ZO4 + H2O

200

4. There are 44g of carbon dioxide, CO2, 22.4dm3 volume of oxygen at s.t.p., O2 and 6 x 1023 molecules of hydrogen, H2 gas. These substances consist of the same

[Relative atomic mass: C, 12; O, 16; H, 1. Molar volume: 24 dm3 mol-1 at room conditions. Avogadro constant, NA, 6 x 1023 mol-1]

A. relative molecular mass B. number of atoms C. number of molecules D. mass E. volume F. number of moles

[6m]

Figure 1

5. Referring to the above diagram, which of the following substances can be used in this experiment to determine the empirical formula. Explain you choice of substances.

A. magnesium oxide (MgO) B. sodium oxide (Na2O) C. argentum oxide (Ag2O) D. copper (II)oxide (CuO) E. zink oxide (ZnO) F. aluminium oxide (Al2O3) Explanation:__________________________________________________________ ___________________________________________________________________ ___________________________________________________________________

[6m]

201

Figure 2

6. Rita conducted an experiment to determine the empirical formula of magnesium oxide. Figure 2 shows the setup of the apparatus that Rita used. The empirical formula obtained from this experiment is Mg2O. This answer is wrong. Explain the possible mistakes that Rita could have made while conducting the experiment that resulted in the wrong answer.

(i) _____________________________________________________________ ______________________________________________________________ (ii) ______________________________________________________________ ______________________________________________________________ (iii) ______________________________________________________________ ______________________________________________________________ (iv) ______________________________________________________________ ______________________________________________________________

[8m]

Figure 3

7. Julia would like to conduct an experiment to determine the empirical formula of an oxide of metal X using the reaction between hydrogen gas and the oxide of metal X. Figure 3 shows the setup of apparatus for the experiment.

202

Explain to her the important steps that she should be taken in conducting this experiment.

(i) ______________________________________________________________ ______________________________________________________________ (ii) ______________________________________________________________ ______________________________________________________________ (iii) ______________________________________________________________ ______________________________________________________________ (iv) ______________________________________________________________ ______________________________________________________________

[8m] 8. NH3(g) + HCl(g) NH4Cl(g) What can be observed from the reaction above? A. A bright flame B. A thick white smoke C. A bright glow D. A green light

[5m]

9. Piperazine is used to kill intestinal worms. Its molecular formula is C4H10N2. Determine the number of molecules in 3.44 g of piperazine. Suzy gave the answer as 1.2 x 1023 molecules. Is she correct? Please explain. [Relative atomic mass: H,1; C,12; N,14; Avogadro constant, 6 x 1023mol-1].

[12 m] 10. Halothane is a gas used as a general anesthetic during a surgical operation.

It contains 17.52% carbon, 2.92% hydrogen, 51.82% chlorine and 27.74% fluorine by mass. Show how you determine the empirical formula for halothane. [Relative atomic mass: H,1; C, 12; F,19; Cl, 35.5].

[12 m]

203

11. A seaweed produces 36 cm3 of oxygen gas, O2 a day in photosynthesis, at

room temperature and pressure. Show how you calculate the number of oxygen molecules produced. [1 mole of gas occupies a volume of 24 dm3 at room temperature: Avogadro constant, NA, 6 x 1023 mol-1].

[12 m] 12. Calcium carbonate reacts with diluted hydrochloric acid according to the

equation below:

CaCO3 (s) + HCl (aq) CaCl2 (aq) + CO2 + H2O (l) What is the volume of carbon dioxide gas released when 5.0 g of calcium carbonate

is added to excess dilute hydrochloric acid solution at room temperature and pressure?

Rashid gave the answer as 2.4 dm3. Is he correct? Please explain. [Relative atomic mass: C, 12; O,16; Ca,40; 1 mol of gas occupies 24 dm3 at room temperature.]

[12m]

204

APPENDIX I

Name:………………………………...……… Class:…………….……… School Name:……………………………….. Gender:………………..….

