Creativity of Biology Students in Online Learning

Creativity of Biology Students in Online Learning: Case Study of Universitas Terbuka, Indonesia

by

Diki Diki

Claremont Graduate University 2015

© Copyright (Diki Diki), (2015) All rights reserved. APPROVAL OF THE REVIEW COMMITTEE This (dissertation/thesis) has been duly read, reviewed, and critiqued by the Committee listed below, which hereby approves the manuscript of (student name) as fulfilling the scope and

APPROVAL FROM THE REVIEW COMMITTEE

Thomas F. Luschei, Chair Claremont Graduate University Associate Professor of Education

David Drew Claremont Graduate University Joseph B. Platt Professor of Education Mihalyi Csikszentmihalyi Claremont Graduate University Distinguished Professor of Psychology and Management

ABSTRACT Creativity of Biology Students in Online Learning: Case Study of Universitas Terbuka, Indonesia

This is a study about the effect of students’ attitudes of creativity toward their learning achievement and persistence in an online learning program. The study also investigated if there was an effect of indirect effect of attitudes of creativity toward learning achievement and persistence through learning strategies. There are three learning strategies, which are deeplearning, strategic-learning, and surface-learning. The participants were students of the department of biology and the department of biology teacher training in Universitas Terbuka (UT – Indonesia Open University), a distance learning university in Indonesia. The researcher sent the questionnaire through email to students who lived throughout Indonesia. There were 102 students participated in the survey. The instruments were rCAB test for value and attitudes toward creativity (Runco, 2012) and approaches and Study Skills Inventory for Students (ASSIST) test (Speth, 2013). There were four research questions (RQ) in this study. The first was if there was a relationship between attitudes of creativity and persistence. The researcher used independent samples t test technique for RQ 1. The second was if there is a relationship between attitudes of creativity and learning outcome. The researcher used multiple regressions for RQ2. The third was if there was an indirect relationship between attitudes of creativity and persistence through learning strategy. The fourth question was if there is an indirect relationship between attitudes of creativity and learning outcome through learning strategy. The researcher used multiple regression for RQ3 and path analysis for RQ 4. Controlling variables were age, income, departments, gender, high school GPA, and daily online activities. The result showed

that fun, and being unconventional negatively predicted learning outcomes while high school GPA positively predicted learning outcome. Age and high school GPA negatively predicted persistence while being unconventional positively predicted persistence. Two variables of deeplearning strategy predicted learning outcome. There were indirect relationships between attitudes of creativity and learning outcomes through deep-learning strategy.

ACKNOWLEDGEMENT

I would like to begin by thanking Allah the Most Gracious and the Most Merciful. He made me who I am today. There is nothing is possible without His blessings. I thank my wife Nian, my son Fikri and my daughter Nisa whose prayers care, and understanding always inspires me. Although I have been away for many years, I always want let them know that I always feel their presence. I am indebted to Dr. Thomas Luschei, the chair and academic supervisor. Not only Dr. Luschei did have a tremendous impact on my academia and academic skills, but also he has outstanding personality. I would like to thank my committee members Dr. Mihalyi Csikszentmihalyi and Dr. David Drew who had a tremendous impact on my academic skills and from whom I did learned a lot. I would like to thank the World Bank through Japan Indonesia Presidential Scholarship for its financial support. I appreciate the support from Universitas Terbuka, Indonesia for providing support and data for the study. I would like also to thank International Place of Claremont Colleges which supports me to adjust with the campus life. Lastly, I thank Muslim Students Association of Claremont Colleges. The organization provides a network for religious activities not only in the campus, but also with local Muslim community in Claremont.

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TABLE OF CONTENTS

ACKNOWLEDGEMENT .............................................................................................................. v Table of Contents ........................................................................................................................... vi CHAPTER I: INTRODUCTION .................................................................................................. 10 Statement of Purpose................................................................................................................. 10 Significance of the Study ............................................................................................................ 4 Background of the Study ............................................................................................................. 5 Theoretical Framework ............................................................................................................... 7 Definition of Terms ................................................................................................................... 10 Limitations of the Study ............................................................................................................ 10 CHAPTER II: LITERATURE REVIEW ..................................................................................... 12 Theories of Creativity................................................................................................................ 12 Creativity Type 1: mini-c .......................................................................................................... 14 Creativity of Type 2: big-C ....................................................................................................... 16 Creativity of Type 3: little-c ...................................................................................................... 17 Creativity of Type 4: Pro-c........................................................................................................ 17 Creativity in Education.............................................................................................................. 18 Student Difficulty in Learning Biology .................................................................................... 22 Creativity in Learning Biology ................................................................................................. 23 Perspectives of Creativity in Learning Biology ........................................................................ 27 The Use of Online Learning in Higher Education .................................................................... 28 The Role of Learning Strategies................................................................................................ 32 Universitas Terbuka (UT) ......................................................................................................... 35 Summary of the Literature Review ........................................................................................... 38 CHAPTER III: METHODOLOGY .............................................................................................. 40 Research Participants ................................................................................................................ 41 Data Collection Procedures ....................................................................................................... 42 Protection of Human Subjects ................................................................................................... 43 Instrumentation.......................................................................................................................... 44 Pilot Test ................................................................................................................................... 47 vi

Data Analysis and Variables ..................................................................................................... 48 CHAPTER IV: RESULTS ............................................................................................................ 54 CHAPTER V: DISCUSSION ....................................................................................................... 74 Implications for Practices .......................................................................................................... 97 Areas for Future Research ....................................................................................................... 100 Limitations .............................................................................................................................. 103 CHAPTER VI: CONCLUSION ................................................................................................. 105 REFERENCES: .......................................................................................................................... 109 Appendix A. List of instruments ................................................................................................. 123 Appendix B: Attitudes and Values ............................................................................................. 123 Appendix C. Learning strategy test (Speth et. al, 2007). ............................................................ 125 Appendix D. Demographic information ..................................................................................... 126

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LIST OF FIGURES

Figure 1 A concept map of the theoretical framework ………………….………………….8 Figure 2. Four-C Model (Kaufman and Beghetto 2009)…………………………………. Error! Bookmark not defined. Figure 3. Model 1 for RQ 1 and RQ 2……………………………………………………... 51 Figure 4. Model 2 for RQ 3 and RQ 4……………………………………………………... 53 Figure 5. The before-diagram of path analysis……………………………………….

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Figure 6. The after-diagram of a relationship between creativity and persistence……

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Figure 7. The before diagram of RQ 4 …..………………………………………………. 67 Figure 8. After diagram of RQ 4. Bookmark not defined.

………………………………………………………Error!

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LIST OF TABLES Table 1. Reliability Test Result of rCAB Test .............................................................................. 47 Table 2. Reliability Test Result of Learning Strategies ................................................................ 48 Table 3. The Required Data .......................................................................................................... 49 Table 4. Descriptive Statistics (N=102) ........................................................................................ 55 Table 5. Indicative Items of Creativity from rCAB Test .............................................................. 56 Table 6. Contraindicative Items of rCAB ..................................................................................... 57 Table 7. Items Of Learning Strategies…………………... ………………………………….….58 Table 8. Result of Independent Samples t test ………………….………………………………60 Table 9 . Group Statistics of Independent Sample T Test……………………………………… 61 Table 10. Regression of Examination Score ……………………………………………………62 Table 11. Regression of Persistence ………..…………………………………………………...63 Table 12. Regression of Examination Score…………………………………………………… 65 Table 13 Regression of Examination Score………………………………………………….…. 68 Table 14. Regression of Var 6 ……………………………………………………………….… 69 Table 15. Regression of Var 10……………………………………………………………….…70 Table 16. Decomposition of Bivariate Covariation……………………………………………...72

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CHAPTER I: INTRODUCTION Statement of Purpose Student success is a priority for any online learning program, thus also for biology education in Indonesia. However, a problem for online learning is the low rate of student persistence. The low persistence means that there was high percentage of students who did not finish their courses. According to Belawati (1998), 95% of the students at the Universitas Terbuka (UT- Open University of Indonesia), a distance learning university in Indonesia, from 1984 through 1990 did not finish their studies, while and Ratnaningsih and Santoso (2010) reported that in 2007, 86.40% of students at the department of management at UT did not finish their studies. Nearly 20% of the students who took the general biology course at the Universitas Terbuka in 2014 did not take the final exam (UT, personal communication). Another problem that students in the UT have is a low rate of achievement. Data from UT in 2014 show that 29.14 % of the general biology students who took the final exam received very low scores and failed the examination (UT, personal communication). One cause of this problem is that the students lack self-awareness regarding their own goals and are not informed as to methods of learning in an online learning environment (Ratnaningsih, Saefudin, and Wijayanto 2007). The problem is more serious in online learning than in face-to-face learning since the former are unable to meet the instructors in person. Additionally, while online courses require independent learning skills, the courses themselves fail to develop such skills. As a result, students’ persistence and achievement levels in online learning are low (Belawati 1998; Catropa 2013). Student persistence and achievement at UT is critical. UT has 450,000 students who live across the huge archipelago of Indonesia and has a mandate in 2015 to train one million elementary school teachers to the level of four-year x

postsecondary degrees. Therefore, there is a great need to identify factors that can help increase the student persistence and achievement in online learning in Indonesia. One factor that supports student achievement and persistence in online learning is creativity. Creativity is finding new and useful way to perform daily activities, including learning (Kaufman and Sternberg 2007; Runco 2004; Necka, Grohman, and Slabosz 2009). According to Runco (2004), creativity is an ability to transform objective world into original interpretation and to decide its usefulness. Runco (2007) also suggested that people tend to consider if their new ideas are appropriate in their community. The creative process takes place when students learn new information. They learn by adapting the information so that it fits to their existing cognition. They also adjust their understanding to learn the new information. Creativity is required in distance-education

students. For example, as online learning does build on regular meetings

between student and teacher, students need special skills to bridge this gap (Pearce 2013). Creativity in biology includes idea generation, analogy, integrating different concepts, and evaluation (Dunbar 1997: De Han 2009, Mumford et al. 2010; Lawson 2001). There are direct and indirect effects of creativity on student achievement and persistence. The direct effect of creativity is to help students transform knowledge during the learning process (Runco 2004). Learning strategies are how students approach and accomplish learning. The indirect effect of creativity occurs through the application of deep-learning strategy. According to Entwistle, Tait, and McCune (2000), there are three approaches to learning: surface, strategic, and deep strategies. Students who possess a deep learning strategy use creative ways to solve problems. According to Entwistle (2000), students who apply deep-learning strategy focus on seeking meaning, using evidence, looking for patterns, examining the logic, and relating ideas, which will lead indirectly to an increase in their achievement and persistence.

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As students with deep learning strategies are more likely to be more creative, it is likely that those students will be more successful. Therefore, there is great value in studying learning strategies and how they contribute to student achievement and persistence, especially for online learning. The purpose of this research is to investigate the relationship between attitudes of creativity, learning strategies, persistence and examination scores among biology students in an online course in Indonesia, while controlling students’ high school GPA, age, gender, department, daily online activities, and family income, the specific research questions are: RQ1. What is the relationship between attitudes of creativity and persistence? RQ2. For those who persist (i.e., those who complete the final examination), what is the relationship between attitudes of creativity and achievement? RQ3. To what extent is attitudes of creativity related to student persistence through learning strategy? RQ4. For those who persist (i.e., those who complete the final examination), to what extent is attitudes of creativity related to student achievement through learning strategies? This study used survey questions to address the relationship between attitudes of creativity and learning strategies of biology students in an online learning program. This research used several quantitative analyses to describe the relationship between attitudes of creativity, learning strategies, the students’ examination scores, and students’ persistence. The research also used multiple regression analysis to find which variable best predicts students’ success. Participants in the study were students of the Universitas Terbuka, who were taking the introductory biology course. The questionnaire was sent through the email to the students.

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Significance of the Study My study examined the role of attitudes of creativity in student success in online learning. Since most research on creativity in biology applies to face-to-face learning environments, this research has provided a noteworthy contribution for biology education specifically and science education more broadly. The study has shown which components of creativity best predict student success. Earlier studies have shown that each component of creativity in biology has different effects toward the student’s learning success. Lawson (2001) pointed out evaluation as the most significant factor of creativity. Students develop new ideas and have their ideas discussed to make it more relevant during the evaluation. Meanwhile, Dunbar (1997) suggested that analogy is one of the most important factors of creativity in biology. Analogy is an important skill in biology, since students may learn by connecting new information with their prior knowledge. In this respect, the study added to the previous research by elaborating on the interactions of the components of creativity. In Indonesia, biology has been taught through online courses since 2001 at the UT. In addition, the program included some face-to-face tutorials and laboratory practice. However, there have been some practical difficulties, such as different levels of experience in learning biology and lack of skills necessary for laboratory practice (Hewindati and Zuhairi 2009). This dissertation would benefit both the students and the university. As the participants of the research were new students, the results were relevant concerning the problems associated with new online learning students. The benefit of the study for the students would be that it can reveal the ways that they can improve their learning results. The students can learn to solve their learning problem although they cannot always contact the instructor. They can also develop more collaboration with other students.

