DYNAMICS OF STUDENTS’ SELF-REGULATED LEARNING WITHIN ECOLOGICAL MULTI-USER VIRTUAL-ENVIRONMENT.

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Khalifatulloh Fiel'ardh, 2013

Dynamics Of Students Self Regulated Learning Within Encological Multi User Virtual Environment Universitas Pendidikan Indonesia | repository.upi.edu

DYNAMICS OF STUDENTS’ SELF-REGULATED LEARNING WITHIN ECOLOGICAL MULTI-USER VIRTUAL-ENVIRONMENT

Research Paper

Submitted as a partial fullfilment of the requirement for the degree of Sarjana Pendidikan in Biology Education

by:

Khalifatulloh Fiel’ardh (0900750)

BIOLOGY EDUCATION DEPARTMENT

FACULTY OF MATHEMATICS AND SCIENCE EDUCATION INDONESIA UNIVERSITY OF EDUCATION

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DYNAMICS OF STUDENTS’ SELF-REGULATED LEARNING

WITHIN ECOLOGICAL MULTI-USER VIRTUAL-ENVIRONMENT

by:

Khalifatulloh Fiel’ardh Registration Number: 0900750

A Research Paper Submitted as a Partial Fulfillment for the requirements of the

Sarjana Pendidikan Degree in Faculty of Mathematics and Science Education

©Khalifatulloh Fiel’ardh 2012 Indonesia University of Education

July 2013

This work may not be printed or copied in whole or in part without

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iii Khalifatulloh Fiel'ardh, 2013

AUTHORIZATION PAGE

DYNAMICS OF STUDENTS’ SELF-REGULATED LEARNING

WITHIN ECOLOGICAL MULTI-USER VIRTUAL-ENVIRONMENT

by:

Khalifatulloh Fiel’ardh

Registration Number: 0900750

Approved and authorized by: First Advisor

Dr.Phil. H. Ari Widodo M.Ed NIP: 196705271992031001.

Second Advisor

Dr. H. Riandi M.Si NIP: 196305011988031002

Acknowledged by:

Head of Biology Education Department

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STATEMENT OF DECLARATION

I clarifiy that this research paper entitled ”Dynamics of Students’ Self-Regulated Learning within Ecological Multi-User Virtual Environment” is my own except where due references are made in the text of the paper, and that it contain no material

which has been accepted for the award of any degree or diploma at any

University. It is submitted in partial fulfillment of the requirements for a Sarjana

Pendidikan degree.

Bandung, July 2013

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v Khalifatulloh Fiel'ardh, 2013

ABSTRACT

This study examined the dynamics of students’ self-regulated learning within Multi-User Virtual Environment (MUVE). A blended learning activity within a MUVE-assisted ecology science curriculum was implemented to a sample of high school students. Qualitative data collection and analysis was embedded within a one-group posttest-pretest experiment to provide a better insight on how self-regulated learning fostering conceptual understanding of causality concepts within MUVE-based curriculum. 32 students from a bilingual private school in Bandung were purposively chosen as participants of this study. Participants were required to complete two quantitative instruments: the motivated strategy for learning biology questionnaire (MSLQ-B); a self-report instrument to measure students’ self-regulated learning and test of students’ conceptual understanding of causal pattern in ecosystem; a type of achievement test to assess students understanding of the causal pattern concept in ecosystem, before and after the intervention. Quantitative data analysis showed that intervention had an adequate impact to both self-regulated learning, as well to conceptual understanding in causality. The qualitative data analysis that acquired from interview, observation and content analysis, expanded the experimental outcomes by providing information on how the help-seeking was the most commonly used learning strategy. A low group engagement level could also be reported, and it implied that a competitive instead of collaborative learning atmosphere emerged. Pathway analysis showed that the self-regulated learning

component significantly affect students’ conceptual understanding.

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ABSTRAK

Penelitian ini bermaksud untuk mempelajari bagaimana dinamika kemandirian belajar siswa dalam Multi-User Virtual Environment (MUVE). Kegiatan blended learning diaplikasikan dalam pembelajaran ekologi berbantu MUVE terhadap sekelompok siswa SMA. Pengumpulan serta pembahasan data kualitatif disisipkan dalam desain penelitian one-group posttest-pretest experiment untuk memperoleh pemahaman tentang bagaimana kemandirian belajar membantu siswa untuk memahami konsep hubungan sebab-akibat dalam pembelajaran berbantu MUVE. 32 siswa dari sekolah swasta bilingual di Bandung secara purposif dipilih sebagai partisipan dalam penelitian ini. Para siswa mengisi dua instrumen kuantitatif: motivated strategy for learning biology questionnaire (MSLQ-B);

sejenis angket tertutup untuk mengukur kemandirian belajar siswa serta, test of students’

conceptual understanding of causal pattern in ecosystem; sejenis tes prestasi untuk mengases pemahaman siswa tentang pola-pola hubungan sebab-akibat dalam ekosistem. Analisis data kuantitatif menunjukkan bahwa pembelajaran dengan MUVE menyebabkan perubahan yang cukup baik pada kemandirian belajar siswa, serta pemahaman mereka tentang konsep hubungan sebab-akibat. Hasil analisis data kualitatif yang diperoleh melalui wawancara, observasi serta analisis konten, menjelaskan temuan eksperimental dengan memberikan informasi bahwa mencari-bantuan adalah strategi yang paling banyak digunakan siswa dalam pembelajaran tersebut. Rendahnya keterlibatan siswa untuk mengikuti kerja kelompok dalam pembelajaran memberi implikasi bahwa lingkungan belajar kompetitif yang terbentuk, bukan lingkungan belajar kolaboratif. Analisis jalur me-nunjukkan bahwa komponen-komponen kemandirian belajar secara signifikan ber-pengaruh terhadap pemahaman konseptual siswa.