Posttest

CHEMISTRY

FORM FOUR


DO NOT OPEN THE EXAMINATION BOOKLET UNTIL YOU ARE TOLD SO Instructions 1. This booklet consists of 12 questions. 2. Answer ALL questions. 3. Please write your answer in the space provided for the subjective questions. 4. You can choose either one or more than one answer for the objective questions.

This paper consists of 6 printed pages.

205

1. There are 6 x 1023 molecules of nitrogen gas, N2, 22.4 dm3 volume of oxygen gas, O2, and 44 g of carbon dioxide gas, CO2. These substances consist of the same

[Relative atomic mass: C ,12; O, 16; N, 14. 1 mol of gas occupies 22.4 dm3 at s.t.p. Avogadro constant, NA, 6 x 1023 mol-1]

A. relative molecular mass B. number of atoms C. number of molecules D. mass E. volume F. number of moles

[6m] 2. Piperazine is used to kill intestinal worms. Its molecular formula is C4H10N2.

Determine the number of molecules in 3.44 g of piperazine. Suzy gave the answer as 1.2 x 1023 molecules. Is she correct? Please explain. [Relative atomic mass: H,1; C,12; N,14; Avogadro constant, 6 x 1023 mol-1].

[8 m] 3. Chalcopyrite, CuFeS2 is a principal ore of copper. Show how you determine

the percentage composition by mass of copper in the ore. [Relative atomic mass: Cu, 64; Fe, 56; S, 32].

[8m]

4. Your friend needs your help to arrange four gases in order from the lowest number of atoms to the highest number of atoms. Show her how you do it.

[Relative atomic mass: H,1; O, 16; C, 12; N, 14; Ca, 40. Molar volume: 24 dm3 mol-1 at room conditions].

2 mol of ammonia, NH3 : _____________________________ atoms 2 mol of nitrogen gas, N2 : ______________________________atoms

2 mol of carbon dioxide, CO2 :___________________________atoms

1 mol of hydrogen gas, H2 :_____________________________atoms

_____________ ______________ ______________ _______________ lowest no. of atoms highest no. of atoms

[5m]

206

Figure 1 5. Referring to the above diagram, which of the following substances can be

used in this experiment to determine the empirical formula. Explain you choice of substances.

A. magnesium oxide (MgO) B. sodium oxide (Na2O) C. argentum oxide (Ag2O) D. copper (II)oxide (CuO) E. lead oxide (PbO) F. aluminium oxide (Al2O3) Explanation:__________________________________________________________ ___________________________________________________________________ ___________________________________________________________________

[5m]

Figure 2

6. Julia would like to conduct an experiment to determine the empirical formula of an oxide of metal X using the reaction between hydrogen gas and the oxide of metal X. Figure 2 shows the setup of apparatus for the experiment. Explain to her the important steps that should be taken in conducting this experiment.

(i) ______________________________________________________________

207

______________________________________________________________ (ii) ______________________________________________________________ ______________________________________________________________ (iii) ______________________________________________________________ ______________________________________________________________ (iv) ______________________________________________________________ ______________________________________________________________

[8m] 7. Calcium carbonate reacts with diluted hydrochloric acid according to the

equation below:

CaCO3 (s) + HCl (aq) CaCl2 (aq) + CO2 + H2O (l) What is the volume of carbon dioxide gas released when 5.0 g of calcium carbonate

is added to excess dilute hydrochloric acid solution at room temperature and pressure?

Rashid gave the answer as 2.4 dm3. Is he correct? Please explain. [Relative atomic mass: C, 12; O,16; Ca,40; 1 mol of gas occupies 24 dm3 at room temperature.]