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The study has enriched the research of online learning in biology in Indonesia. Other studies of how students learn biology did not involve either online instruction or distance learning. The study of Nugraini (2013) focused on Indonesian high school students. Meanwhile, the study of Hewindati and Zuhairi (2009) involving biology students of the UT focused on laboratory practice. The current study, focusing as it did on creativity in online study, provided input about how students learn biology through such systems. Its results have shown the benefit of creativity for learning biology in this context. The study had implications beyond the field of biology, as it has benefited UT and other universities in Indonesia. For the UT, the benefit of the study has been the introduction of input concerning the role of creativity in learning strategies. With the information generated by this study, the university could provide advice for new students about how to learn in an online environment. Since UT collaborates with universities inside and outside Indonesia, the study has provide knowledge concerning best practices in online learning that can be shared with those universities as well. Background of the Study The two biggest problems of students in online learning are low retention and low achievement. Between 1984 and 1991, the completion rate for students at the UT was 4.8 % (Belawati 1998). This result was in line with Ratnaningsih and Santoso (2010) reported that in 2007, 86.40% of students at the department of management at UT did not finish their studies. Despite its flexibility and popularity, the level of success of online learning is low. In addition to the low retention of online learning, biology is a difficult subject. The research by Kibuka-Sebitosi (2007) conducted with 11th and 12th grade students from South Africa found misconceptions about the topics of genetics and inheritance. The result was

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supported by Cimer (2012), who studied 207 11th grade students in Turkey. Cimer found that there are five topics in biology that were considered difficult: the matter cycles, endocrine system and hormones, aerobic respiration, cell division, and genes and chromosomes. A study by Haambokoma (2007) in Zambia showed that certain areas of genetics, like crosses, calculations, genetic terms, mutations, meiosis, mitosis, sex determination, variations, and co-dominant are the most difficult topics. However, there are not many studies about online learning in biology. The cited studies were conducted in face-to-face learning, which still allows the teacher to guide students in person. The studies consisted of high school students of different ages and levels of understanding regarding biology; this does not match well with a population of university students. There is little research about creativity and its relation to learning strategies among biology students in online learning. Earlier research on creativity in biology education (De Han 2009; Dunbar 1997; Lawson 2001; Mumford et al. 2010) was set in the classroom or in the classroom’s associated laboratory activities. Based on the research questions, the hypothesis for the study was that students with higher attitudes of creativity are more likely to have higher levels of persistence and higher learning achievement as measured by examination score. The researcher measured attitudes of creativity with the rCAB (Runco Creativity Assessment Battery) test (Grivina 2013), which measures attitudes and values toward creativity. Students’ learning strategies were measured using Approaches and Study Skills Inventory for Students (ASSIST) test (Speth,Namur, and Lee, 2013), to find out their learning strategies, which were deep-learning, strategic-learning, and surface learning. Another hypothesis is that attitudes of creativity have an indirect effect on student achievement and persistence through learning strategy. Students engaging in the deep

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learning strategy are more likely to be more creative (Arend 2006; Entwistle, McCune and Walker, 2000). Attitudes of creativity predicts students’ learning strategies (Entwistle, McCune and Walker, 2000). Theoretical Framework The theoretical framework for this dissertation is based on the earliest work by a study by Richvana, Dwiastuti, and Prasetyo (2012) showed that there is a significant influence of students’ creativity levels on the learning results of the biology students. The participants in the study were senior high school students in Indonesia. The study used the Group Investigation Model (Sharan and Sharan 1990) as the framework. The model includes creativity as a part of the learning process. Students’ creativity is important as it helps students gather, analyze, integrate, present, and evaluate new data and transform that new data into new knowledge. Activities in the learning process were group discussion, finding of information, and synthesizing of findings. The researcher established experimental and control groups of participants. The experimental group was provided with instruction under the Group Investigation Model; the control group adhered to the conventional learning practices. The results of the study showed that students with higher creativity produced higher scores. The authors suggested that the effect of the learning model was more dominant for the psychomotor domain of students, while creativity was more related to the cognitive and affective domain of the students. Richvana, Dwiastuti, and Prasetyo (2012) also asserted that the use of the Group Investigation Model is also related to the deep-learning strategy of the students. The students using the Group Investigation Model learn by asking questions, participating in group discussion and making presentation. Therefore, there should be a study on the learning style of the students.

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This model showed that attitudes of creativity plays a significant role in learning biology. Figure 1 shows the relationship between attitudes of creativity, students’ low retention, students’ difficulties in learning biology, learning strategies, and the research questions. Figure 1 shows a relationship of attitudes of creativity with student achievement scores and persistence. In this respect, students with higher attitudes of creativity are more likely to finish their study in the online class. They can find different ways to analyze new knowledge (Kaufman and Sternberg 2007) Moreover, students with higher attitudes of creativity are more likely to have a higher score in final examination.

Figure 1. The concept map of the theoretical framework. Learning strategy is also part of the study to measure students’ creativity. Learning strategy is cognitive means by which individuals acquire measurable learning (Martin and Saljo 1976). There are three kinds of learning strategies, which are deep learning, surface learning, and strategic learning. The more creative students are more likely to study with deep learning. They connect the topic that they are learning with previous knowledge and they find alternative

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solutions (Entwistle, McCune and Walker, 2000.). The study also measures the relationship between student creativity and learning strategy. For purposes of this study, this research defines creativity a ability to find new and useful way to perform daily activities, including learning. The definition draws from mini-c creativity by Kaufman and Sternberg (2007), personal creativity by Runco (2004), and fluid creativity by Necka, Grohman, and Slabosz (2009). The definition of creativity employed in this study has been used in previous studies concerning creativity in learning biology. For example, De Haan (2009) used the mini-c creativity of Kaufman and Sternberg for a study about the importance of creativity for learning biology. As suggested by Richvana, Dwiastuti, and Prasetyo (2012), any research about creativity should consider the differences of learning emphasis across different cultures. Lan and Kaufman (2012) suggested that there was a different attitude of creativity between American and East Asian students. Wang (2007) provided evidence that American students scored higher in elaboration compared to Taiwanese students. The American students scored higher in evaluation, critical thinking, and exact verbal expression. The difference of cultural background showed that there should be more research on creativity among Indonesian students. This study took into account the effect of students’ backgrounds in relation to their learning results. Higher-achieving or wealthier students may have had both greater levels of creativity (the independent variable) and persistence and test scores (the dependent variables). To isolate the impact the attitudes of creativity on persistence and exam scores, net of previous achievement and income, this study measured students’ high school GPAs and socio economic status (SES) in an effort to control for the effect of these variables on the outcomes under study.

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The focus of the study was attitudes of creativity in biology students. Son’s (2009) study did not elaborate on the need for creativity among biology students in online learning (Son 2009). Path analysis was the technique to describe both the direct and indirect relationships between creativity, learning strategies, and the examination scores of biology students in an online learning program. Definition of Terms Creativity. This term refers to finding new and useful way to perform daily activities, including learning (Kaufman and Sternberg 2007; Runco 2004; Necka, Grohman, and Slabosz 2009).. Distance education. According to Moore (1993), distance education refers to method of instruction that are applicable when space and/or time separates teachers from learners. Learning strategy. In general, this term refers to the cognitive means by which individuals acquire measurable learning (Martin and Saljo 1976). Online learning. This term refers to learning that results through programmed interaction over the internet, such interaction ranging is complexity from simple downloading of instructional content to structured interactions that include assessments and assigned certifications (Daniel 2013). Limitations of the Study It was likely that students would have different learning strategies. Students who preferred a strategic learning strategy (Speth, Namur, and Lee 2013) would not depend on creativity for their learning success. They would prefer learning how to answer the examination questions for gaining the best grade instead of acquiring an understanding of the subject matter. Another limitation of the study was the difficulty of drawing causal links between the explanatory

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variables and the outcomes of interest. Although it was likely that creativity influenced persistence and achievement, it was also possible that other factors not measured by this study had a relationship to both the independent variables and the dependent variables. As a result, this study examined relationships rather than causal effects of one variable or set of variables on another variable. Lastly, there was a low response rate, which was around 30%. The students are in different cities and provinces across different islands in Indonesia. Therefore, it was likely that the students would not respond for various reasons. The dissertation consists of five chapters. Chapter II, the literature review, addresses theories concerning creativity and learning strategies. It discusses studies of creativity in the teaching of biology, and it also describes the features of distance learning and online learning at Universitas Terbuka in Indonesia. Chapter III presents the research methodology of this study; it includes the statistical techniques— independent samples t test, multiple regression, and path analysis. Chapter IV presents and describes the empirical findings, and Chapter V discusses the results and draws implications . Chapter VI presents the study’s conclusions..

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CHAPTER II: LITERATURE REVIEW The literature review provides the theoretical background of the study. It begins with the subject of creativity, of which there are said to be four types. This study focuses on the theory of mini-c creativity, also known as everyday creativity. In biology, there have been studies of the components of mini-c creativity, which are idea generation, analogy, integrating different concepts, and evaluation. The review then discusses learning strategy. Both creativity and learning strategy influence student achievement and persistence in online learning. The final discussion in the literature review deals with online learning in the university, including a description of Universitas Terbuka in Indonesia. Theories of Creativity Kaufman and Geghetto (2007) segment the concept of creativity into four variants according to magnitude. In their view, creativity is divided into mini-C, pro-C, little-C and big-C (Figure 2). The study uses the mini-C creativity concept, which considers creativity as an attribute of ordinary people (Beghetto and Kaufman, 2007). The theory of mini-C aligns with the personal creativity notion of Runco (2004). Runco defined creativity as the individual transformation of objective reality into original and useful interpretation. Runco’s definition also aligns with the fluid creativity of Necka, Grohman, and Slabosz (2009). Fluid creativity is a skill that human beings possess. In general, creativity is a skill that everyone uses in daily life. The creative person does not have to be an expert in any field and creativity does not have to be a significant achievement for the community.

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Figure 2. Four-C Model, from Kaufman and Beghetto (2009). There are four different levels of creativity according to Necka, Grohman, and Slabosz (2006). The first level is an intrapersonal level, which is fluid creativity. Fluid creativity lasts for several minutes. The second level is crystallized creativity. This is creativity for solving problems. The next level of creativity is mature creativity. This is the creativity must be novel, original, and valuable. Then, the highest level is eminent creativity, which requires acceptance and recognition. This is the creativity of the highly intelligent people and it is rare. Another theory of ordinary people’s creativity is everyday creativity. According to Runco (2007), creativity is a potential attribute of all humans. The base of creativity is originality and usefulness. All persons have the potential to use creativity in adaptation, problem solving, and learning. Runco’s example of creativity is language. People use languages to express new ideas that that they have never discussed before. Since the expressions are new and useful, those are examples of creativity. One attribute of mini-c creativity is that it is subjective. According to Kaufmann and Beghetto (2007), mini-c creativity is a personal, subjective, internal, mental, or emotional form

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of creativity. The authors elaborated that intrapersonal judgment is the difference between mini-c creativity and other kinds of creativity, such as little-c and big-C. Runco (2004) suggested that personal creativity depends on individual interpretation of what is useful for personal purposes. Creativity Type 1: mini-c Mini-c creativity is important in learning. According to Beghetto and Kaufmann (2007), learners do not only receive the information from others when they learn. Additionally, they transform that information during the learning process in a creative way. The process is influenced by the learners’ experience and existing ideas. Another application of mini-c creativity resulted from a study of Hristovski et al. (2012) in sport science. If an athlete found a new technique, or the athlete adapted an already known technique that addressed personal needs, this would be an example of mini-c creativity. The significance of mini-c creativity is that it is the beginning of other forms of creativity that may change into more obvious forms. Vygotsky (2004) pointed out that a child has an imagination that will develop in later ages into realistic activities, as in the art and sciences. Hence, this imagination needs nurturing and feedback (Kaufman and Beghetto 2009). The mini-c creativity corresponds to the intrapersonal creativity of Necka, Grohman, and Slabosz (2009). Both interpretations of this level of creativity reveal the basic level of creativity. Since mini-c is the basis for of other types of creativity, the mini-c creativity can transform into other types (Kaufman and Beghetto 2007; Necka, Grohman, and Slabosz 2006). According to Kaufman and Beghetto (2007), a person who has a personal understanding of a topic has mini-c creativity. A person may have a personal understanding of nuclear physics. If the person then can understand it with a new and appropriate method, the person moves into little-c. However, when the person can make a significant contribution to the field of nuclear

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physics, then the person is considered to be involved in the big-C creativity. Another example rests in the music discipline and provides a different level of activity. Anyone can be familiar with a certain musical product at a level of mini-c. When the person can learn to play the music, which takes several years, it means an achievement of little-c. An example of the big-C occurs when the person can become a musician making significant contributions to the music world as a musician. Kaufman and Beghetto (2007) also suggested that an early start a move from mini-c into a higher level of creativity, including little-c and big-C is significant for career advancement. On the other hand, the pro-c creativity is a bridge between little-c and big-C creativity. Examples of the pro-C creativity are activities of professional artists. It applies to persons that achieve beyond the little-c, but not yet to big-C. Pearce (2013) used the mini-c theory to study creativity in Canada. Pearce’s study found that autonomy is a factor of creativity. Autonomy is also important in online learning, since students have to decide how and when they will study (Lin 2011; Runco 2007). The change of different types of creativity requires feedback. According to Kaufman and Beghetto (2007), the changing of different types of creativity is important for maximizing the potential of the new idea. Appropriate feedback is necessary for developing mini-c creativity. In addition, ideational code-switching is necessary to connect little-c and mini-c. The ideational code switching is a skill to move between mini-c creativity, that is an intrapersonal interpretation, and little-c, that requires interpersonal judgment. The interaction between a more experienced partner and a novice is necessary. A mini-c novice needs skilled others who can help to understand conventions, standards, and existing knowledge in that particular domain. The more experienced partner gives feedback, when the idea of the novice is rejected by the society. On the other hand, the novice must learn from the expert. The novice needs for feedback from

