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
ii
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
xi
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
pencapaian lebih baik secara signifikan dan lebih bermotivasi secara signifikan
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
pencapaian lebih baik secara signifikan dan lebih bermotivasi secara signifikan
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”.
xiv
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 &
2
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
3
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
5
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
6
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
7
(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
constructivist environment.
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
achievement score (as measured by the posttest minus the pretest).
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
using the MOI approach in their achievement score.
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
achievement score (as measured by the posttest minus the pretest).
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.
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).
H16: The high-ability students will show a significant difference compared to the
low-ability students who are using the multimedia instruction approach in their
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.
H16b: 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 IMMS score.
H16c: The low-ability students who are using the MCI approach will show a
significant difference compared to the low-ability students who are
using the MOI approach in their IMMS score.
H17: The field-independent students will show a significant difference compared to
the field-dependent students who are using the multimedia instruction
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
show a significant difference compared to the field-independent
students who are using the MOI approach in their IMMS score.
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 the MOI approach in their IMMS score.
H18: The male students will show a significant difference compared to the female
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
the MOI approach in their IMMS score.
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
constructivist environment.
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
multimedia instruction.
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.
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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
48
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
53
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
54
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
55
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
56
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
57
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,
63
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
constructivist environment.
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:
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
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:
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).
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.
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 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
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.
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:
H16: The high-ability students will show a significant difference compared to the
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:
H17: The field-independent students will show a significant difference compared to
the field-dependent students who are using the multimedia instruction
approach in their IMMS score.
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:
H18: The male students will show a significant difference compared to the female
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.
143
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.
H16b: 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 IMMS score.
H16c: The low-ability students who are using the MCI approach will show a
significant difference compared to the low-ability students who are using the
MOI approach in their IMMS score.
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
using the MOI approach in their IMMS score.
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
the MOI approach in their IMMS score.
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.
146
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)
H33 (Multimedia Instruction)
_ _ 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)
H44 (Multimedia Instruction)
_ _ 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)
H26 (Multimedia Instruction)
_ _ 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)
H37 (Multimedia Instruction)
_ _ 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)
H48 (Multimedia Instruction)
_ _ 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
the MOI approach in their achievement score.
H12c: The low-ability students who used the MCI approach did not show a
significant difference compared to the low-ability students who used
the MOI approach in their achievement score.
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
approach in their achievement score.
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
show a significant difference compared to the field-dependent
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
approach in their achievement score.
H14c: The female students who used the MCI approach showed a significant
difference compared to the female students who used the MOI
approach in their achievement score.
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
the MOI approach in their IMMS score.
H16c: The low-ability students who used the MCI approach showed a
significant difference compared to the low-ability students who used
the MOI approach in their IMMS score.
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
approach in their IMMS score.
H17b: The field-independent students who used the MCI approach did not
show a significant difference compared to the field-independent
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
MOI approach in their IMMS score.
H18c: The female students who used the MCI approach did not show a
significant difference compared to the female students who used the
MOI approach in their IMMS score.
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
155
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
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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-
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independent students performed better that field-dependent students in the
multimedia instruction.
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
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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
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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.
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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.
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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.
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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
Suggested Learning Activities
Learning Outcomes Notes
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.
A student is able to: state the
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
Suggested Learning Activities
Learning Outcomes Notes
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.
A student is able to: state the
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
Suggested Learning Activities
Learning Outcomes Notes
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.
A student is able to: state the
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
Suggested Learning Activities
Learning Outcomes Notes
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)
A student is able to: state the
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
Suggested Learning Activities
Learning Outcomes Notes
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
CHEMICAL FORMULAE AND EQUATIONS
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
CHEMICAL FORMULAE AND EQUATIONS
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]
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
220
APPENDIX K
UNIVERSITI SAINS MALAYSIA
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
UNIVERSITI SAINS MALAYSIA
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: ____________________________
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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:-
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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
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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”
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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”
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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.
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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.
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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
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