[12m]

8. New elements, X and Z are found. Show how you balance this equation. 2H2XO3 + AH3ZO3 BHX + CH3ZO4 + DH2O The coefficient C is: A. 2 B. 3 C. 4 D. 5

[10m]

Left Right H: H: X: X: O: O: Z: Z: H2XO3 + H3ZO3 HX + H3ZO4 + H2O

208

Figure 3

9. Rita conducted an experiment to determine the empirical formula of magnesium oxide. Figure 3 shows the setup of the apparatus that Rita used. The empirical formula obtained from this experiment is Mg2O. This answer is wrong. Explain the possible mistakes that Rita could have made while conducting the experiment that resulted in the wrong answer.

(i) ______________________________________________________________ ______________________________________________________________ (ii) ______________________________________________________________ ______________________________________________________________ (iii) ______________________________________________________________ ______________________________________________________________ (iv) ______________________________________________________________ ______________________________________________________________

[8m] 10. A seaweed produces 36 cm3 of oxygen, O2, gas a day in photosynthesis, at

room temperature and pressure. Show how you calculate the number of oxygen molecules produced. [1 mol of gas occupies a volume of 24 dm3 at room temperature: Avogadro constant, NA, 6 x 1023 mol-1].

[8 m]

209

11. Halothane is a gas used as a general anesthetic during a surgical operation.

It contains 17.52% carbon, 2.92% hydrogen, 51.82% chlorine and 27.74% fluorine by mass. Show how you determine the empirical formula for halothane. [Relative atomic mass: H,1; C, 12; F,19; Cl, 35.5].

[8 m] 12. NH3(g) + HCl(g) NH4Cl(g) What can be observed from the reaction above? A. A bright flame B. A thick white smoke C. A bright glow D. A green light

[6m]

210

APPENDIX J

Cattell Culture Fair Intelligence Test

211

Pusat Teknologi Pendidikan dan Media

Universiti Sains Malaysia

PROJEK PENYELIDIKAN PENILAIAN

Ujian "g" BEBAS BUDAYA SKALA 2 BORANG B

(diterjemah dan disesuaikan dari

Cattell Culture Fair Intelligence Test, 1973, Institute for Personality and Ability Testing, U.S.A)

ARAHAN

Terdapat empat ujian di buku ini. Baca setiap soalan dan kemudian bulatkan setiap huruf untuk menunjukkan pilihan jawapan anda pada helaian jawapan yang diberikan. Jangan tulis apa-apa di buku ini. Jangan Buka Buku Sehingga Diberi Arahan.

BORANG Cattell

212

Sambung ke muka sebelah

213

Tamat Ujian 1 Berhenti!! Tunggu Arahan

214


215

Amat Ujian 2. Berhenti!! Tunggu Arahan

216


217

Tamat Ujian 3 Berhenti!! Tunggu Arahan

218


219

Tamat Ujian 4.

220

APPENDIX K


PROJEK PENYELIDIKAN PENILAIAN

Skala Motivasi Bahan Pengajaran (Instructional Materials Motivation Scale)

Kecenderungan Perbandingan

(Comparative Preference)

oleh

John M. Keller Florida State University, USA

diterjemah oleh

Toh Seong Chong Pusat Teknologi Pendidikan & Media

Universiti Sains Malaysia Januari, 1994

SMBP1

221

Jawapan anda kepada soalan-soalan berikut akan membantu kami merancang dan merekabentuk perisian Pengajaran Berbantuan Komputer (PBK) yang berkesan dan menarik. ARAHAN Terdapat 36 pernyataan dalam soal selidik ini. Baca setiap soalan dan kemudian bulatkan setiap huruf untuk menunjukkan pilihan jawapan anda pada helaian jawapan IMMS yang diberikan.

Soalan-soalan ini adalah mengenai pendapat dan sikap sahaja. Oleh itu tidak wujud jawapan betul atau salah. Cuma pilih jawapan yang paling sesuai dengan perasaan anda.

A = sangat tidak setuju B = tidak setuju

C = tidak pasti

D = setuju

E = sangat setuju

1. Apabila saya melihat pelajaran ini, saya berasa pelajaran ini mudah bagi saya.

2. Terdapat beberapa unsur yang menarik pada awal pelajaran ini dan ini menarik

perhatian saya.