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the experts in case the community does not understand his or her creativity. Since little-c needs recognition from the community, the novice should improve the creative idea into a more recognizable and useful form for the community. Creativity of Type 2: big-C As opposed to the mini-c, the big-C theory is concerned mostly with the achievement of expert and eminent persons. According to Kaufmann and Beghetto (2009), an example of the big-C theory is Csikszenmihaly’s theory, according to which creativity takes place in a system of person, field, and domain. In the theory, creativity is an idea, which is a person’s contribution. The idea should be validated by experts, who constitute the field. The field gives feedback to the person who contributed the idea. Both the person and the field must be in a similar activity, such as arts, mathematics, physics, or social science. Those activities construct what is called a domain. This organization portrays the formation of the creative ideas of great scientists and artists that shaped events and the course of history (Csikszenmihaly 1994). An example of big-c creativity is Islam, the religion of 88% of the Indonesian population. Creative endeavor has been a part of Muslim society throughout history. Creativity in the religious context within Islam is called ijtihad. Ijtihad requires understanding of basic Islamic principles. Creativity should also follow understanding of the guidance, such as divine principles, usage of lawful means, and serving society (Abd-allah 2006; Al-Karasneh and Saleh 2010). For example, Khaleefa, Erdos, and Ashria (1996) argued that creative arts in several African countries are not rich of sculpture. One reason is that making an object resembling a human is prohibited in Islam. However, the art of poetry are growing in the African countries. Creativity manifests in poetry more easily in Africa as it has not been restricted by religious rule. According to Sawyer (2006), the creative person and the field should have similar understanding

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about the creative ideas. In this case, any creative ideas in Islam should follow the principle of the religion based on the evaluation of the field through the religious scholar. Creativity of Type 3: little-c Another form of creativity is the little-c. As opposed to the big-C theory that explains the creativity of the experts, the little-c domain is more related to the creativity that is seen within the everyday activities of common people. Studies of this approach mainly deal with unconventionality, inquisitiveness, imagination, and freedom rather than analytical skills (Kaufmann and Sternberg 2009). This type of creativity is studied broadly on a worldwide basis (Mouchirod and Lubart, 2006; Simonton 2009). In other words, the little-c theory is descriptive of creativity as a part of daily life. Creativity is not limited to the important persons in history; rather creativity is also an achievement for common people, as long exhibits novelty and usefulness in everyday settings. Creativity of Type 4: Pro-c The pro-c creativity is the type of creativity found in professional communities. Pro-c is more meaningful than the little-c creativity, since people recognize the creative ideas. However, as opposed to big-c, the pro-c creative ideas do not have the high level of recognition as do eminent ideas. Pro-c creativity is manifest, for example, in the instance of the professional chef who creates a new recipe. The creative idea is more recognized than what he makes at home, but his idea is only recognized within his working environment (Kaufmann and Sternberg 2009). All four kinds of creativity are interconnected. Kaufmann and Beghetto (2007) stated that all creativities are rooted in mini-c. With adequate support, novices can expand their initial minic creativity into more meaningful creativity, such as little-c or big-c later in time. When moving into a higher level of creativity, learners need to perform ideational-code switching. This means

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that learners need the capability to move between intrapersonal creative interpretations, which are the characteristic of mini-c, into interpersonal creative interpretation. In other words, the learners move from mini-c into little-c creativity. Creativity in Education In this study, the definition of creativity is based on the everyday creativity of ordinary people. The theory of Beghetto and Kaufman (2007) about creativity fits the research purpose. They described a definition of creativity, which was the personal interpretation of new and useful experiences, actions, and events, which they named mini-c creativity. The creativity of online biology students is an example of the mini-c creativity. They develop new ideas to solve their learning problems in understanding biology. Creativity is important in online learning (Mintu-Wimsatt 2007). As online learning does not have regular meetings between student and teacher, students need to have specific skills in order to study and overcome their problems (Pearce 2013). Creativity is one of the abilities that enable students to find out new ways of learning in the unique learning environment. A framework for applying creativity in education was suggested by Lin (2011). Lin used Beghetto and Kaufman’s (2007) definition of mini-c creativity in this framework. Creative pedagogy is a practice that enhances creative development in the interplay of three elements: creative teaching, teaching for creativity, and creative learning. Creative teaching and teaching for creativity are interconnected. Creative teaching focuses on making learning more interesting and effective through innovation. Teaching for creativity focuses on the identification, encouragement, and provision of support for creativity. The third element, which is creative learning, is a context where student learning occurs in situations where students learn with

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playfulness, imagination, and collaboration (Lin 2011). Line believed these elements to be important for students developing their creative skills. Creativity influences learning by allowing the learner to achieve better results. Lones (1999) discussed a model of creativity that relates creativity and learning. The model is based on Csikszenmihalyi’s theory of creativity. In the theory, creativity is a result of an interaction among individual, field, and domain. The individual is the one who has a new idea. The field is a group of experts in a certain activity involving the individual. Meanwhile, the domain is the activity in which the individual is taking part. Lones described creativity as a change in a person’s behavior in a manner that the environment recognizes and allows the person to do better (Lones 1999). The change of behavior is a result of learning. Therefore, creativity is a significant skill needed in order for student to learn better. Creativity supports collaborative efforts among the students. Moran and John-Steiner (2003) pointed out that creativity has a social dimension. Students develop creativity while learning in social activities. Another benefit to improving creative thinking among students in online learning is to enhance collaboration and social constructivism (Lin 2011). Hence, online learning that fosters the collaboration of the students is a medium where students can develop their creativity. Creativity is important for the students’ career development. Fasko (2001) used an idea of Guilford (1950) to the effect that learning is a transformation of knowledge. The transformation can take place in many field of activities. Moreover, students need to be creative not only when they study, but also when they graduate and face the challenges of the environment. Creativity is a part of everyday life, as it facilitates problem solving and adaptation (Runco 2004).

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De Haan (2009) suggested the notion of training for creativity. De Haan quoted the previous studies of Scott et al. (2004), who argued that creativity can increase through training. Scott (2004) undertook a meta-analysis of 70 papers on creativity training in different fields. The Scott study on divergent thinking (fluency, flexibility, and elaboration) focused on problem solving, performance,. His results showed that divergent thinking and problem solving were the traits most affected by training.. Some practices like case study, problem solving, and group work lead to increase students’ creativity. Divergent thinking is one focus of creativity training. Therefore, many creativity tests focus on flexibility, originality, fluency, and elaboration, all of which are believed to be components of divergent thinking (Scott 2004). Studies were performed in Indonesia relating to the efficacy of creativity training among biology students. Utami (2010) used the Value Clarification Technique to improve the creativity of female 10th grade high school students studying biology. There was an increase in creativity score in the students’ biology test score. Widhiyantoro, Indrowati, and Probosari (2012) showed that the creativity of high school students can increase through the Guided Discovery method. In the method, teachers guided students to find the answers. Teachers gave the problem, provided guidance, and guided students throughout the learning process. The students found the answer by relating various concepts under supervision of the teachers. The participants were 10th grade students. The result showed that there was an increase of creativity most notably in elaboration, but also in flexibility, fluency, and originality.

There was also a study in Indonesia that

employed the “Science, Technology and Society” (Yager et al. 2009) approach to develop creativity among biology students (Smarabawa 2013). In that STS method , students learned biology through discussion of actual problems within society. At first, the teacher introduced the problem to the students. Then, students discussed the problem in groups. The students had to

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discover possible solutions for the problems. The research method was a quasi-experimental study. The result was that 11th grade vocational high school students using the method showed higher creativity and biology scores compared to a control group. All of the studies, however, were conducted in a classroom setting. Creativity is important for students in online learning. As online learning does not have regular meetings between student and teacher, students need to have specific skills in order to study (Pearce 2013; Mintu-Wimsatt, Sadler, and Ingram 2007). Pearce explained the role of creativity when the students overcome their problems, for example doing assignments or making choices. They made trial and error and made difficult decisions to solve the problem The online learning in the Universitas Terbuka requires students to be more independent due to the geographical barrier in the archipelago, limited infrastructure, and individual job and family responsibilities. Creativity is one of the skills that enable students to find out new ways of learning in this unique setting (Fischer and Sugimoto 2005; Edmonson 2011). Mishra et al. (2013) and Muirhead (2007) described that online learning exposes students to an abundance of choices of information. The challenge of the students is now to analyze, integrate, and redefine the information, even information from across disciplines. A previous study at Universitas Terbuka (UT) showed that creativity is an important skill that students acquire from their learning experience in the distance learning environment, including online activities. Ratnaningsih (2013) studied 2,417 graduates and stakeholders of the university. The study showed that the graduates considered creativity as one soft skill that students need during their study in the university along with time management, problem solving, and team work. The study showed that the stakeholders of UT also regard creativity as an

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important skill for UT graduates. Despite the fact that there is no specific preparation for creativity training, creativity is an important attribute of student initiative. Student Difficulty in Learning Biology Students have difficulty in learning biology. According to Nugraini (2013), high school students in Indonesia feel that abstract concepts in biology are difficult to learn. The difficulty is caused by misconception, interrelation of various subjects, and the nature of the topic (Nugraini 2013). For example, students do not understand the structure of chromosomes in a cell nucleus and their role in genetic activities (Cimer 2012; Kubika-Sebitosi 2007). It is hard for the student to learn it, since they do not have acquired the necessary background knowledge of the subject. Biology includes some topics that are more complex and difficult to master than others. According to Cimer (2012), the five most challenging topics are matter cycles, endocrine system and hormone, aerobic respiration, cell division, and genetics. Cimer studied 177 students at a secondary school in Turkey. Several reasons were also found for the learning difficulty. Cimer (2012) explained that the nature of the topic, teachers’ style of teaching, students’ learning habits, students’ negative feelings and attitudes towards the topic and a lack of instructional resources contributed to the students’ difficulties with the subject matter. Any improvement that connects to learning habits, teaching styles, and attitudes will help students learn biology. Moreover, it is possible that an improvement in these areas can alleviate the difficulties caused by the nature of the topics and lack of resources. Misconception may also play a role when students cannot connect different topics of biology. Accordingly, Kubika-Sebitosi (2007) conducted an investigation with secondary school students in two provinces in South Africa about their understanding of genetic concepts. The finding was that students did not have clear idea about the role of the gene in a cell of an

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organism. They did not have a coherent conceptual framework regarding a cell or genetics. Based on the finding, students could not find any correlation between the structure of cell, including the chromosome and DNA as the part of the cell structure that contain the gene itself, with the genetic traits of any organism. Another contributing factor to student difficulty in learning biology is the teaching method. Oztap, Ozay, and Oztap (2003) performed a survey of biology teachers at secondary schools in Turkey. The survey showed that the teachers recognized difficulties in teaching certain topics in biology, such as cell division. Hambooaka (2007) found that several factors are the causes of the students’ difficulty in learning biology. Those factors include the inability of teachers to explain the topics, the students’ belief that biology is difficult to learn, and the lack of adequate learning aids. As a result, Hambooaka concluded that the method of teaching biology should address those factors. Creativity in Learning Biology In my study, the focus is on the creativity skills exercised by students in learning biology in an online, distance education setting. Studies have shown that creativity consists of idea generation, analogy, integrating different concepts, and evaluation (Dunbar 1997; De Han 2009; Mumford et al. 2010; Lawson 2001). Beghetto and Kaufman (2007) elaborated on these defining elements of creativity. Idea Generation Idea generation is one of the critical components of creativity in biology. According to Mumford et al. (2010), idea generation is more important for biology students compared to students of other disciplines. In biology, where there are well-integrated and well-defined

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concepts like DNA, ecology, and energy cycle, creativity is more about gaining information or making an evaluation about a certain object. A creative process leads to a new image or action, one never before seen or experienced (Vygotsky 2004). Ideal generation is, therefore, an important part of creativity. Analogy Analogy is a learning strategy that uses creativity. According to Runco and Chand (1995), analogy is using knowledge in new ways. In biology, the process of analogy is to make a connection between two things—the base and the target. A target is a concept that a learner is studying. The base is a prior concept that is already familiar. In the analogy process, the learner uses the base as an example for elaborating on the target (Dunba 1997). The concept was supported by Runco (2007), who suggested that a new discovery may be a result of a different interpretation of previous findings. An example of using analogy can be found in molecular biology. Researchers find the function of a new gene by comparing it with another gene whose functions are already known. Based on the DNA structure of the new gene, researchers can can search other genes with similar DNA molecules. Therefore, they can make confirmable predictions about the functions of the new gene (Dunbar 1997). Integration of Different Ideas The integration of different ideas (Kohn, Paulus, and Korde 2001) is a creative technique in biology. McCabe (2011) conducted research regarding the use of analogy in a study about biology. The study used visual learning support in a microbiology course for undergraduate students in Australia. The study showed that students who have better skills in finding a connection between two different ideas have a better understanding of the topic. De Haan (2009)

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suggested that students need to integrate materials across different subjects. The integration process uses divergent and convergent thinking as parts of creativity. As a result, creative students can engage in different ideas and perspectives. Maas (2009) discovered that one creative skill in biology was finding a link to different research findings. The example was when he found a relationship between two experiments in bacterial genetics. A creative person can find a relationship, unlike the other persons who are less creative. Another example, the study of Kibuka-Sebitosi (2009) showed that some students cannot relate the structure of a chromosome within a cell and the DNA as a molecular structure of the gene. The skill in integrating two different ideas like the structure of chromosome and the structure of DNA will help students to learn the topic. Evaluation Another creative technique is evaluation. Lawson (2001) studied how biology students evaluate their own ideas. Lawson’s idea is in line with Runco and Chand (1995). The students were to test their own ideas by several different alternatives and their various implications. For example, students in a genetics course performed a calculation of the Mendelian Law in genetics. After that, they evaluated their ideas with various possibilities that could result. The post-test showed that students’ thinking skills increased after the study. While the research of Dunbar (1997) showed the importance of creativity in biological research, creativity needs to be taught to the students. Students’ creativity can increase, if they have proper training. Lawson (2001) studied how to design a curriculum to improve student creative thinking. The author used a model of connecting different planes of thought. Analogical reasoning was the method of connecting different thoughts. There were three phases of thinking:

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preparation, incubation, and the illumination. The author used the principle of analogy to explain the Mendelian Law of genetics. In the preparation phase, according to Lawson’s theory, a person uses both conscious and unconscious thought. The author suggested a model for creative thinking. In the unconscious phase, a person has two planes of thought. Only one plane has a connection to the target. Therefore, there must be a link between both planes of thought. Both planes of thinking must be active so that the person can consciously see the relation of both planes of thinking and find out a solution. The conscious phase is the illumination phase. In this phase, the person does verification. It is a process of making sure that all steps in the process are correct (i.e., the idea becomes a real research question). The Lawson’s hypothesis explains the possible solution for the process. The Lawson’s model of thought pointed out the role of connecting planes of thought as a significant point in creative thinking. The connection is brought about by analogy, which is also suggested by Dunbar (1997) and Runco (2007). The analogy is an application of an old idea being transformed into a new application in a different field to produce a new breakthrough. Such analogy is called combinatorial thinking, analogical reasoning, or analogical transfer (Lawson 2001). The processes of analogy and building hypotheses are parts of the evaluation by Lawson (2001). For example, Lawson used the analogy and hypothesis model to explain the theory of natural selection and the Mendelian theory. Students had to formulate the hypothesis of natural selection from Darwin. For Mendelian theory, students had to develop the hypothesis based on experiments using dice to represent possibilities of color variation in corn kernels. The hypothesis is based on kernel color distribution. Students learned the concepts of dominant,

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recessive, allele, phenotype, and genotype, which were components of the theory of natural selection. Although the experiment did not measure creativity per se, the experiment showed that the students’ thinking skills improved. The cycle of creative activities is a part of inquiry in biology (Maas 2003; Wolpert 1996). Maas emphasized the importance of having enough information about the topic, recognizing the problem, and breaking away from conventional view. For example, a learner might begin by asking a new research question, which would require careful problem formulation so that an accurate question was being posed about a real problem. In other words, choosing a problem is as important as solving a problem. Perspectives of Creativity in Learning Biology Research by Dunbar (1997), DeHaan (2009), Lawson (2001), and Mumford et al. (2010) opined that creativity is necessary for learning biology. Students’ creative skills, like the skill to organize information across different subjects being learned, are vital. Organizing information across different objects is also important. In addition, students need the skill to develop ideas and challenge them from different viewpoints (Kozbelt, Beghetto, and Runco 2010). Mumford’s (2012) and Dunbar’s (2001) ideas about creativity had different foci. Mumford (2012) focused on the cycle of idea generation and the resulting feedback from the successive steps of the cycle. This may help students who have difficulty in understanding complex concepts of biology. However, the idea does not explain how the students understand the relation of one concept to another. Meanwhile, Dunbar (2001) focused on relating one topic to other topics. This may help students discover the relationship of one phenomenon to the broader context in which it exists.

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Despite the differences, both Mumford (2012) and Dunbar (2001) concurred on one point, that there is a connection between problem solving and analogy. Mumford (2012) suggested that creativity for solving problem requires a person to have previous knowledge about a subject. The knowledge about the subject provides a basis for organizing new information. Understanding the new information requires and reorganization results in a new and unique idea. If a person can recombine the new information with the broader knowledge that the person has in mind, there will be a broader and deeper process of recombination. In other words, the person may connect a subject with other subjects that seem unrelated, is indicated by Dunbar (2001). According to Dunbar, the skills of analogy and relating with different subjects are part of a creative process. Both processes help to develop a more creative process for solving problems. The Use of Online Learning in Higher Education Students can foster creativity through online learning. Ziegler and Diehl (2009) suggested that the use of a computer network allows students to contribute ideas in parallel and improves their motivation through social competition. Students in online courses can share their ideas regardless of the other students’ contribution. Even if other students contribute different ideas, the contribution of different ideas stimulates the idea generation of each student. According to Runco (2006), this feature of online learning allows students to develop their creativity. Online learning allows students to apply creativity. For example, the Web 2.0 technology is a media whereby students may contribute to produce content. While maximizing the students’ innovation possibilities, the online media also allows students to be independent in deciding what they learn. Learning strategies are also relevant to online learning. Students can use the deeplearning strategy, which is learning to understand the content of the learning material, in online learning. Online learning offers the opportunity to learn by understanding the relationships

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among different topics. The current study of the effect of creativity and learning strategy on student achievement and persistence has provided information about improving student success. Online learning is a growing field that allows learners to experience a more flexible learning environment. Online learning is available regardless of geographical barriers and job responsibilities (Daniel 1996), making it more accessible for the working adult. The use of the internet allows the student to gain access to virtually unlimited knowledge. Allen and Seaman (2008) described online learning as a learning program that delivers 80% of its content online. Certain features of online learning—computer supported collaborative learning, learnergenerated online courses, improvement of critical thinking, self-directed investigation, Web 2.0, and Facebook—engender creativity (Allen and Seaman 2008). The computer-supported collaborative learning (CSCL) concept support creativity in student behavior. Goodyear, Jones, and Thompson (2014) described CSCL as a learning activity where information technology plays significant role in the collaboration. CSCL may be a synchronous situation, where participants work at the same time, or asynchronous, where participants work at different times from each other. CSCL can also be either face-to-face, online, or in a blended mode. Since the CSCL allows participants to provide and receive feedback, it can foster the mini-c creativity, according to Beghetto and Kaufman (2007). The use of online learning allows biology students to develop creativity. Shen, Lei, Chang, and Namdar (2014) used technology-enhanced, modeling-based instruction (TMBI) in science. This method encourages students to explain scientific phenomena by using, creating, sharing, and evaluating models. The models may include physical model, computerized visualizations, graphs, and mathematical formulae. One example of TMBI is PhET Interactive Simulation from the University of Colorado, Boulder. Students can use it for visualization and testing scientific

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models. Another example of TMBI is River City from the Harvard Graduate School of Education. River City is a virtual town where students can make experiments, develop hypotheses, and test the hypothesis in the virtual model. Students may work in groups of two or four. Both of these projects have shown positive results. Students had an increase of inquiry learning after using the River City. Both PhET and River City are useful for biology students. Nugraini (2013) and Widhowati (2013) found out that most methods of teaching biology in Indonesia involve memorization of facts. Therefore, the use of online learning to create virtual experiments followed by the development and testing of a hypothesis is a technique that leads directly to creativity in learning biology. Another application of online learning for fostering creativity is scientific experimentation. Online learning can support learning how to conduct self-directed investigation. Lazonder (2014) suggested several such applications, including scaffolding for hypothesis generation, experimentation, and evidence evaluation. These activities have a connection to the ideas of Lawson (2001) and Wopert (1996) about inquiry in biology. One limitation in the use of online activities to learn through inquiry is that students must have self-regulatory skills, as they need to design, monitor, and evaluate their own research. The future of online learning includes Web 2.0. Features of Web 2.0 are blog, wikis, YouTube, Facebook, and Twitter. Despite the widespread use of online learning, including the Web 2.0 technology, Hsu, Ching, and Grabowski (2014) found that there is less research on constructing meaning. Students prefer to use the wiki as a place to present their ideas, rather than use it to give feedback on others ideas or revise their own. They use private communication like email to share their ideas and revise them prior to uploading them into the wikis

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As one of the most popular social media worldwide, Facebook is also a medium for creativity training. Alias et al. (2013) created such an application. They used Facebook to study creativity among students in secondary education in Malaysia. The authors used TTCT (Torrance Test of Creative Thinking) to measure the creativity. The result was that the difference in the means of pre-test and post-test scores in creativity was 27.50 in the TTCT test. The mean difference of the treatment group and control group were 4.90 for writing creativity and 5.68 in problem solving. There was an increase of different aspects of creativity in the study. Another application of Facebook for creativity training was undertaken by Sulaiman (2013), who compared creativity of college students in a physics course in Malaysia. The experimental group was exposed to online problem-based learning (PBL) activities, but the control group was not. The measurement was taken using TTCT on fluency, flexibility, originality, and elaboration. There were significant increases in flexibility, originality, and elaboration, but not in fluency. The use of Facebook and other Web 2.0 concepts for online learning points to the importance of the design process. Instructional design for online learning should include the concept of Web 2.0. Goodyear, Jones, and Thompson (2014) elaborated principles of Web 2.0 in the design. For example, Web 2.0 blurs the distinction between synchronous and asynchronous activities. The content is more user-generated, and it uses more forms of different media. Learners have more opportunities to convey ideas during learning activities. Therefore, the design of online learning with the Web 2.0 technology provides better support for creative ideas. Biology education in Indonesia also uses online technology. Nugraini (2013) studied the teaching of biology among senior high school students in a city in Indonesia. The study showed that the use of web–based training in biology increases the students’ acceptance of knowledge

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and the interests for both high-achievers and low achievers. However, the experiment with online learning used only online video in a classroom setting. The Universitas Terbuka uses online learning in a distance learning setting across the country, unlike Nugraini’s experiment. Tapilouw and Juanda (2009) conducted a study on the use of interactive multimedia for biology students of the UT. The topic was Heredity Substance and Protein Synthesis. The result showed that students in the experimental group achieved higher results in understanding the content compared to the control group. The Role of Learning Strategies Learning strategy affects student creativity. For example, students who use deep learning want to understand the material being learned. They engage in deep learning by connecting old ideas with new ideas. In this respect, they find a new understanding of the material (Shih and Gamon 2002). This idea connect with Runco’s (2006) definition about creativity. Runco’s definition of creativity is that a learner has ability to transform a new knowledge so the learner can understand the new knowledge better. Creative students are likely to use the deep learnig strategy. It means that deep learning strategy supports student creativity. Learning strategies reflect different approaches taken by students to learn a subject. Martin and Saljo (1976) posited that there are inter-individual differences in types of learning. The researchers conducted a study on how students reading an academic paper. They found that there were two levels of learning. The first level is surface level, which means that students learn only the text. This level is the same as rote learning. In the deep level, students learn is the content in the text. The outcomes of learning are different for both cases. Therefore, a measurement of how students learn is useful in order to observe the differences in students’

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learning process. The measurement can give the teachers information about what weakness the students have in achieving their learning goals. There are several studies of the students’ learning strategies in online learning. Shih and Gamon (2002) explained the need to investigate how students learn through new technologies in order to improve curriculum and instructional design. The investigation of Shih and Gamon (2002) of learners in two online biology courses showed that students in online courses use three learning strategies: finding out the most important ideas from a lecture, memorizing key words, and relating new ideas to old ideas. The learning strategies were important factors in the students’ success, as there was a significant correlation between the learning strategies and the student’s achievement. Tomanek and Montplaisir (2004) described different learning strategies of biology students, especially in learning cell division. The strategies were deep learning, surface learning, and strategic learning. A feature of the deep approach is that students are motivated to understand the content. Students want to master the learning content. They connect new ideas with previous knowledge and they connect evidence with conclusions. They elaborated logically. The deep learning approach is more conducive to creativity. The connection of new ideas with previous knowledge is an example of mini-c creativity, according to Beghetto and Kaufman (2009), DeHaan (2009), Kibuka-Sebitosi (2009), and McCabe (2011). According to Entwistle (2000), deep-learning strategy includes finding out possible alternatives for reaching conclusions. Arend (2006) found out that some students’ strategies were metacognitive self-regulation and critical thinking. As described by Beghetto and Kaufman (2007) concerning mini-c creativity, such strategies of learning focused on finding new ideas for developing concepts with which a student is already familiar..

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There is a connection between deep approach learning strategy and Lawson’s (2001) analogical reasoning. According to Lawson, biology students need to connect different subjects in order to understand a certain topic. Biology includes several different, but interconnected subjects (Tekkaya 2002). Therefore, learning a subject may require understanding of other subjects and linking those different subjects. Other approaches are not suitable for learning biology. The surface approach implies that students only intend to complete task requirements. They do not care about strategy or the purpose of learning. They cannot deduce principles from evidence. They cannot explain anything. They only memorize the content for examination. For example, most biology students in Indonesia learn biology by memorization (Nugraini 2013; Widhowati 2013). As a result, they are less motivated and have difficulty learning biology. Students using a strategic approach want to get a high grade, but have little interest in understanding the content even though they may work diligently and efficiently. The Tomanek and Montplaisir (2006) study showed that most students were using surface and strategic learning and were, therefore, finding it hard to learn biology. For example, most learning activities that students did were reading activities, answering old test material, or highlighting notes with markers. Those were examples of surface and strategic learning, as those activities did not require connecting different subjects and elaborating arguments logically, as in deep learning. There was, however, one student who engaged in a deep learning strategy. Distinct from techniques used by other students, this particular student made flashcards for the subjects that professors had explained. He also explained this same content to a friend, who would ask him questions. This means that he had to understand the concept in order to be able to answer the question from his friend, which is a deep learning activity. Perhaps not surprisingly, he received the best grade in his class.