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PREFACE

This study resulted from the desire to understand the complexity of

science learning in this 21st century. Nowadays teachers and students are capable

on accessing an enormous quantity of information available to be applied in

learning. As a consenting adult, it would not be difficult for teacher to filter and

control information for learning purpose. On the contrary, students were not

having the same capability to utilize the information maximally, they would rather

to access information related with their interest and enjoyment this condition will

eventually hindered learning process. Self-regulated learning, an emerging

construct in educational psychology were claimed to be an answer on fostering a

community of students that could utilize the enormous amount of information

optimally. Unfortunately this issue were still rarely addressed in the field of

scien-ce education, for that instanscien-ce it is becoming important to be studied.

Bandung, July 2013

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ACKNOWLEDGEMENT

All praise is for Allah Sub'hanahu wa Ta'ala and may His blessings, peace

and favours descend in perpetuity on our beloved Prophet Muhammad al-Mustafa,

Sallallahu 'alaihi wa Sallam who is mercy for all the worlds. I acknowledge Him, for

providing me the perseverance to complete this study. I would also like to express my

gratitude and thanks, to:

1. Dr. Phil Ari Widodo M.Ed., as my first supervisor for his professional insight,

consultations, and confidence as integral pieces that made it possible for me to

complete this study.

2. Dr. Riandi M.Si, as my second supervisor as well as the head of my department,

for his thought, consultations and nurturance that guide me upon completing this

study.

3. Dr. Widi Purwianingsih M.Si as my academic supervisor that have been giving

me nurturance so I can complete my undergraduate study, for all of these years

and for Dr. Siti Sriyati M.Si as the head of my study program.

4. Prof. Nuryani Rustaman M.Ed and Drs. Andrian Rustaman M.Ed.Sc for their

attention and motivation that encourages me to finish my study. Dra. Sariwulan

Diana M.Si, for her kindness, attention and critics upon my completion of this

research in particular and during my study in general.

5. My family and colleagues from class of 2009 from Biology Education department

and International Program for Science Education, for the prayers, support, and

confidence they had been given to me, upon accomplishing academic challenges I

encountered up to this point.

I realize that this study is still far from perfection, a more comprehensive study need

to be conducted in the next. In the end author hopes that this piece of study could

contribute to the development of Biology Education.

Bandung, July 2013

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LIST OF TABLES

Table 2.1 Components of Self-Regulated Learning ... 12

Table 2.2 Ecosystems’ Content National Curricular Demand ... 20

Table 3.1 Instruments of the Study ... 37

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Table 3.3 Distribution of Ecosystem Content and Causality Indicators in the

SCCP-E ... 39

Table 3.4 Statistical Tests to Examine Hypothesis. ... 42

Table 4.1 Student’s Role in the Multi-User Virtual Environment ... 47

Table 4.2 Design Element of EcoMUVE along with Their Use in Investigation ... 48

Table 4.3 Means of Pretest, Posttest and N-Gain Index Calculation of the MSLQ-B Scores ... 50

Table 4.4 Results of Paired t-Test of the MSLQ-B Scores ... 52

Table 4.5 Students’ Self-Regulated Learning Worksheet Score in EcoMUVE ... 81

Table 4.6 Results of t-Tests and calculation of n-gain of the SCCP-E ... 85

Table 4.7 Summary of Qualitative Findings ... 95

Table 4.8 Significant Correlation among SRL Components and SCCP-E Score . 97 Table 4.9 Results of Pathway Analysis ... 98

LIST OF FIGURES

Figure 2.1 Zimmerman’s Cyclical Model of Self-Regulated Learning ... 9

Figure 3.1 Embedded Experimental Research Design ... 33

Figure 3.2 Overall Research Procedure ... 41

Figure 4.1 Intervention Design for the Experimental Phase of the Study ... 49

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Figure 4.3 Students’ Response of the Internal Goal Orientation Factors ... 55

Figure 4.4 Students’ Response of the External Goal Orientation Factors ... 56

Figure 4.5 Students’ Response of the Task Value Factors ... 57

Figure 4.6 Students’ Response of the Control Beliefs Factors ... 59

Figure 4.7 Students’ Response of the Self-efficacy Factors ... 60

Figure 4.8 Students’ Response of the Test Anxiety Factors ... 62

Figure 4.9 Students’ Response of the Rehearsal Strategy Factors ... 64

Figure 4.10 Students’ Response of the Elaboration Strategy Factors ... 66

Figure 4.11 Students’ Response of the Organization Strategy Factors ... 67

Figure 4.12 Students’ Response of the Critical Thinking Strategy Factors ... 70

Figure 4.13 Students’ Response of the Self-Regulation Strategy Factors ... 71

Figure 4.14 Students’ Response of the Effort Regulation Strategy Factors ... 73

Figure 4.15 Students’ Response of the Time and Study Management Strategy Factors ... 74