3. Topik-topik yang ada dalam pelajaran ini lebih sukar difahami daripada yang saya sangka.

4. Selepas membaca maklumat pengenalan, saya berasa yakin bahawa saya tahu apa yang harus saya pelajari dari pelajaran ini.

5. Soalan-soalan kuiz memberi saya satu perasaan kepuasan atas kejayaan.

6. Adalah jelas kepada saya bahawa isi kandungan pelajaran ini ada berhubungkait dengan perkara-perkara yang sudah saya ketahui.

7. Beberapa skrin paparan komputer mengandungi maklumat yang terlalu banyak sehingga sukar untuk memetik dan mengerti butir-butir yang penting.

8. Bahan-bahan yang terdapat pelajaran ini menarik.

9. Terdapat cerita atau contoh dalam pelajaran ini yang menunjukkan kepada saya bagaimana bahan ini mungkin penting kepada sesetengah orang.

10. Menghabiskan pelajaran ini dengan jayanya adalah penting untuk saya.

11. Mutu penulisan pelajaran ini telah membantu saya terus berminat.

12. Pelajaran ini begitu abstrak sehingga sukar bagi saya memberi perhatian yang berterusan terhadapnya.

13. Apabila saya mencuba pelajaran ini, saya berasa yakin menguasai isi kandungan.

14. Saya amat menyukai pelajaran ini sehingga saya ingin tahu dengan lebih mendalam topik ini.

15. Paparan skrin dalam pelajaran ini membosankan dan tidak menarik.

16. Isi kandungan dalam pelajaran ini adalah relevan kepada minat saya.

222

Bagi soalan berikut baca soalan dengan teliti kemudian bulatkan A, B, C, D atau E untuk menunjukkan pilihan jawapan anda pada helaian jawapan IMMS yang diberikan

A = sangat tidak setuju B = tidak setuju

C = tidak pasti

D = setuju

E = sangat setuju

17. Cara maklumat disusun pada setiap skrin membantu saya memberi perhatian yang berterusan terhadapnya.

18. Terdapat penjelasan atau contoh mengenai bagaimana seseorang menggunakan pengetahuan dalam pelajaran ini.

19. Latihan atau kuiz dalam pelajaran ini terlalu sukar.

20. Pelajaran ini mempunyai unsur-unsur yang merangsangkan sikap ingin tahu saya.

21. Saya berasa sungguh seronok belajar pelajaran ini.

22. Ulangan yang terdapat dalam pelajaran ini menyebabkan saya kadang-kala berasa membosankan.

23. Isi kandungan dan gaya penulisan pelajaran ini memberi gambaran bahawa isi kandungannya sangat bermanfaat.

24. Saya mempelajari sesuatu yang menakjubkan dan di luar jangkaan saya.

25. Sejurus selepas saya mencuba pelajaran ini, saya yakin bahawa saya boleh lulus ujian tentangnya.

26. Pelajaran ini tidak relevan bagi keperluan saya kerana saya sudah pun tahu hampir kesemuanya.

27. Berbagai jenis keratan bacaan, latihan, kuiz dan ilustrasi telah membantu saya terus menumpukan perhatian saya terhadap pelajaran.

28. Gaya penulisan pelajaran ini membosankan.

29. Saya dapat menghubungkaitkan isi kandungan ini dengan perkara yang pernah saya lihat, lakukan atau fikirkan dalam kehidupan saya.

30. Terdapat terlalu banyak perkataan di setiap paparan skrin sehingga ia kurang menyenangkan.

31. Saya berasa seronok kerana telah menamatkan pelajaran dengan jayanya.

32. Isi kandungan pelajaran akan berguna kepada saya kelak.

33. Sebenarnya saya tidak memahami sebahagian maklumat yang disampaikan dalam pelajaran ini.

34. Organisasi kandungan yang begitu baik telah membantu saya menambahkan keyakinan saya mempelajari pelajaran ini.

35. Saya berbangga belajar dengan pelajaran ini yang telah direkabentuk dengan baik.

36. Saya telah mendapat ganjaran yang memadai dengan ikhtiar saya.

Terima kasih atas kerjasama anda.