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This result showed that students who do not use the deep-learning strategy do not clearly understand the content. The students listened to the instructors during the lecture. However, they did not pay attention to the cues for the upcoming examinations that the instructors delivered. Students needed to be able connect different ideas that the instructors delivered with what they derived from other resources. Universitas Terbuka (UT) The setting for this study of creativity and learning strategy in biology was Universitas Terbuka in Indonesia. The study provided new input about student creativity within online learning and distance education. Under such conditions, learning strategies are important factors, since the students have less opportunity to meet instructors and other students in person. Providing quality higher education is a huge challenge for Indonesia. The country is the largest archipelago with 17,000 islands. The country has a population of 220 million, which is the fourth largest population in the world (after China, India, and the United States). The population has high diversity. Although the country has Bahasa Indonesia as its official language, there are more than 700 local languages spoken the islands. The nominal per capita gross domestic product (GDP) is $ 3,499 and the Gini index is 35.6, while the human development index (HDI) is 0.629 (IMF 2013; The World Bank 2011). The diversity may become a source of creativity, since there are different races, ethnicities, and income levels (Wang, Fussle, and Cosley 2011). Distance education is a significant component of the Indonesian higher education system. Distance education allows students to study despite geographical barriers, family and job responsibilities, and age (Zuhairi et al. 2009; Luschei, Dimyati, and Padmo 2008, Holmberg 2005, Moore and Kearsley 2012). The establishment of distance education system in Indonesia

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maximizes the coverage of education for those who live in remote areas or isolated islands, those who have limited funding, and those who have little opportunity to attend physically a regular face-to-face higher education university (Belawati 2009). Universitas Terbuka (UT) is the primary distance education institution in Indonesia. It was established in 1984 to accommodate graduates of secondary schools who could not attend other universities because of geographical barriers, jobs, or family obligations (Daniel 1996). The university has a central role in preparing future teachers in Indonesia. This is the only university using entirely distance education system in Indonesia (Belawati 2009). The university has four faculties, which are the Faculty of Mathematics and Natural Sciences, the Faculty of Social and Political Science, the Faculty of Economics, and the Faculty of Educational Sciences and Teacher Training. There are 35 bachelor degree program and four master’s level programs. The master’s level programs are in management, public administration, fisheries management, and mathematics education. In order to facilitate student services, there are 34 regional offices throughout the country (Belawati 2009). There are also an increasing number of Indonesian nationals who take courses while residing abroad, for instance in Malaysia, Hong Kong, Taiwan, and Saudi Arabia (Belawati 2009). The university uses various modes of learning activities. There are 962 printed textbooks (including 30 with multimedia supplemental material), 117 radio tutorial programs, 419 online tutorials, and 1,002 televised tutorial programs. Certain programs, including biology, have laboratory practices conducted in partner universities. A proof of the strength of distance education is its contribution to Indonesian national education. In 2007, 2.7 million classroom teachers graduated from the university (Luschei, Dimyati, and Padmo 2008). In 2010, 11.58% of Indonesian university students enrolled in

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distance education institutions (Moeliohardjo 2010). In addition, 20% of Indonesian primary school teachers had two-year degrees from the UT (Luschei, Dimyati, and Padmo 2008). As a part of distance education, online learning is important for improving learning quality in Indonesia and for providing more educational opportunity. The use of online learning at UT began in 1997 (Luschei, Dimyati, and Padmo 2008). The internet has provided more access for students to information. The internet has enabled the students to communicate more quickly and more easily with instructors and other students. Another benefit of online learning has been that students can join collaborative learning activities with students from other countries (Boltuc 2008). For example, Indonesian students can learn together with students from different countries through the online learning. They can undertake such activities while they are still resident in their home country. The application of online learning at UT is increasingly more important. UT has a pioneering role in the use of the internet for education in Indonesia, since some segments of students still have limited access to internet facilities (Belawati 2009). According to Kozma and Vota (2014), this is an example of using information and communication technology to reform the educational sector. Therefore, the experience of UT in online learning comprises increasing the coverage of learning as well as improving the quality of service. The online learning at the department of biology consists of online tutorial and interactive computer simulations. There are 33 courses in the UT’s biology curriculum that use online tutorials. Meanwhile, there are three courses that use interactive computer simulation. The online tutorial has an online discussion forum where students can communicate with a tutor and other students (Hewindati and Zuhairi 2009). According to Luschei (2014), the online technology may be more effective for providing learning support rather than for delivering content. The

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application of online tutorials at UT also supports the students’ independent learning (Belawati 2009). BIOL 4477 General Biology is an introductory course for all students of the Faculty of Mathematics and Natural Sciences. Students who take the course are from the departments of mathematics, statistics, agricultural extension, and environmental studies, as well as biology. On average, 300 students take this course every semester. This study also includes students in the biology teacher training program of the Faculty of Educational Studies and Teacher Training. They are teachers of secondary school throughout Indonesia who are taking a bachelor degree. The introductory biology course for them is PEBI4101 Introductory Biology. Summary of the Literature Review The literature review has discussed mini-c creativity and the different components of creativity in biology. It has discussed creativity as indirectly related to student achievement and persistence through learning strategy. Students employing deep learning strategy are more likely to succeed. The review included the application of online and distance learning at Universitas Terbuka in Indonesia. Previous studies have shown that creativity is important for learning (Guilford 1950). Creativity means transforming knowledge during learning process (Beghetto and Kaufman, 2007; Runco 1996). Creativity is also important for learning biology (Richvana, Dwiastuti, and Prasetyo 2012; Widhiyantoro, Indrowati, and Probosari 2012). There are few studies of creativity in online learning and virtually none for biology students in online learning. Since UT uses online learning as a part of its distance education, the

38

study of the role of creativity in relation to students’ learning outcomes and persistence is of major intellectual and social importance.

39

CHAPTER III: METHODOLOGY As suggested by Richvana, Dwiastuti, and Prasetyo (2012), creativity improves learning achievement in biology of high school students in Indonesia. Creativity may improve students’ persistence, which was shown by Nugraini, Choo, Hin, and Hoon (2013). In addition, a study by Utami, Noviar, and Agustina (2010) suggested that creativity training improve madrasah students’ motivation to learn biology in Indonesia. Therefore, there is a need to conduct a study of students’ creativity in online learning in Indonesia. The purpose of this study was to measure the effect of creativity on learning strategy, achievement, and persistence. The hypothesis for the study was that if the students had higher creativity, they would produce higher test scores and maintain higher completion rates. Based on the hypothesis, the researcher developed four principal research questions: The method used to examine the research questions included other control variables, which were age, gender, departments, daily online activities, income, ethnicities, and high school GPA. RQ1. What is the relationship between attitudes of creativity and persistence? RQ2. For those who persist (i.e., those who complete the final examination), what is the relationship between attitudes of creativity and achievement? RQ3. To what extent is attitudes of creativity related to student persistence through learning strategies? RQ4. For those who persist (i.e., those who complete the final examination), to what extent is attitudes of creativity related to student achievement through learning strategies? This study used a quantitative methodology to discover the relationship between learning strategy, attitudes of creativity, examination score, and persistence. For example, the analysis of RQ1 used independent samples t test (Field 2013). Meanwhile, the analysis of RQ2 used

40

multiple regression to analyze the relationship between independent variables and a dependent variable. The analysis of RQ3 used multiple regression while the analysis of RQ4 used path analysis to find out if there was indirect relationship between creativity and persistence or between creativity and learning outcomes involving learning strategies. Research Participants Two groups of students in different programs participated in the study. The first group of participants was comprised of students in the Department of Biology, Faculty of Science, Universitas Terbuka (UT) in Indonesia. There were 286 students in this group. They were from different departments, including mathematics, statistics, food technology, agribusiness, and biology. They were students who were taking a distance-learning program. A component of the distance learning was online tutorial, which was the online activity of this study. The range of their ages was 18 to 30 years old. Their ethnicities were mostly Javanese, Sundanese, and Jakarta. Javanese are the largest ethnic group in Indonesia, comprising almost one third of the national population. Sundanese are the second largest, numbering 15% of the population. Jakarta is the ethnicity of local people in the capital city. Most of the participants were employed. Another group of students was made up of 34 students from the Department of Biology, Faculty of Teacher Training and Educational Studies, Universitas Terbuka. They were pursuing bachelor degrees in education. The total number of students in the group was 40. They lived throughout Indonesia. Their ethnicities were mostly Javanese, Sundanese, Jakarta, Melayu, Ambon, and Makasar. Their ages ranged between 25 and 40. They were teachers at secondary schools who were taking a teacher upgrade course to attain bachelor degrees in education, and they worked all throughout Indonesia.

41

To recruit students, the researcher distributed a questionnaire and consent form via e-mail to the students. The researcher sent the questionnaire nine times. After five times of sending the questionnaire, the researcher sent a reminder by telephone to those who had not replied. Eventually, 102 students replied. All data arrived by January 2015. Data Collection Procedures At first, the researcher requested the list of students from the university administration. Then, the researcher sent announcements to the students about the study and requested their participation. The content of the announcement presented the aim of the study, the benefit of the study for the university and its students, and a brief explanation of the study’s purpose and nature. An electronic survey was sent to participants in the early fall of 2014. The survey consisted of rCAB test, and Approaches and Study Skills Inventory for Students (ASSIST) TEST. These tests were translated from English into Bahasa Indonesia, the official language of Indonesia and one of the most widely spoken languages in the world. All students in both groups of biology students were given the survey, responses to which arrived approximately one month after having been delivered. The participants’ responses came approximately a month after the delivery. The collection of examination score data from the university started at the end of the fall 2014 semester. The researcher requested the data from the administration of the Universitas Terbuka. The researcher collected data about the number of students who did not take the final examination. The data from students who did not take the final examination would be used to measure the level of student persistence. Those who did not take the final examination were

42

students who failed, while students who took the examination were those with high level of persistence. The study also requested the students’ high school GPA, which allowed examination as to whether there was an influence of students’ high school GPA on their achievement score at the college. Another purpose of collecting students’ high school GPAs was to control for the effect of these high school GPAs, a proxy of ability, toward achievement and persistence at the college level. Income level was required to control for the socio-economic status. Protection of Human Subjects There were two procedures required by IRB prior to the delivery of the questionnaire. The first was the CGU IRB. In addition, there was an IRB process at the Universitas Terbuka. The application to the CGU IRB preceded the application to the IRB of UT. Both CGU and UT IRBs approved this study. The study was voluntary, which meant that the students could decide without penalty (e.g., affect their grades, would be placed at risk for any reason) whether to participate. Participants would know the purpose of the study. As the study was voluntary, they might withdraw from the study at any time. Before conducting the survey, the researcher provided participants with informed consent forms that included the purpose of the study, its risks, its potential benefits, and alternatives to the research. The information would help students to decide whether they would join the There were few social risks in the study. In order to minimize potential social risk, the researcher maintained confidentiality of the data and involvement of the participants in the study. The researcher was responsible for the privacy and confidentiality of the research data. The study would produce generalizable knowledge for biology students in UT. Nevertheless, the researcher

43

provided information, assured voluntary participation, and maintained privacy to make sure that any possibilities of risks were low. The study was based on equality of all. This study was reflective of distribution of different groups in the community regarding gender, age, and socio-economic status. Recruitment reflects the diversity of the population that may benefit from the knowledge generated from the study. The application of IRB to UT ensured that the study was sensitive to the local context, since the study was conducted in Indonesia. The IRB at UT evaluated the translation of the questionnaire. They considered the content of the questionnaire with respect to the local culture. Instrumentation Creativity Measurement The instruments were rCAB (Gravina 2013) to measure students’ attitudes of creativity in biology and a questionnaire from Speth, Namur, and Lee (2007) to measure learning strategy. The creativity questionnaire was used since it covered creativity components in an educational setting. The questionnaire measured individual interpretation of creativity according to (Beghetto and Kaufman, 2007). The questionnaire of Speth, Namur, and Lee (2007) measured the students’ learning strategy according to ASSIST (Entwistle, Tait, and McCune 2000). This study used Attitudes and Values from rCAB (Runco Creativity Assessment Battery) in Gravina (2013) to measure attitudes of creativity. There are two scales of the test, which are the indicative scale and contraindicative scale. Indicative scales are attitudes and values that support creativity. The contraindicative scales are attitudes and values that do not support creativity. The test has 15 indicative items and 10 contraindicative items. The scales are the independent variables for study. The instrument used in this study was a Likert-scale questionnaire. Every

44

participant had four choices of response to each prompt, which were 1=strongly disagree, 2=disagree, 3= agree, and 4 = strongly agree. (See Appendix B for the test material of rCAB).

Learning Strategies Measurement One method of assessing student learning strategies is the Approaches and Study Skills Inventory for Students (ASSIST). The Approaches and Study Skills Inventory for Students (ASSIST) assess students’ approaches to study (Marton and Saljo 1976). ASSIST derived from earlier Approaches to Study Inventory - ASI (Entwistle, Tait, and McCune 2000; Entwistle and Tait 1996). Although Martin and Saljo (1976) described two learning strategies, Entwistle, Tait, and McCune (2000) described three learning approaches: deep, surface, and strategic. According to Hughes and Peiris (2006) and Entwistle (2000), deep learning takes place when students learn the content of the material to be learned; surface learning occurs when students learn by reproducing knowledge; and strategic learning occurs when a student does what is necessary to achieve the best examination score possible. According to Entwistle, Tait, and McCune (2000), the surface approach is found among students with higher risk of failure. A benefit of ASSIST is that it can identify students’ approaches toward learning. According to Tait and Entwistle (1996), ASSIST may help students by identifying the weak learning strategies in which they are engaged. Similarly, Webster (2002) suggested that the benefit of the test for the students is that they can become aware of their learning approach, which affects their learning success. Therefore, the measurement is beneficial for both students and instructors. The ASSIST can help educational administrators develop better online pedagogy. Thang (2005) and Gadelrab (2011) found that there is a negative correlation between surface learning

45

and performance. They explained that the findings were caused by excessive memorization in the teaching method. Therefore, the online learning method should focus on understanding the subject matter. The result of ASSIST testing shows which students have adopted a deep learning, strategic learning, or surface learning approach. ASSIST is a self-report questionnaire test. The study used a version of ASSIST that was used by Speth et al. (2007). The ASSIST is an 18-item test. There are three scales, which are strategic, deep, and surface learning. Each scale has six items. Every participant has four response choices for each item: 1=strongly disagree, 2=disagree, 3= agree, and 4 = strongly agree. The score of each scale is the sum of each item related to that scale. Each person has three scores for each strategy (Tait, Entwistle, and McCune 1998). (See Appendix C for test material.) Demographic data were collected from the students. The data include gender, age, ethnicity, high school GPA, family income, and hours of online activity per day. The purpose of collecting the data was to provide additional information about the students’ background and to control for these factors to isolate the relationship between the key variables. (See Appendix D for demographic data questions). Students’ achievement was measured by the final examination score. The examination was conducted in the end of semester. This was a multiple choice, paper-and-pencil test with 45 items. Students of UT in different regions of the country do the examination in certain locations. The grade is measured on a scale of 0 to 100, based on the percentage of number of correct answers. Students’ persistence is measured by whether the students attended and did the final examination. The students who attended and took the final examination are considered persistent students. Those who did not take the final examination were not persistent students. The

46

measurement of influences on persistence uses independent samples t test since the answer is “yes” or “no” for attending final examination. Pilot Test The aim of the pilot test was to measure the validity and reliability of the instruments. The pilot test was conducted in the fall semester of 2014. The participants were students of the university in the previous semester. The administration of UT provided the names of the students for the pilot. These students participated in an online survey with the research questionnaire. The result of the pilot test contributed to the calculation of reliability of the instrument. The reliability test of the attitudes of creativity scores of the rCAB test showed that the Cronbach Alpha was .85 from 25 participants. According to Tavakol and Dennick (2011), the Cronbach Alpha of the rCAB pilot test was high. Therefore, the test had high reliability. Table 1. Reliability Test Result of rCAB Test Cronbach's Alpha Based Cronbach's on Alpha Standardized Items 0.851 0.855

N of Items 25

The reliability test for the learning strategies showed that the Cronbach Alpha was .86 from 18 participants. According to Tavakol and Dennick (2011), the Cronbach Alpha of the ASSIST test for learning strategy was high in the pilot test. Therefore, the test has high reliability.