Figure 4.16 Students’ Response of the Peer Learning Strategy Factors ... 76

Figure 4.17 Students’ Response of the Exploratory Behavior Strategy Factors ... 78

Figure 4.18 Students’ Response of the Communication Behavior Strategy ... Factors 79 Figure 4.19 Distributions of Students’ Group and Individual Engagement in EcoMUVE ... 82

Figure 4.20 Mean of the SCCP-E Scores ... 86

Figure 4.21 Mean of Energy Flow Theme of SCCP-E ... 87

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Figure 4.23 Mean of Matter Dynamic Stability Theme of SCCP-E ... 93

Figure 4.24 Correlational and Regression Relationship among variables ... 99

LIST OF APPENDICES

Appendix A. Research Instruments ... 116

Appendix B. Intervention ... 129

Appendix C. Findings, Data Transformation and Data Analysis ... 158

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1 Khalifatulloh Fiel'ardh, 2013

CHAPTER I INTRODUCTION

A. Background

Science is commonly perceived as a difficult subject (Liliasari et al., 2008:

81); despite students‟ innate curiosity about the natural world, science classes

rarely foster their interest. In most science classes students spend time listening to

lectures, carrying out preordained “cookbook” laboratory activities, and memori

-zing the science facts that are emphasized in current high-stakes tests, this

condition eventually made them losing interest in science as they move beyond

school level (Honey & Miller, 2010:8). This situation demands an urgent

intervention, especially for Indonesian students that were far lag behind their

global peer in terms of science achievement (Pearson, 2012:42).

The intervention should put student as the centerfold in which teacher act as a

facilitator rather than an instructor, or as the saying goes teacher are no longer

“sage of the stage” but should be a “guide by the side” (Manner, 2003:91). In this

approach, teachers spark students‟ interest by engaging them in investigations,

helping them to develop understanding of both science concepts and science

processes, while maintaining motivation for science learning (Honey & Miller,

2010:12).

Despite the number of efforts, it seems however that the improvement does

not yet meet the expectations of the stakeholders (Liliasari et al., 2008:82). This

could be attributed to the fact that most effort only focused on a certain aspect.

“…most of our research studies (in science education red.) are done in simplicity ways, for example the influence of X to students’ achievement, while improvement of students’ achievement is not to be expected from a change in one single variable….unfortunately, the pedagogical issues explored are limited certain issues, such as teaching methods, models, and approaches. In terms of science, most research treated science as a byproduct of the changes in pedagogical aspects. The nature of scientific concepts is rarely studied….”

(Widodo, 2008:8) Duit (in Widodo, 2008:7) implied that science education research should address

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One of the alternative innovations to promote students' science learning is

the utilization of information and computer technologies (ICT) (Liliasari et al.,

2008:82). Using technology in the science classroom benefits teachers and

students. Generally, teachers who use technology are more motivated,

knowledgeable in scientific inquiry and positive about teaching than those who do

not. The use of technology enables students to collaborate on meaningful research

projects with their classmates and students throughout the world. By working on

research projects, students learn to use scientific tools and technologies

appropriately (Colley in Manner, 2003:94).

In recent years Multi-User Virtual Environments (MUVEs) that was originally

emerged as gaming software, started to gain fame and attention as a learning

media. MUVEs incorporate a virtual world in which learners control characters

that represent their online personas. Through these so called „avatars‟, learners can

explore the world, interact with objects, communicate with other users, and

generate and test hypotheses while they explore (Nelson & Erlandson, 2007:1).

That instance made MUVEs a promising platform in improving students‟ learning

of science. One of the alternative innovations to promote students' learning of

science is by utilizing computers and information technologies (ICT) (Liliasari et

al., 2008:82). This was made possible as ICT enable learners to see and interact

with representations of natural phenomena that would otherwise be impossible to

observe (Honey & Miller, 2010:8).

One of the successful cases in the utilization of MUVE to foster learning of

causality nature of scientific concept was shown by a research conducted by a

group of researcher from Harvard Graduate School of Education (HGSE)

(Metcalf et al., 2011:1). They developed a software called EcoMUVE (Ecological

Multi-User Virtual Environment) which simulate an ecosystem along with

interaction among it‟s component that constantly change over a time period,

which provide a semi-authentic inquiry experience for student. Through an

experiment to a group of secondary school student it is shown that activities with

the virtual ecosystem significantly bridge understanding of one out of three

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Understanding causal patterns is important in learning ecosystem, as this

understanding is also used by ecologists to reason ecological phenomenon.

Grotzer (2011:8) explained that the absence of this understanding cause student to

structure information differently and end up distorting them, even when they are

taught with the accurate information. Experience alone does not help students

understand how to structure the concepts. Students need support to get beyond the

cognitive hurdles involved. Students typically reason using simple linear cause

and effect, where one thing directly makes another thing happen. Because of that,

most students were unable to reason science correctly and end up distorting them,

even when they are taught with the accurate information (Metcalf, 2011:8).