223

APPENDIX L

Group Embedded Figures Test (GEFT) Materials Stop-watch, text booklets, and a set of sharpened soft black pencils with erasers. Extra pencils should be available. Directions Distribute test booklets and pencils. As soon as the identifying information on the cover page has been filled in, the Examiner says: Now start reading the Directions, which include 2 practice problems for you to do. When you get to the end of the Directors of Page 3, please stop. Do not go beyond Page 3.” Proctor should circulate in the room making sure the subjects are doing the two practice problems correctly and they do not turn past Page 3. When all subjects have finished reading the directions on Page 3, E says: “Before I give the signal to start, let me review the points to keep in mind.” (Read the statements at the bottom of Page 3, stressing the necessity for tracing all lines in the Simple Form, including the inner lines of the cube, simple form “ E” as well as for erasing all incorrect lines.) The examiner then says,” Are there any questions about the directions?” (He should then pause to allow questions) “Raise your hand if you need a new pencil during the test.” The examiner then says,” When I give the signal, turn the page and start the First Section. You will have 2 minutes for the 7 problems in the First Section. Stop when you reach the end of this section. You can begin now!” This section is primarily for practice with the format of the test. Proctors should circulate and give additional explanations to those who seem to be having difficlty with this set of practice items. After 2 minutes, the examiner says: “STOP - Whether you have finished or not. When I give the signal, turn the page and start the Second Section. You will have 5 minutes for the 9 problems in the Second Section. You may not finish all of them, but work as quickly and accurately as you can. Raise your hand if you need a new pencil during the test. Ready, go ahead.” After 5 minutes, the examiner says: “STOP – Whether you have finished or not. When I give the signal, turn the page and start the Third Section. You have 5 minutes for the 9 problems in the Third Section. Raise your hand if you need a new pencil during the test. Ready, go ahead.” After 5 minutes, the examiner says: “STOP – Whether you have finished or not. Please close your test booklets.”

224


Pusat Teknologi Pengajaran dan Multimedia

PROJEK KAJIAN ALAT PENGUKURAN PSIKOLOGI

UJIAN GEFT

Diterjemah oleh Profesor Madya Dr. Toh Seong Chong

© copyright 2005

Nama: ____________________________ Tingkatan: _________________________ Jantina: _____________________________ Sekolah: ____________________________

225

Arahan : Ini adalah satu ujian untuk menentukan kebolehan anda mengesan bentuk mudah yang tersembunyi dalam gambarajah yang kompleks.

Di sini ditunjukkan satu bentuk mudah "X"

Bentuk "X" ini tersembunyi dalam gambarajah berikut. Cuba kesankan bentuk "X" dan lukiskan bentuk yang sama saiz dan arah dalam gambarajah berikut:-

226

Jawapan yang diperlukan ialah:

Dengan menunjukkan kepada bentuk-bentuk mudah yang disenaraikan di muka surat 6, anda diminta mengesankan bentuk-bentuk yang dinamakan itu dalam gambarajah yang berikut dalam Bahagian Pertama, Kedua dan Ketiga. Anda boleh merujuk kepada muka surat 6 bila-bila masa. Lukiskan bentuk yang perlu di atas gambarajah tersedia. Padamkan semua kesalahan. Jangan bimbang jikalau tidak boleh mengesan semua bentuk yang diperlukan. Anda tidak dibenarkan memula tanpa disuruh. BAHAGIAN PERTAMA

Hentilah di sini dan tunggu arahan

227

BAHAGIAN KEDUA

Kesanlah bentuk “G” Kesanlah bentuk “A” Kesanlah bentuk “G”

Kesanlah bentuk “E” Kesanlah bentuk “B” Kesanlah bentuk “C”

Kesanlah bentuk “E” Kesanlah bentuk “D” Kesanlah bentuk “H”

228

BAHAGIAN KETIGA

Kesanlah bentuk “F” Kesanlah bentuk “G” Kesanlah bentuk “C”

Kesanlah bentuk “E” Kesanlah bentuk “B” Kesanlah bentuk “E”

Kesanlah bentuk “A” Kesanlah bentuk “C” Kesanlah bentuk “A”

229

Bentuk-bentuk yang diperlukan

230

APPENDIX M

Summary of Lessons’ Protocol for the Teachers to Conduct Multimedia Constructivist Instruction.