47

Table 2. Reliability Test Result of Learning Strategies Cronbach's Alpha Based Cronbach's on Alpha Standardized Items 0.866 0.847

N of Items 18

Data Analysis and Variables The researcher conducted descriptive and multivariate analysis. Initially, the researcher calculated descriptive statistics. The aim of these measurements was to measure the participants’ response to all assessments. The descriptive statistics included attitudes of creativity, learning strategies, achievement, persistence, high school GPA, age, gender, departments, daily online activities, ethnicities, and family income. Besides computing descriptive statistics, this study included inferential statistics about creativity. The purpose of inferential statistics was to discover any relationship between attitudes of creativity, learning styles, and examination score. Multivariate analysis included multiple regression modeling and path analysis. The researcher conducted path analysis to develop the models of relationship (Kachigan 1991; Kelly 2014, Loehlin 2004) between creativity and the dependent variables, which were student achievement and student persistence.

48

Table 3. The Required Data No 1

2 3

Variables Creativity Learning strategies Learning outcome

4

Persistence

5

Income

6

Ethnicity

7

Department:

8 9 10

Measurement rCAB test

ASSIST test Examination score

Note A measurement of attitudes and value toward creativity. There are 25 items of Likert scale question. Fifteen items are indicative and ten items are counterindicative. Each participants had three learning strategies scores: deep, strategic, and surface. There are 18 items of Likert scale. The scores ranges from 0 through 100.

Attendance at examination

=1 of student takes final exam =0 if student does not take final exam

Yearly income

The income is converted from Indonesian Rupiah into US Dollar. The currency was US $ 1 equal Rp. 13.000.

Students’ ethnicity

The departments where the participants enrolled Age Years High school The GPA has a range GPA between 1 through 10. Daily online The length of students’ activities online activity each day.

The ethnicities are Javanese, Sundanese, Jakarta, Melayu, and Batak Students were either in the department of Biology or Biology teacher education. The best of the GPA is 10. There were four categories which were >1 hour per day, 1-2 hours a day, between 2-3 hours a day, and >3 hours a day.

This study focused on the four principal research questions. To answer those questions, there were two instruments, which were the rCAB test for creativity and ASSIST test for learning strategies. To answer the research questions, I used the following approaches: RQ 1. To what extent does creativity affect students’ persistence? This RQ was intended to measure whether items of attitudes toward creativity predicted their persistence. The reason for the RQ was the study of Nugraini et al. (2013) and Utami, Noviar, and Agustina (2010), which showed that the creativity training improved high school students’ interest in learning biology. The independent variable was items of attitudes toward creativity in rCAB test. There were 25 items on the test, of which 15 were indicative items and 10 were contraindicative items. The choice of using both items or only indicative or 49

counterindicative was based on the Cronbach Alpha. The dependent variable was students’ persistence that was measured by whether the students completed the final examination for the biology course. The researcher did independent t test to find out to what extent attitudes of creativity affected students’ persistence. Differences in the attitudes toward creativity variables were examined across those who persisted and those who did not, to test for statistically significant differences between the two. RQ2. For those who persist (i.e., those who complete the final examination), what is the relationship between creativity and achievement? The objective of RQ2 was to measure whether attitudes of creativity predicted the students’ test scores. The research question was to confirm the study of Utami, Noviar, and Agustina (2010) and Richvana, Dwiastuti, and Prasetyo (2012) in Indonesia. In the studies, high school students with higher creativity scores had higher test scores in biology. The independent variable was attitudes of creativity, which were items of attitudes toward creativity in rCAB test. There were 25 items on the test, which 15 were indicative items and 10 were contraindicative items. This study did not use the contraindicative items, since those items did not enter the equation. The dependent variable was the examination score. Control variables were high school GPA, age, gender, department, daily online activities, ethnicities, and family income. The analysis used multivariate multiple regressions. The multivariate multiple regression is a statistical technique to describe a linear relationship between the independent variables and a dependent variable (Healey 2012, Field 2013). For RQ 1 and RQ 2, there was a Model 1, shown in Figure 3.

50

Figure 3. Model 1 for RQ 1 and RQ 2. RQ3. To what extent are attitudes of creativity related to student persistence through learning strategy? The objective of this research question was to measure the indirect relationship between creativity and student persistence through learning strategy. This research question would confirm the studies of Tomanek and Montplaisir (2004) concerning different learning strategies of biology students. They found that students who learn with deep learning are less likely to have difficulty in learning biology. They are more motivated to learn and, therefore, they are likely to have higher persistence. To measure the relationship, the researcher used multiple regression analysis technique to find out to what extent creativity in biology affects student persistence through learning strategy. The relationship is described in Figure 4. The independent variables were student learning strategy that was measured by the ASSIST test and student attitudes of creativity that were measured by rCAB. Each participant had scores for the three learning strategies. The dependent variable was student persistence, as measured by whether the student completed the final exam. Control variables were high school GPA, age, gender, department, daily online activities, ethnicities, and family income. RQ4. For those who persist (i.e., those who complete the final examination), to what extent are attitudes of creativity related to student achievement through learning strategy?

51

The objective of the research question was to measure the indirect relationship between creativity and student achievement through learning strategy. The research question would confirm the studies of Nugraini (2013) and Widhowati (2013). The studies showed that most Indonesian high school students learn by memorization, which is more related to strategic and surface learning. The creative students do not engage in those learning strategies. To measure the relationship, the researcher used path analysis technique (Kelly 2014; Loehlin 2004) to find out to what extent creativity in biology affects student achievement through learning strategy. The exogenous variable is creativity and learning strategy. The creativity is measured by items of attitudes toward creativity in rCAB test. There were 25 items on the test, of which were 15 indicative items and 10 were contraindicative items. The learning strategy was measured by the result of ASSIST test. Each participant had scores for the three learning strategies. Meanwhile, the endogenous variable was student learning achievement as measured by examination scores. Control variables were high school GPA, age, gender, departments, daily online activities, ethnicities, and family income. The researcher used decomposition of bivariate covariation to measure the strength of causal relationship. For RQ3 and RQ4, the diagram of model 2 is shown in Figure 4.

52

Figure 4. Model 2 for RQ 3 and RQ 4.

53

CHAPTER IV: RESULTS This chapter consists of five parts. The first part describes participants’ responses to the survey. Following are the results of each research question, beginning from RQ1 through RQ4. The RQ1 section describes the independent samples t test of the student’s persistence. The RQ2 section describes the multiple regression predicting students’ learning outcome. The RQ3 section describes the multiple regression of variables that predict learning outcomes. The R 4 section describes the path analysis of students’ persistence and learning outcomes. There were 102 responses for the questionnaire, which was sent to 286 students at Department of Biology, Faculty of Mathematics and Natural Sciences and 34 students at the Department of Biology Teachers, Faculty of Teacher Training, and Educational Studies. The response rate was 31.87%. Forty-seven participants were men and fifty-five were women. Demographic data of the participants included age, gender, income, department, and ethnicity (see Table 4. for descriptive statistics). Their ages ranged from 17 to 45 years of age, while the average age was 24.8. Their average income per year was US $ 2106.41. The income ranges from US$ 346.15 through US$ 6,692.31. The largest percentage of students (10%) earned US $ 2,200 per year. According to the World Bank Income Classification Group (2012) and OECD (2015), the income levels of the participants were low through middle class. High school GPA of the participants ranged from 6.00 through 9.30. In Indonesia, the high school grade ranges from 1 through 10 in each subject, with 1 the lowest and 10 the highest. Their average high school GPA is 7.82. The highest percentage is 8.00, with 20% of recipients reporting this GPA. The ethnicity of the majority of the participants was Javanese, which was 41.2% of the 102 students. The second largest ethnicity is Indonesian, which includes those of mixed ethnicity

54

and those who refused to identify themselves with any single ethnicity. There were 23.52% students who identified themselves as Indonesian. There were 94 students from the Department of Biology, Faculty of Mathematics and Natural Sciences, and eight students from the Department of Biology Teacher Training, Faculty of Teacher Training and Educational Studies. The highest percentage of daily online activity is 1-2 hour per day, which is 51% of the participants. Twenty percent of the students did online activities less than one hour per day. Meanwhile, 17.9% of the students did online activities 2-3 hours per day. Ninety-two students took the final examination. The minimum score was 22.22. The maximum score was 77. The mean score was 50.34.

Table 4. Descriptive Statistics (N=102)

Age Income (US $) High school GPA Online daily hour Deep learning strategy

N 99 81 93 95 102

Minimum maximum 17 45 346 6692 6 9.3 1 4 12 20

Mean 24.79 2106 7.82 2.13 15.82

Std Dev 5.99 1784 0.69 0.81 1.58

Strategic learning strategy

102

12

23

18.86

2.06

Surface learning strategy Learning outcome

102 92

10 22.22

24 77.77

17.48 50.34

2.55 12.48

Measurement of reliability used Cronbach Alpha (Tavakol and Dennick 2011). The reliability of counterindicative items of rCAB test was .33. According to Tavakol and Dennick 55

(2011), the reliability of counterindicative items of the rCAB test was low. The reliability of indicative items of rCAB test was .63. Meanwhile, the reliability for overall rCAB creativity test is .56. The reliability of the ASSIST test for learning strategies was .60. The 18 items of the ASSIST test were grouped into three subscales according to the three learning strategies. Cronbach Alpha for each, deep-learning, strategic-learning, and surface learning strategy respectively, were .56, .60, and .64. Table 5. Indicative Items of Creativity from rCAB Test N 1. Even if some method has worked well in the past, it is a good idea to question and perhaps change it on a regular basis. 4. Diversity is a good quality in an organization that wants to be innovative. 5. When solving problems is often a benefit to postpone judgment about possible solutions. 7. Solutions and ideas improve in general when we consider a variety of perspectives. 9. If we produce a big number of ideas, we are more likely to find some valuable solutions and ideas. 10. Problem solving and innovation benefit from changes in perspectives. 11. Collecting data and obtaining new information can be useful before solving a problem. 15. I look for different ways of isolate myself, thus I can concentrate and think deeply about my work. . 16. We can find useful ideas when we change the perspective of a problem; no just looking at the problem as it is presented to us. 17. There is a clear benefit when one look for ideas that others will not even consider.. 20. It is useful to consider the opinion of those who have a different perspective, even when we are trying to solve a problem. 22. Work can be fun if we face projects as if they were games. 23. Being original can be useful at work or at school.. 24. Sometimes it is better not to be conventional. 25. I am tolerant with people that are different, bohemians, unconventional, strange.

56

Minimum Maximum

Mean

Std. Dev

101

1

4

2.7723

0.61451

102

1

4

3.3922

0.63178

101

1

4

3.3235

0.56572

102

1

4

3.402

0.49272

102

1

4

3.2843

0.66567

102

1

4

3.0392

0.48587

101

1

4

3.4257

0.53566

101

1

4

2.6139

0.76119

102

1

4

3.1373

0.48826

102

1

4

3.1863

0.54008

102

1

4

3.3039

0.54116

102 102 102

1 1 1

4 4 4

3.3824 3.2843 3.2255

0.58095 0.53357 0.50555

102

1

4

3.1176

0.55032

Table 6. Contraindicative Items of rCAB N 2. One of the advantages of experience is that you can make useful assumptions and work faster. 3. It is a waste of time when all the people involved in a project share their ideas 6. Maybe is good for a scientist to be strange or extravagant, but for most of us is better to follow the crowd. 8. It is not enough to find an original idea. That idea is worthy if we check it, verify it and put it to work 12. Any group work and every project should have a person in charge, that makes constantly that time is not wasted exploring each option. . 13. The best is to keep a stand of “proof and truth” regarding innovation, when we find something that works. 14. Good ideas result from concentrating in a problem. It is good not to rest when one is involved in a project. 18. I avoid working out of my area of knowledge. I do not want to be a beginner again. 19. One important thing in work is to find something that is approved by others (supervisors, colleagues, clients, etc.) 21. It is difficult for me to work with people that have different education or work experience.

Minimum Maximum

Mean

Std. Dev

101

1

4

1.81

0.72

102

1

4

3.13

0.62

101

1

4

2.02

0.59

102

1

4

1.7

0.73

102

1

4

1.64

0.56

102

1

4

1.98

0.58

102

1

4

1.71

0.65

102

1

4

2.69

0.88

101

1

4

2.23

0.77

102

1

4

2.82

0.69

The result of the rCAB test is shown in Table 5 and Table 6. The rCAB test has 15 items of indicative traits for creativity. In each item, at least more than 80% of the participants agreed or strongly agreed, except on item no 15, which is “I look for different ways of isolate myself, thus I can concentrate and think deeply about my work.” In that item, only half of the participants agreed or strongly agreed with the statement. The result of learning strategy is shown in table 7.