Though proven to be effective in fostering learning of complex science

topic through immersion of causality nature, learning with EcoMUVE is still

risky. Since, students are given access to a wide range of information represented

as text, graphics, animation, audio, and video structured in a nonlinear fashion

(Jonassen, in Azevedo & Cromley, 2004:524). Learning in such an environment

requires a learner to regulate his or her learning, that is, to make decisions about

what to learn, how to learn it, how much to learn, how much time to spend on it,

how to access other instructional materials, how to determine whether he or she

understands the material, when to abandon or modify plans and strategies, and

when to increase effort (Williams, in Azevedo & Cromley, 2004:254). Or in other

word student need to practice self-regulated learning.

According to one-of cited source self-regulated learning which being

referred as an important new construct in education (Boekaerts, 1999:445) defined

as “proactive processes that students use to acquire academic skill, such as

setting goals, selecting and deploying strategies, and self-monitoring one’s effectiveness, rather than as a reactive event that happens to students due to impersonal forces” (Zimmerman in Rosen, 2010:70). Self-regulated learning is essential to the learning process as it can help students to create better learning

habits and strengthen their study skills, apply learning strategies to enhance

academic outcomes, monitor their performance and evaluate their academic

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highly personalized, Self-Regulated Learning is not asocial in nature and origin.

Student can learn how to self-regulate their learning through instruction and

modeling from teachers, parents or colleagues. Teacher could utilize a variety of

effective instructional strategies for encouraging self-regulation in the classroom

(Zumbrunn et al., 2011:13).

Given those arguments a study was then important to be conducted. This

study follows the suggestion by Widodo (2008:8) in which several

multi-disciplinary issues: self-regulated learning (educational psychology), nature of

scientific concepts (life science) and multi-user virtual environment (educational

technology). This study is not only to point out the effectiveness of MUVE as a

learning media that foster understanding of scientific concepts and self-regulated

learning but also to highlight as well to present a different perspective about

research in science education. The perspective was expected to eventually help

community of science educators to understand the dynamics in learning.

This study was conducted in the middle-upper educational level, involving

high school students within ecosystem learning. Knowledge about ecosystems and

populations is an important strand of the life science, besides that, content

standards and the processes underlying ecosystems exemplify sophisticated causal

features inherent to ecosystem processes and relationships make it difficult to

understand the dynamics involved in concepts such as energy transfer, matter

recycling, decomposition, and interaction between biotic and abiotic factors

(Grotzer, in Metcalf, 2011:1). The ecosystem learning with this study was

conducted using the MUVE software, called Ecological Multi-User Virtual

Environment (EcoMUVE) as suggested by previous research (Metcalf, 2011).

EcoMUVE immersing the features of causal complexity, such as action at a

distance, time delays, and non-obvious causes in the virtual ecosystem facilitate

construction of the conceptual understanding of the complex causality patterns

(Grotzer, 2011:1).

The biggest challenge of this study is to present a deep and comprehensive

analysis about the problems of students‟ understanding of causality and self

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study. Both methods were combined to present “…a more complete picture of the

phenomenon under study than either method could produce on its own…”

(Fraenkel et al., 2011:584). This study combined a quantitative, pre-experimental

method with qualitative methods include interview, observation and content

analysis

B. Statements of the Problem

The problem that will be focused in this research is as follow “How is the dynamics of students’ self-regulated learning on understanding causal patterns in ecosystem via Multi-User Virtual Environment?”, the problem was then expanded

into several research questions as follow:

1. How is the students’ self-regulated learning behavior before and after the intervention?

2. How is the students’ self-regulated learning behavior before and after the intervention?

3. How is the students’ understanding of causal patterns before and after the intervention?

4. How is the correlation between self-regulated learning and students’ understanding of causal pattern after the intervention?

5. How is the effect of self-regulated learning to students’ understanding of causal pattern after the intervention?

6. How do the qualitative results explain and expand the experimental outcomes?

C. Purpose of the Study

The main purpose of this study was to examine the dynamics of students‟ self

-regulated learning on understanding causal patterns in ecosystem, via learning

with the Multi-User Virtual environment.

D. Problem Limitation

Due to the wide range of problems addressed, researcher then set a limitation

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1. Dynamics of students‟ self-regulated learning is represented by the change

of students‟ self-regulated learning and understanding of causal patterns in

ecosystem after the given intervention: Multi-User Virtual Environment.

2. The ecosystem concepts that were addressed in this study are energy flow,

matter cycle and balance of the ecosystem.

3. This study covered analysis of four out of five causal patterns: domino

causality, mutual causality, cyclic causality and dynamic stability.

E. Assumptions of the Study

Multi-User Virtual Environment (MUVE) offers an immersion of scientific

concepts which accommodate the perspective of causality through 3-D

visuali-zation. The open-ended nature of MUVE on the other hand require student to

self-regulate their learning in order to obtain optimal experience which in turn foster

understanding of complex ecology concept, by reasoning the causal patterns.