Prior to the implementation of the Multimedia Constructivist Instruction in the computer laboratories, the facilitator will be guided on how to help the students and the students will be given the Cattell “Culture Fair” Intelligence Test, GEFT Test and the pretest. 1. The facilitator will be trained on how to give instructions to the students

regarding the courseware. 2. The first part of the first lesson will be a demonstration on how to go about the

key boarding skill. (a) The students will be given the rules and regulations in the computer

laboratory. (b) The students will be taught on how to “switch on” and “switch off” the

computer. 3. The second part of the lesson will be a demonstration on how to go about the

courseware and the usage of the menu, sub-menu and tools in the courseware. The facilitator will demonstrate this to the whole class by using the LCD projector.

4. The students must be guided to use the courseware as they wish. They are

free to move about the courseware. 5. The students will be learning “Chemical Formulae and Equations” in 8

lessons (80 minutes per lesson). 6. At the end of the session, a posttest comprising of 12 questions will be given

to all students. 7. The students are encouraged to communicate with their friends in the

computer laboratory, ask teacher for help or even to chat with others in the network system regarding their subject matter. The facilitator has to make sure that the students do not misuse this opportunity for other purposes. This is in line with “the social and contextual support tool” in the Jonassen’s model of Constructivist Learning Environment.

8. On the whole, the implementation of the courseware will be in nine lessons,

one lesson for the instruction and demonstration and eight lessons for the learning process.

231

APPENDIX N

Summary of Lessons’ Protocol for the Teachers to Conduct Multimedia Objectivist Instruction.

Prior to the implementation of the Multimedia Objectivist Instruction in the computer laboratories, the facilitator will be guided on how to help the students and the students will be given the Cattell “Culture Fair” Intelligence Test, GEFT Test and the pretest. 1. The facilitator will be trained on how to give instructions to the students

regarding the courseware. 2. The first part of the first lesson will be a demonstration on how to go about the

keu boarding skill. (a) The students will be given the rules and regulations in the computer

laboratory. (b) The students will be taught on how to “switch on” and “switch off” the

computer. 3. The second part of the lesson will be a demonstration on how to go about the

courseware and the usage of the menu, sub-menu and tools in the courseware. The facilitator will demonstrate this to the whole class by using the LCD projector.

4. The students must be guided to use the courseware according to the

sequence of the tutorial. They must use the step-by step method to learn the topic. Their movement along the courseware is restricted.

5. The students will be learning “Chemical Formulae and Equations” in 8

lessons (80 minutes per lesson). 6. At the end of the session, a posttest comprising of 12 questions will be given

to all students. 7. The students are “not encouraged” to communicate with their friends in the

computer laboratory, ask the teacher for help or even to chat with others in the network system regarding their subject matter.

8. The facilitator has to monitor and make sure that the students do not misuse

the usage of computer and internet. 9. On the whole, the implementation of the courseware will be in nine lessons,

one lesson for the instruction and demonstration; and eight lessons for the learning process.

232

APPENDIX O

Reliability Pilot Test 1

****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) Reliability Coefficients N of Cases = 20.0 N of Items = 50 Alpha = .6317

Reliability Pilot Test 2

****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) Reliability Coefficients N of Cases = 20.0 N of Items = 30 Alpha = .7417

Reliability Pilot Test 3 ****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) Reliability Coefficients

233

N of Cases = 20.0 N of Items = 12 Alpha = .7564

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