57

Table 7. Items Of Learning Strategies

1. Often I find myself wondering if the work I am doing here is really worthwhile. 2. When I'm reading an article or book, I try to find out for myself exactly what the author means. 3. I organize my study time carefully to make the best use of it. 4. I concentrate on learning just those bits of information I have to know to pass. 5. I look carefully at instructor's comments to see how to get higher grades next 6. Regularly, I find myself thinking about ideas from lectures when I'm doing other things. 7. I'm pretty good at getting down to schoolwork whenever I need to. 8. Much of what I'm studying makes little sense. It's like unrelated bits and pieces. 9. I put a lot of effort into studying because I'm determined to do well. 10. When I'm working on a new topic, I try to see in my own mind how all the ideas fit together.

Mean

Std. Deviation

4

3.02

0.72

2

4

3.37

0.59

2

2

4

3.32

0.57

102

3

1

4

2.56

0.86

102

3

1

4

3.25

0.61

102

3

1

4

2.69

0.67

101

3

1

4

2.7

0.62

102

3

1

4

2.47

0.7

102

2

2

4

3.64

0.5

102

2

2

4

3.24

0.47

N

Range

102

3

1

102

2

102

58

Minimum Maximum

Table 7. (continued)

11. I don't find it at all difficult to motivate myself. 12. Often I find myself questioning things I hear in lectures or read in books. 13. I manage to find conditions for studying which allow me to get on with my work easily. 14. Often I feel I'm drowning in the sheer amount of material we have to deal with. 15. Ideas in course books or articles often set me off on long chains of thought of my own 16. I often worry about whether I'll ever be able to cope with the school work properly. 17. When I read, I examine the details carefully to see how they fit in with what's being said. 18. I often have trouble in making sense of the things I have to remember. Valid N (listwise)

Mean

Std. Deviation

4

2.76

0.71

1

4

2.96

0.6

2

2

4

3.23

0.47

101

3

1

4

2.5

0.83

101

3

1

4

3.12

0.57

102

3

1

4

3.02

0.82

101

3

1

4

2.81

0.56

102

3

1

4

2.78

0.75

N

Range

Minimum Maximum

101

3

1

102

3

102

98

In the study, analysis of creativity used the indicative items for measuring creativity. The reason for using only the indicative items was that the indicative items had higher Cronbach Alpha at .55 than the contraindicative that had Cronbach Alpha of .33. The researcher used the items for answering each the following four research questions. The data analysis used three learning strategies derived from the 18 variables of learning strategy from the ASSIST test. The

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reason was that the Cronbach Alpha for learning strategy was .60, which was moderate. The Cronbach Alpha for each of learning strategy was .56, .64, and .60 for deep-learning, strategiclearning, and surface-learning respectively. RQ1 To What Extent Does Creativity Affect Students’ Persistence? The researcher did an independent samples t test to examine similarities and differences between students who persist and who did not. The N was 102. The result was shown in Table 8.The result showed that Levene’s test was not significant, since p = .61. However, the results of two-tailed test were significant. The result was .005 and .02 which were < 0.05. Therefore, there was a significance difference between those who persisted and did not persist. Table 8. Result of Independent Samples t test Levene's Test for Equality of Variances

Equal 24. Sometimes variances is better not assumed to be Equal conventional variances not . assumed

t-test for Equality of Means

Mean Std. Error Difference Difference

95% Confidence Interval of the Difference Lower Upper

F

Sig.

t

df

Sig. (2tailed)

0.27

0.61

2.87

100

0.005

0.52

0.18

0.16

0.87

3.01

8.39

0.02

0.52

0.17

0.12

0.91

There was a significant difference between the students who persisted and who did not persist on variable 24 (Sometimes is better not to be conventional) such that student who persisted had reported higher mean. In table 9, the mean of those who persisted is 3.27, which is greater than the means of those who did not persist, which is 2.75. The student who persisted self-reported to have stronger support for being unconventional than student who did not persist. However, this result was weak since 94 out of 102 participants persisted.

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Table 9. Group Statistics of Independent Sample T Test

24. Sometimes is better not to be conventional.

Std. Error Mean

Persist

N

Mean

Std. Deviation

Persist

94

3.27

0.49

0.05

No persist

8

2.75

0.46

0.16

RQ2. For those who persist (i.e., those who complete the final examination), what is the relationship between attitudes of creativity and achievement? The researcher used the students’ examination scores as the dependent variable. This means that only students who completed the final exam (i.e., those who persisted) were included in the sample. As a result, the sample size fell to 94 for this analysis. The score was the total percentage correct, ranging from 0 through 100. The researcher did a stepwise multiple regression. The independent variables were fifteen indicative items in the rCAB attitudes and values of creativity test and the control variables of age, high school GPA, income, , daily online activities, departments, and gender. The researcher used mean replacement for observations with missing data. The results were that R2 was .21, F was 14.91 and sig F .000. The R2 meant that 21% of the variations were accounted for by the two variables that entered the equation. The result was shown in table 11. Var 22 (“work can be fun if we face the project as if they were games”) predicted learning outcome with negative value. The beta weight was -.26. Those who value fun more score lower final grades, holding the other variables in the model constant. A demographic 61

factor, high school GPA, predicted learning outcome with positive value. The beta weight was .40. Students with higher high school GPA were more likely to score higher final grades, after controlling for other variables in the model. Meanwhile, age, income, daily online activities, departments, and gender were not predictors for learning achievement. Table 10. Regression of Examination Score

Model HS GPA

Standardized Coefficients Beta 0.4

t

Sig.

4.22

0

22. Work can be fun if we face projects as if they were -0.26 -2.72 games. Note. Dependent variable is the examination score.

0.01

RQ3. To what extent are attitudes of creativity related to student persistence through learning strategy? In order to answer RQ3, the researcher used multiple regression. The N was 102. The variables were the fifteen indicative scores of rCAB test, three learning strategies, and control variables. The three learning strategies were deep, strategic, and surface learning. Control variables were age, income, high school GPA, gender, daily online activity, and departments. The endogenous variable was the students’ persistence, measured as students taking the final examination. The researcher did a stepwise regression with mean replacement for observations with missing data. Three variables predicted persistence: variable 24 (“sometimes it is better not to be conventional”), age, and high school GPA. The result is shown in Table 11. The model showed that the R2 was .15, F was 8.23, and sig F was .00. The R2 means that 15% of the variation were

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accounted for by variable 24, age, and high school grade. There were no variables of learning strategies that entered the equation. Table 11. Regression of Persistence Model

Standardized Coefficients

t

Sig.

24. Sometimes it is better not to be conventional. Age

0.26

2.79

0

-0.33

-2.88

0

High School GPA

-0.23

-2.07

0.04

The beta weight of variable number 24 is .24. This means that there was a positive relationship between variable number 24 and persistence. Students with higher scores on being unconventional were more likely to persist in their studies, holding the other variables constant. Age negatively predicted persistence. The beta weight was -.33. Younger students were more likely to persist in their studies, holding the other variables constant. As opposed to the RQ2, high school GPA negatively predicted persistence. The beta weight was -.24. Students with higher high school GPA were less likely to persist in their studies, holding the other variables constant. RQ4. For those who persist (i.e., those who complete the final examination), to what extent is creativity related to student achievement through learning strategy? To find out the relationship between attitudes of creativity and student achievement through learning strategy, the researcher conducted a path analysis. The ultimate endogenous variable was students’ grades on the final examination, which meant that the sample only included those students who persisted throughout the course and took the final exam. As a result, the sample is reduced to 94.

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There were two models of path analysis in RQ4. The first model used three subscales of learning strategies as endogenous variables. The exogenous variables were fifteen indicative variables of attitudes of creativity and control variables. The control variables were age, gender, high school GPA, departments, income, and daily online hour. The ultimate endogenous variable was examination score. The before diagram was shown in Figure 5.

Figure 5. The before diagram of path analysis with three subscales of learning strategy The first model showed that R2 was .21, F was 14.91 and sig F .000. The R2 showed that 21% of the variations were accounted for by the two variables that entered the equation. The result was shown in table 12. Var 22 (“work can be fun if we face the project as if they were games”) predicted learning outcome with negative value. The beta weight was -.26. Those who value fun more score lower final grades, holding the other variables in the model constant. High school GPA predicted learning outcomes with positive value. The beta weight was .40. Students

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with higher high school GPA were more likely to score higher final grades, after controlling for other variables in the model. The after diagram was shown in Figure 6.

Table 12. Regression of Examination Score of Model 1.

Model

HS GPA 22. Work can be fun if we face projects as if they were games.

Standardized Coefficients

t

Sig.

Beta 0.4

4.22

0

-0.26

-2.71

0.01

Note. Dependent variable is the examination score.

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Figure 6. The “after” diagram of path analysis with three subscales of learning strategy.

A demographic factor, high school GPA, was also a positive predictor of learning achievement. The beta weight was .36. Students with higher high school GPA were more likely to score higher final grades, after controlling for other variables in the model. Meanwhile, age, income, daily online activities, departments, and gender were not predictors for learning achievement. Since the result of the first model of path analysis did not show any indirect relationship of attitudes of creativity and learning outcomes through learning strategies, the researcher made another model with individual items of learning strategies. The exogenous variables were the fifteen indicative scores of rCAB test, and six control variables. The endogenous variables were eighteen items of learning strategies. Control variables were age, income, high school GPA, 66

gender, and departments. The researcher did a stepwise regression. The before diagram of the model is shown in Figures 7. The model used mean replacement and measured the 18 subscales/items of learning strategies (Thang, 2005). The after diagram of the model is shown in Figure 8.Variables that did not enter the equation were deleted in Figure 8.

Figure 7. The before diagram of path analysis with individual items of learning strategy. In the model, the researcher used individual items of learning strategies and mean replacement for observations with missing data. The R2 was .32, F was 8.13 and sig F was < .00. The R2 showed that 32 % of the variation was accounted for by the variables that entered the equation. This model showed that variable 22 and variable 24 of attitudes of creativity negatively predicted learning outcomes. Variable 6 and variable 10 of learning strategies positively predicted learning outcomes. The result was shown in Table 13.

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Table 13. Regression of Examination Score of Model 2. Standardized Coefficients Beta

t

Sig.

High school GPA

0.311

3.362

0

22. Work can be fun if we face projects as if they were games.

-0.279

-2.95

0

Model

10. When I'm working on a new topic, I try to see in my own mind how all the ideas fit together.

0.21

2.227 0.03

6. Regularly, I find myself thinking about ideas from lectures when I'm doing other things.

0.204

2.232 0.03

24. Sometimes is better not to be conventional.

-0.187

-2.04 0.04

Note. Dependent variable = examination score The study showed that there was a significant but weak indirect relationship between attitudes of creativity and learning outcomes. There were two items of deep-learning that predicted learning outcomes. A variable of deep-learning strategy predicting learning outcome was var 6 (regularly, I find myself thinking about ideas from lectures when I'm doing other things).The item of learning strategy was affected by three attitudes of creativity. The item of learning strategy was negatively affected by var 4 (Diversity is a good quality in an organization that wants to be innovative), and positively affected by var 9 (If we produce a big number of ideas, we are more likely to find some valuable solutions and ideas), var 17 (There is a clear

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benefit when one look for ideas that others will not even consider) and online daily hour. The result was shown in Table 14.

Table 14. Regression of Var 6

Model

Standardized Coefficients Beta

(Constant)

t

Sig.

2.487

0.015

4. Diversity is a good quality in an organization that wants to be innovative.

-0.277

-2.883

0.005

17. There is a clear benefit when one look for ideas that others will not even consider..

0.288

3.045

0.003

Online daily hour

0.22

2.345

0.021

9. If we produce a big number of ideas, we are more likely to 0.191 1.989 0.05 find some valuable solutions and ideas. Note: Dependent variable = 6 (regularly, I find myself thinking about ideas from lectures when I'm doing other things).

Another variable of the learning strategy items was var 10 (When I'm working on a new topic, I try to see in my own mind how all the ideas fit together). This item of learning strategy was affected by var 23 (Being original can be useful at work or at school) of the attitudes of creativity. The Beta weight of var. 23 was .40. The higher the students valued originality, the more likely they got higher score in the exam, holding the other variables in the model constant. The result was shown in Table 15.

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Table 15. Regression of Var 10 Standardized Coefficients

Model

t

Sig.

4.13

0

Beta 23. Being original can be useful at work or at school..

0.4

Note. Dependent variable = 10. When I'm working on a new topic, I try to see in my own mind how all the ideas fit together. Decomposition of bivariate covariation in Table 16 showed the strength of causal relationship. According to Jacobson (2000), the pair of variables showing the highest total causal effect has the strongest causal relationship. In the second model, the three variables that share the strongest causal relationship with learning outcome were high school GPA (total causal .31), var 10 (total causal .21) and var 6 (total causal .20). High school GPA was the strongest predictor of persistence followed by var 6 and var 10 of deep learning.

Table 16. Decomposition of Bivariate Covariation Daily Var 4 - Var 9 - Var 17Var 23 - Var 22 - Var 24 - Var 6 - HS GPA- Var 10 hour grade grade grade grade grade grade grade grade grade grade Original covariance

-0.23

0.03

-0.02 -0.07

0.12

-0.22

-0.23

0.27

0.39

0.21

Direct indirect Total causal Non causal

0 -0.06 -0.06 -0.17

0 0.04 0.04 -0.01

0 0 0.06 0.04 0.06 0.04 -0.08 -0.11

0 0.08 0.08 0.04

-0.28 0 -0.28 ,06

-0.19 0 -0.19 -0.04

0.2 0 0.2 0.07

0.31 0 0.31 0.08

0.21 0 0.21 0

Total causal effects of indirect effect between attitudes of creativity and learning outcome were weak. There was weak indirect effect of attitudes of creativity (var 4, var 9, var 17) and daily online activity toward learning outcome through var 6 of deep learning. Var 17 of attitudes 70

of creativity had the strongest total causal effect which was .06, followed by var. 9 and daily online activity which was .04 respectively. There was also a weak indirect effect of var 23 of attitudes of creativity toward learning outcome through var 10 of deep learning. The total causal effect was .08.