F. Hypotheses of the Study

The quantitative part of this study tested these hypotheses:

H1-1 : There is a significant difference of self-regulated learning before and after the intervention

H1-2 :There is a significant difference of understanding of the causal patterns in ecosystem before and after the intervention.

H0-3 : There is no significant correlation between self-regulated learning and

students’ understanding of the causal patterns in ecosystem after the intervention.

H1-3 : There is a significant correlation between self-regulated learning and

students’ understanding of the causal patterns in ecosystem after the intervention

H0-4 : There is no significant effect of self-regulated learning to students’ understanding of the causal patterns in ecosystem after the intervention. H1-4 : There is a significant effect of self-regulated learning to students’

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G. Significance of the Study 1. Theoretical Significance

As a reference to encourage the use of Educational MUVE as an alternative

learning media that foster understanding of scientific concepts as well as

self-regulated learning.

2. For researcher

Obtain experiences on how to conduct a computer assisted-learning with the

use of Educational MUVE, in order to encourage self-regulated learning on

understanding scientific concepts.

3. For other researchers

As a reference on how to conduct a mixed method study in science education

research and to introduce the urgently resolved but rarely studied issue within

science education research in Indonesia.

4. For teachers

To encourage the practice of promoting student‟s self-regulated learning in

everyday teaching, via educational Media such as MUVEs.

5. For students

Facilitate the construction of self-regulated learning behaviors and skills, as

well as understanding of scientific concepts for student participating in this

research.

H. Organization of the Writing

Chapter 1 gives introduction of the research, and it consists of seven

subchapters which are background of the study (A), statements of problems (B),

purpose of the study (C), problem limitation (D), assumption of the study (E),

hypotheses of the study (F), significance of the study (G) and organization of the

writing (H). Chapter 2 comprehensively discusses theories used in this research,

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ecosystem learning (C) and multi-user virtual environment (D), the chapter also

include related works (E) and theoretical framework of this study (F). Chapter 3

presents the operational definition (A), research design (B), data source (C),

instrumentation (D), data processing (D), data analysis (E) and the summary of

the previous subchapter (of Chapter 3) in the procedure of the study (F).

Chapter 4 include the findings and discussion of the study which organized

into several subchapter based on the design of the study into: pre-intervention

qualitative phase (A), experimental outcomes (B) and post-intervention qualitative

phase (C), that chapter also include the general discussion (D) that sums the idea

of the previous subchapter and closed with the implication of the study (E).

Finally in chapter 5 conclusion (A) and recommendation (B) of the study

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CHAPTER 3 RESEARCH METHOD

A. Operational Definition

To avoid confusion on understanding the content of this paper, operational

definitions of variables in this study are presented as follow:

1. Self-Regulated Learning

This variable was differed into three sub variable: a) as ability, which referred

to the score of worksheet which signified the completion of both individual and

group learning activity which administered during the intervention; b) as behavior,

which referred to students’ response of the self-report questionnaire that was

designed to assess the two scales of self-regulated learning: motivation and

learning strategy. The self-report questionnaire was administered before and after

the intervention; c) as event, which referred to additional data about students’ self

-regulated learning activity that acquired using interview and participative

observation. This variable was assigned as first dependent variable.

2. Understanding of the Causality Nature

This variable was defined as participants’ achievement in a paper-and-pencil test that consisted of 10 open-ended questions. The test was designed to assess participant’s understanding of causalities’ concepts in ecosystem. The test was administered before and after the intervention. This variable was assigned as

second dependent variable.

3. Multi-User Virtual Environment

The Multi-User Virtual Environment (MUVE) was referred to software that

simulated a 3-D pond ecosystem. The software was designed to visualize

causality-related concepts. The learning activities with this hypermedia consist of

two sessions, individual learning and face-to-face collaborative learning. During

the individual learning session, students explore the virtual ecosystem with the

guide of individual worksheet. While during the face-to-face session student

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complete the group investigation. Both worksheets were then scored and appointed as students’ engagement level. The learning activity using the EcoMUVE was assigned as independent variable.

B. Data Source

All participants were tenth grade students enrolled in a private bilingual

boarding school located in Bandung, West Java, Indonesia. Total of 32, 10th grade

female students from four different classes were assigned in this study. Those

participants were assigned purposively (Fraenkel et al., 2011:100) by researcher,

to assure that they were already familiar to the use of computers and software

prior to research. Participants were also informed about their involvement in the

research and they complete a letter of consent. They were allowed to withdraw

themselves during the research. Participants were not given any compensation for

their involvement in this research. Participants were then assigned into groups of

five to complete a learning session with the EcoMUVE during a two weeks

period.

C. Research Design

This research was conducted using a mix of Qualitative and Quantitative

Methods. Creswell & Clark (2007:12) explained that the combination of both

methods provided a better understanding of research problems than either

approach alone, since one method offers strength that offset weakness of

separately applied method as well as providing more comprehensive evidence of

the phenomena under study. While the research design that was incorporated in

this research is the Embedded Experimental Model Design (see Figure 3.1),

The quantitative part of this research was conducted using Pre-Experimental

method, an experimental research method that only involve one group of

participant and do not include the use of random assignment (of research sample)

(Fraenkel et al., 2011:275). This method was chosen because random assignment

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research was conducted using a combination of observation, interview and content

analysis (Fraenkel et al., 2011: 445).

Figure 3.1 Embedded Experimental Model Design

(Adapted from Creswell and Clark, 2007: 68)

Information:

quall before intervention : participant observation and teacher interview before the treatment

QUAN premeasure : pre-test of self-report questionnaire and understanding of causality achievement test

Intervention : Learning activity via EcoMUVE.

qual during intervention : Students’ entry of worksheet

QUAN postmeasure : post-test of self-report questionnaire and understanding of causality achievement qual after intervention Students’ entry of reflective journal and result

of semi-structured interview

Interpretation Interpretation by merging both quantitative and qualitative data, with priority on quantitative data.

D. Instrumentation

Below are presented the steps to develop each instrument administered in this

study.

1. Judgment

The instruments were first reviewed and judged by a group of experts. The

experts are college professor of either pedagogy of biology and ecological science. In total there were four experts (two of them are author’s advisor). The

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instruments were reviewed for its construct and content validity. The judgment

sessions were held during several sessions. After each judgment sessions the

instruments were revised. After the instruments were approved by the first

advisor, they were then administered in the pilot testing.

2. Try Out

After judgment session the quantitative instrument were administered in a pilot testing session to test its’ criterion validity. The tryout was done to a sample of the kinds of individuals that will be required to respond in the final data

collection. Since the participant of this research was high school students. A total

of 81 high school students, the instrument were being tested to a sample of

students who already learned the ecosystem topic.

3. Factor Analysis

After pilot testing, the quantitative instruments were analyzed using a test

item analysis. The open-ended item achievement tests and questionnaire analysis

was conducted using the ANATES uraian version 4.0.7 9 and IBM SPSS V.21, the

analysis itself encompassed these aspects:

a. Validity

A different type of validity was analyzed for each type of quantitative

instrument. For the achievement test the concurrent validity, which referred to a

type of criterion-related validity that estimate present understanding of the

test-taker, was analyzed (Gronlund, 1987:143). While the self-report questionnaire

was analyzed for its predictive validity, which refers to the ability of questionnaire to predict students’ self-regulated learning behavior (Gronlund, 1987:143). To measure both validities, researcher calculate (using the software) the validity

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b. Reliability

Reliability refers to the consistency of test scores-that is, to how consistent

they are from one measurement to another (Gronlund, 1987:138). To measure

this, researcher also used the software that was based on Kuder-Richardson

formula 20 (KR20), To measure this, researcher also used the software that was

based on Kuder-Richardson formula 20 (KR20), which is based on the proportion

of correct and incorrect responses to each of the items on a test and the variance of

the total scores (Cohen et al., 2010:236).

c. Degree of Difficulty

Item difficulty or degree difficulty denotes the potential of a question to be

answered correctly by the students (Gronlund, 1991:123). The simple step to

calculate this index is by using this following formula. After the index of

discrimination was obtained, researcher interpreted the value by referring to the

criterion (Arikunto, 2009).

d. Index of Discrimination

Index of Discrimination or Item discriminability referred to the potential

items in question to show differences between the groups of students (Cohen et

al., 2010:441). In this study the index of discrimination referred to the potential of the SCCP to differentiate students’ based on their understanding of the causal patterns in the ecosystem. The calculation within the software was based on this

simple equation (Cohen et al., 2010:442).

4. Results of Factor Analysis

The factor analysis of MSLQ-B showed the MSLQ-B have a very high reliability (Cronbach’s α= 0.918). It could also be reported that majority of test items are valid, some of the item were revised based on Judgment. While for the CAT, the reliability index was also high (Cronbach’s α= 0.87). The validity analysis showed that all of the true-or-false questions were having a low

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Some of the items (rcal>rtable) were used in this study, some of them were revised

based on judgment (0<rcal<rtable (see detail in the Appendix A).

5. Research Instruments

Table 3.1 presents instruments that are used in this study, along with their

data source and the aim of its incorporation in the study.

Table 3.1 Instruments of the Study standing of Causal Pattern in Ecosystem Achievement Test

Self-Regulated Learning Worksheet Students

(qualitative→

Anecdotal field note Observation Cover information

uncovered by other instruments.

a. Motivated Strategy for Learning Biology Questionnaire (MSLQ-B)

MSLQ-B is a self-report instrument designed to assess student's motivation

and their use of different learning strategies. This instrument was adapted from

MMSLQ (Mathematics Motivated Strategies for Learning Questionnaire), a

modified MSLQ. Just like the original MSLQ that was first developed by Pintrich

& de Groot (1990), the MSLQ-B. It used 5-point (1: not at all true of me to 5:

very true of me) Likert-type scale attitude. This instrument consisted 50 items

(See Appendix B) that are comprised of two scales, the motivation and learning

strategies, each scale consist of different categories, and each category consist of

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Table 3.2 Results of the Factor Analysis for the MSLQ-B Components

No. Validity Components No. Validity

S C F S C F

note: S= scale, C= category, F= factor, no.= item number; motivation categories: value factors (IGO= internal goal orientation, EGO= external goal orientation, TV= task

value), expectancy factors (CB= control beliefs, SE= self-efficacy), Affective factor (TA=

test anxiety); learning strategy categories: cognitive strategies factors (RE=rehearsal,

EA= elaboration, ORG= organization), metacognitive strategies factors (SC=

self-critics, SR= self-regulation), noninformational resource management (ER= effort

regulation, TSM= time and study management, PL= peer learning ), informational

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b. Students’ Conceptual Understanding of the Causal Pattern in Ecosystem

(SCCP-E)

This instrument was administered to measure student’s conceptual under

-standing of the causal patterns. This instrument consists of 15 true-false questions

and 10 open-ended essay questions (see Appendix A.2). The Ecosystem topic that

are used as the context belong to the basic competency 4.1 (“Analyze the relationship between the components of an ecosystem, change of matter and energy, and roles of human in maintaining ecosystem balance”) of the

competency standard 4 (“Describe roles of ecosystem’s components in energy

flow and biogeochemical cycles, also the use of the components of an ecosystem for life”), in the Indonesian Educational Unit (BNSP, 2006:178). This instrument was administered in the quantitative part of the study. Each question posed

different indicators that referred to both ecosystem topic and causality (see Table

3.3). Each indicator was then used to score the test.

Table 3.2 Results of SCCP-E Factor Analysis

(calculated via ANATES Uraian)

Reliability Index: 0.89 (very high)

EC CP Item Difficulty Discrimination Validity Decision

EF DC

1a 0.7 (very easy) 0.02 (low) 0.14 (very low) Item revised

1b 0.60 (medium) 0.30 (sufficient) 0.56 (sufficient) Item used

1c 0.8 (easy) 0.36 (sufficient) 0.68 (high) Item used

MC 1d 0.7 (medium) 0.57 (high) 0.61 (high) Item used

MC

DC 2a 0.5 medium 0.71 (high) 0.84 (high) Item used

CC

2b 0.5 (medium) 0.59 (high) 0.79 (high) Item used

2c 0.5 (medium) 0.65 (high) 0.69 (high) Item used

2d 0.5 (medium) 0.64 (high) 0.86 (high) Item used

BE DS 3a 0.5 (medium) 0.52 (high) 0.85 (high) Item used 3b 0.6 (medium) 0.45 (high) 0.75 (high) Item used

note: EC= ecosystem content; EF= energy flow, MC= matter cycle, BE= balance in ecosystem; CP= causal patterns. DC= domino causality, MC= mutual causality, CC= cyclic causality, DS= dynamic stability. Due to low validity all of

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c. Self-Regulated Learning Worksheet

To obtain the data of participants’ self-regulated learning during the learning activity, participants were required to complete two types of worksheets:

individual and group worksheet. Each worksheet was then scored using scoring

guide generated by author (see Appendix A). The rubric comprised of indicators

that indicate participants’ completion of both group and individual worksheet.

Each worksheet were scored independently and the acquired score were then appointed as students’ engagement level during the MUVE-assisted learning activity.

d. Qualitative Instruments

Several qualitative instruments (see Appendix A) were administered in this

study, included: reflective journal, interview schedule, and anecdotal field note.

The reflective journal is an open-ended questionnaire that was administered after

the intervention, to obtain additional information about students’ self-regulated

learning. The interview schedule was used to guide interview session before the

intervention (with teacher) and after the intervention (with students). The

anecdotal field note was completed by author to capture additional information

uncovered by other instruments. The variety of qualitative instrument were

administered as a techniques to check the validity of the acquired data.

E. Data Processing

1. Motivated Strategy for Learning Questionnaire Scoring

Since the main instrument that were used to measure SRL behavior data were

a questionnaire that consisted of ordinal scales (5-scale Likert type), the data must

first transformed into interval scale before furtherly analyzed. Method of

successive interval (MSI) was administered to transform the data. This method

implemented separately for each question of both posttest and pretest data (see

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2. Students’ Conceptual Understanding of Causal Pattern in Ecosystem

Achievement Test Scoring

The open ended questions were scored using a scoring guide generated by

author (see Appendix A), by adapting similar format from Grozter (2009:44-51).

The score represent the percentage of indicators within the participants’ answer of

the questions, so the maximum score for this test is 100. Resulted score were then

categorized using the tertiary even split method.

3. Self-Regulated Learning Worksheet

As mentioned before participants were required to complete two types of

worksheets: group and individual. Each worksheet was scored separately using a

scoring guide generated by author (see Appendix A). Average score of individual and group worksheet were then assigned as the score of participants’ self -regulated learning activity within the learning activity. Resulted score were then

categorized using the tertiary even split method.

4. Quantification of Qualitative Data

As an additional data to analyze the dynamics of students’ self-regulated learning, data from two qualitative methods: content analysis and semi-structured

interview were quantified into percentage Entries reflective journal and responses

from interview were then coded using the MaxQDA software to find the emerging

themes, as suggested by Creswell & Clark (2007:130).

B. Data Analysis

Analysis of the quantitative data was conducted mainly using the IBM

Statistical Package Software for Social Science (SPSS) version 21.0. (Argyrous,

2011). The analysis was conducted using descriptive statistics. The assumptions

tests of the two important properties of data distribution were also conducted to

determine what type of inferential statistics test that should be conducted. The two

basic assumptions were: normality (using Saphiro-Wilk Test) and homogeneity

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tests were conducted which include: linearity, autocorrelation and

hetero-scedasticity. After the assumption tests, researcher used inferential statistic test to

examine the proposed hypothesis as featured in Table 3.4. Detailed results of

each test were included in the Appendix C.

Paired sample test instead of one sample t-test was incorporated in this study

as there were no standard to be compared against the acquired data. For this

instance there were no standard of self-regulated learning ability, self-regulated

learning behavior and understanding of the causal pattern in ecosystem (Colardaci

et al., 2011:301-313)

Table 3.4 Statistical Tests to Examine Hypothesis.

Hypotheses Statistical Tests

H1-1 There is a significant difference of students’

self-regulated learning before and after the intervention.

paired sample z-test or Wilcoxon test

H1-2 There is a significant difference of students’

understanding of causal patterns before and after the intervention.

paired sample t-test or Wilcoxon test

H1-3 There is a significant correlation between

self-regulated learning component and students’ understanding of causal patterns

Pearson Product moment or Spearman

Correlation H1-4 There is a significant effect of self-regulated

learning’s component to students’ understanding of causal patterns.

Pathway Analysis

To specifically address the first and second research question, normalized gain

between pretest and posttest of both MSLQ-B and SCCP-E were also calculated

(See Appendix D for the detailed result). The acquired scores of the gain could

then be used to show the effectiveness of the given intervention. For this instance

the amount of gain expand the result acquired from the difference tests. The

normalized-gain was then categorized based on standards appointed by Hake

(1999:1). After completing the quantitative data analysis, the result of qualitative

data analysis were infused to enrich the acquired data. Crosscheck to the literature

review was also conducted to explain important findings.

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The summary of overall procedure in this study is presented in Figure 3.2 in

the following page.

Figure 3.2 Overall Research Procedures PROBLEM IDENTIFICATION

PROBLEM IDENTIFICATION SCIENCE LEARNINGSCIENCE LEARNING LITERATURE REVIEWLITERATURE REVIEW

CAUSALITY NATURE (Grotzer, 2009)

QUANTITATIVE PREMEASURE QUANTITATIVE POSTMEASUREQUANTITATIVE POSTMEASURE POST-QUALITATIVE

ORDINAL SCALE INTERVAL SCALEINTERVAL SCALE

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CHAPTER 5

CONCLUSION AND RECOMMENDATION

A. Conclusion

Referring to the main objective of this learning which is to examine the

dynamics of students’ self-regulated learning on understanding causal pattern in ecosystem via multi-user virtual environment, based on research finding, analysis

and discussion several conclusions could be made as follow.

Experimental quantitative outcomes revealed that students were more engaged

in an individual learning activity rather than a group learning activity within the

Multi User Virtual Environment. Qualitative findings expand that outcome by

pointing out that this condition might be attributed to some problem: incompetent

group leader, reliance on face-to-face discussion rather than using online platform

and poor time management.

Experimental quantitative outcomes revealed that there was a significant

difference of students’ self-regulated learning after learning via the Multi User virtual environment; although a low gain was reported for both motivational and

learning strategy scale. Qualitative findings expand that outcome by providing

evidence that students need more prompt from teacher to be able to increase their

self-regulated learning skill. Experimental quantitative outcomes revealed that

there was a significant difference of students’ understanding of causal pattern in

ecosystem after learning via Multi-User Virtual Environment, with a medium gain

reported.

Correlational analysis showed that among the self-regulated learning

components only: individual engagement, internal goal orientation, critical

thinking and self-efficacy that significantly correlated with academic

achievement, which in this study refers to students’ understanding of causality

patterns. Highest correlation that could be recorded in this study was shown by the

individual engagement. Pathway analysis showed that only internal goal

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have a significant effect to students’ achievement, in terms of conceptual

understanding of causality.

Qualitative findings expand that outcome by providing evidence that

students were overly focus on their individual learning thus remaining them

unable to obtain the overall idea about the learning activity thus rendering the

relatively mediocre performance during achievement test. Overall, the qualitative

findings provide evidence that most students were unable to apply a variety of

learning strategy, they tend to overuse the help seeking strategy to solve every

problem.

B. Recommendations

The introduction of the educational psychology and educational

techno-logy issue in this study is an attempt by the researcher to provide a wider

perspective on how research study could be conducted in the field of biology

education in particular and science education in general. It is suggested for future

researchers that have a similar interest to study this issue, should devise a more

comprehensive intervention that could foster students’ self-regulated learning. For

instance an externally regulated learning environment in which teacher monitor

students learning could be applied as most students were still unable to complete

the group learning activity. A class discussion in the end of learning activity

would also important as it will endorse the collaborative learning environment

instead of competitive learning environment. Future research should also focus in

one of the self-regulated learning components (eg. focus on the motivation or the

learning strategy)

Regarding the educational technology, in future research a more

elaborative, interactive and visually appealing learning media should also be

devised in order to maximize the potential of technology-enhanced learning

environment. Future researcher should also cover different topic that are usually

difficult for students to learn. A larger sample should also be administered in this

kind of study to provide answer for the internal validity unable to be addressed in

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