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Figure 8. After diagram the third model of RQ 4.

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Chapter IV has shown the results of RQ1 through RQ4. RQ1 showed that there was one variable of attitudes of creativity that predicted persistence, which was variable 24 (“sometimes it is better not to be conventional”). The result of RQ2 showed that variable 22 (“work can be fun if we face projects as if they were games”), negatively predicted learning outcomes. Meanwhile, high school GPA positively predicted learning outcomes. Learning strategies did not predict learning outcomes. However, the result of RQ3 showed that variable 24 (“sometimes it is better not to be conventional”), age, and high school GPA entered the equation. Var 24 positively predicted persistence. age and high school GPA negatively predicted persistence. The result of RQ3 showed that learning strategy, as measured by three subscales, did not predict persistence. The result of RQ4 showed that var 22 of attitudes of creativity negatively predicted learning outcomes. Var 24 of attitudes of creativity and high school GPA positively affected learning outcomes. Two variables of deep-learning strategy, which were var 6 and var 10, positively predicted learning outcomes. Therefore, there were indirect relationships between attitudes of creativity and learning outcomes through learning strategy.

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CHAPTER V: DISCUSSION This chapter presents the findings of the study, beginning with the findings for each RQ. It then presents an explanation about attitudes of creativity items that predicted both learning outcomes and persistence. Next is discussion about the themes of the answers for the open ended questions. This is followed by a discussion of implications, notably those for practice, which focuses on the implications for Universitas Terbuka. Lastly, the chapter discusses the limitations of the study. In RQ 1, the independent samples t test results showed that there was a significant difference between the students who persisted and who did not persist on variable 24 (Sometimes is better not to be conventional) such that students who persisted had reported a higher mean. In table 9, the mean of those who persisted is 3.27, which is greater than the means of those who did not persist, which is 2.75. The student who persisted self-reported to believe more strongly in being unconventional than student who did not persisted. The result supported Nugraini (2013) who pointed out that creativity improved motivation among high school students in Indonesia. However, this result was weak since 94 out of 102 participants persisted. The high percentage of persistence among participants of this study was unlike the previous studies. The persistence rate in this study was 92%, as opposed to 80% of biology students in 2014 or 4.8% of general UT students (Belawati 1998). Therefore, the less variability in the data may be the cause of no significant relationship between creativity and persistence in the independent samples t test. However, the path analysis technique showed a relationship between creativity and persistence. In the RQ2, the discovery of a relationship between attitudes of creativity and learning achievement in the multiple regression confirmed the findings of previous researchers (Fischer

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and Sugimoto 2005; Lin 2011; Mishra et al. 2013; Muirhead 2007; Richvana, Dwiastuti, and Prayitno 2012; Utami 2010; Edmonson 2011), who found a relationship between students’ creativity and learning achievement. The traits of attitudes of creativity that are significant were diversity (#4), fun (#22), originality (#23), and being unconventional (#24). Originality had positive relationship with learning outcomes. This finding suggests that the more students valued originality, the more likely they are to have higher examination scores. Meanwhile, diversity, being unconventional, and fun had a negative relationship with learning outcome. The more students possess these traits, the more likely they are to have lower examination score. One controlling factor, high school GPA, also predicted student learning outcomes in RQ2. The students who had higher high school GPAs were more likely to have higher examination score. The other control factors—age; gender, daily online activity, and departments—did not predict learning outcomes. A possible explanation was that since diversity negatively predicts learning outcomes, the influence of that factor became less important. The finding of RQ3 through multiple regression suggested that attitudes toward being unconventional negatively affected persistence. However, there was no indirect relationship between creativity and persistence through learning strategies. None of the three learning strategies predicted persistence. In the case of persistence, being unconventional had a positive value. The students who valued being unconventional were less likely to persist. This study showed that the persistence of students in the department of biology and department of biology teaching was high. Ninety two percent of students of both departments took the exam. The high level of persistence was unlike the previous studies of Belawati (1998) and Catropa (2013). Both authors reported a low level of persistence; in this study, persistence was almost at 92%.

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The model in the RQ4 used the 18 individual items of learning strategies in the ASSIST test. The reason for using individual items of learning strategies was that participants may have similar equal total scores on two subscales. Therefore, using combinations of these scores will result in confusing result (Speth and Lee, 2013). In this study, the researcher used individual items based on Thang (2005) who analyzed individual items of The New Approaches to Study Inventory (NASI), another version of ASSIST. In this model, there were two indirect relationships between items of attitudes of creativity and learning outcomes through learning strategies. The first was between var 4, var 9, and var 24 of attitudes of creativity and learning outcomes through var. 6 of learning strategies. The second indirect relationship was between var 23 of attitudes of creativity and learning outcomes through var 10 of learning strategy. Both var 6 and var 10 of learning strategies were elements of deep-learning. The study showed low R2 in some models, which means that the models explain only small percentage of factors predicting either persistence or learning outcome. For example, the result of the path analysis for RQ 3 showed R2 was .15, which means that the model describe only 15% of variables in the equation. Moreover, R2 in the multiple regression of RQ 2 and RQ 4 was .21 each. It means that the equation predicting 21% of factors for learning outcome. This study disclosed that attitudes of creativity predicted the biology students’ learning success in online and distance learning at the university level. Previous studies (Richvana, Dwiastuti, and Prayitno 2012; Utami 2010) showed significance of creativity for high school students’ success in a classroom setting. Creativity is important for learning biology regardless of the students’ education level. The findings about attitudes of creativity and learning achievement also support a relationship between creativity and learning (Guilford 1950; Vigotsky 2004). Students who

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valued creativity have a better ability to transform their knowledge and to increase its relevance (Kaufman and Begetto 2007). Moore and Kearsley (2012) described that learning in distance education means that a student can combine new knowledge into their existing knowledge structure. Moreover, it means that they can transform what they find from the textbook and from the online activities into working knowledge. The results also supported the study of Ratnaningsih (2013) about the role of creativity for UT students. Creativity is an example of mini-C creativity, which is the individual skill to overcome problems in a unique way (Kaufman and Sternberg 2006). In this study, creativity was an important skill for success in education. Further, the importance of creativity for positive learning outcomes has been found to hold true across diverse cultures and geographies. For instance, the relationship between creativity and learning achievement is discussed by Atkinson (2004). She found a significant positive relationship between creativity and the learning achievement of university students in England. Similar results were also found by Pishgadham, Khodadady, and Zabihi (2011) among English language students in Iran. In distance education, the role of creativity is in line with the concept of transactional distance and learner autonomy. The separation of teacher and students creates a transactional distance. Transactional distance is an interplay between teachers and learners in a separation from each another. (Keegan 2006; Moore and Kearsley 2012). According to Moore (1973), the transactional distance occurs when there is a communication and psychological gap between teacher and students, due to their separation. According to Moore (2012), an important factor of the transactional distance was learner autonomy. Moore described learner autonomy as an independence of learners to develop their own knowledge individually based upon their own experience.

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The learning autonomy allows students of distance education to take control of their learning process. However, not all students may take advantage of using their own autonomy to study. The separation between students and teachers is still a factor that influences students’ persistence (Peat and Franklin 2004). In other words, students in online learning settings have difficulty, since they still share a transactional distance. In this study, the significance of being unconventional (in variable 24) for persistence supports that both autonomy and being unconventional are both necessary for students’ persistence. The finding of this study was that attitudes of creativity predicted examination scores and persistence confirms the role of attitudes of creativity for learning autonomy. Previous studies disclosed efforts to increase learner autonomy by creativity techniques, especially within the online environment. An advantage of an online environment is that it allows collaboration. In addition, activities in an online environment are more participatory. For example, Baken and Kliewe (2012) did an experiment with online learning in Germany. Their aim was to improve idea generation (Amabile 1983) in online activities. The reason for using online activities was that online activities were flexible, faster, and provided a rich means for collaboration. The study showed that there was an increase of creativity skills among participants in the online activities, although there was no measurement of learner autonomy. Those advantages of online activities, such as flexibility and ease of collaboration, are more dominant in Web 2.0, which enables students to participate in creating content. Originality, which was measured by variable 23, is becoming more important. For example, Kovačić et al. (2012) did a study on how to improve students’ creativity based on learner autonomy. They used a Web 2.0 platform, such as wiki, blog, online survey, and online debate among college students

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in Croatia. The study showed that learner autonomy supported creativity and learning results. However, Web 2.0 may become confusing for students (Moore and Kearsley 2012). Considering the advantages of online activities, participants in this study have the potential to take advantage of using the online tutorial with their attitudes of creativity. According to the rCAB test, their attitudes of creativity predicted their persistence and learning outcomes. For example, one significant variable was variable 24 (“sometimes is better not to be conventional”). This trait is related to diversity. The participants felt that being different would allow them to have more new ideas. The role of online learning for diversity that will foster creativity aligns with Wang, Fussle, and Cosley (2011). They posited that a group discussion would have more creativity, if the members contributed diverse ideas. The online activities of UT students are examples of an online group discussion, where students from different backgrounds share ideas. However, the regression did not show that most of the demographic data (daily online activities, income, age, and gender) were predictors of learning outcomes. As opposed to “being unconventional”, attitudes toward originality positively affected learning outcome. The result showed that attitudes toward originality are an important part of creativity. In addition, this study showed that variable 23 (“being original can be useful at work or at school”) was a significant predictor of learning outcome. Runco, Illies, and Eisenman (2005) and Torrance (1995) showed that originality is related to creativity. Amabile (1983) and Sawyer (2003) also suggested that originality is part of creativity, although Sawyer pointed out that originality does not necessarily mean creativity. Rietzschel, Nijstad, and Stroebe (2010) argued that people tend to consider originality as the most significant criteria of creativity. Therefore, most participants consider that originality is one of the most important criteria for creativity, confirming previous studies.

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Originality is an important element in creative problem solving. According to Mumford and Vessey (2011), there are eight steps involved in solving a problem creatively. These steps are: 1. Problem definition. 2. Information gathering 3. Concept selection 4. Conceptual combination 5. Idea generation 6. Idea evaluation 7. Implementation of planning 8. Solution monitoring According to Mumford and Vessey (2011), these steps are connected with originality. Therefore, the significance of originality in this study may partly explain the importance of the other attributes of creativity in learning biology, which are problem finding, idea generation, analogy, and idea testing (DeHaan 2009; Dunbar 1997; Lawson 2001). This section discusses this study’s findings on originality in terms of Mumford and Vessey’s (2011) categories for creative problem solving. First, finding a problem with a proper definition is a predictor of originality. In Mumford and Vessey’s study, undergraduate students, who could define problem better, had a higher originality scores. Although this study did not investigate idea generation, which is part of creativity, the result indirectly confirms the significance of idea generation. The indirect confirmation is that originality is an element of idea generation (Runco 2006). According to Csikszentmihalyi (1994), Guilford (1950), Mumford et al. (2010), and Vygotsky (2004), originality is a crucial component of creativity. Originality not

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only concerns producing as many ideas as possible, it is also key to making decisions, judgments, and evaluations of ideas. This study showed that attitudes of originality is important for students in learning biology, although the study did not specifically observe how students at UT draw analogies between ideas or topics that they learn in online activities. Originality is necessary in making analogies, which is part of creativity for learning biology. A creative person can find a connection of different ideas through analogy (Dunbar 1997; Russ and Dillon 2011). Analogy is an important element of deep-learning strategy, since deep-learning strategy includes finding patterns of relationship among subjects (Entwistle, 2000). Previous studies by Kubika-Sebitosi (2007) and Nugraini (2013) disclosed that biology students need skills in analogy. This study did not focus on idea evaluation, although originality influences skills to evaluate ideas. The significance of var 10 of deep-learning strategy and var 23 of attitudes of creativity showed the importance of originality for idea evaluation. Var 10 is about relating ideas while var 23 is about originality. Var 23 positively predicted var 10. Licuanan, Dailey, and Mumford (2007) suggested that measuring originality is a method to evaluate new innovative ideas. Idea evaluation is also important for deep-learning strategy, especially when learners are relating ideas and finding patterns among ideas (Entwistle, 2000). This study showed that originality, which is represented by the significance of variable 23, (“being original can be useful at work or at school”), is one of the factors of creativity that predicts learning outcome. Another importance of originality for creativity is that when the environment is conducive to originality, creativity will improve. According to Kozbelt, Beghetto, and Runco (2006), if the environment promotes originality, there is an increase of creativity. Similarly, Ward and Kolomytis (2006) posited that if students are given assignments with a

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specific detail in mind, they show less creativity. Furthermore, they will be more creative, if the assignment requires more abstract approaches. The finding about factors of originality affects the university in preparing the curriculum. The development of learning materials at UT was based upon the requirements that the materials allow self-learning and that they be self-contained with respect to the learning objectives (Daniels 1996). This goal may be less clear with the introduction of Web 2.0 that allows participation of learners in the content production (Friedman and Friedman 2013; Hsu, Ching, and Grabowski 2014). This challenge emphasizes the role of creativity, since the creative students will be able to transform the information into their understanding (Runco 2006). Moreover, in view of the findings of the study about the significance of originality (variable 23), the biology students will have the skill to participate in the online environment, even if UT introduces more Web 2.0 technology. Originality is related to uniqueness, which is measured by variable 24, (“sometimes it is better not to be conventional”). Kozbelt, Beghetto, and Runco (2006) suggested that a stigma for being unconventional or unique might inhibit creativity. If the environment does not allow people to behave differently, people will limit their own original ideas. Meanwhile, the result of this study confirmed this statement, since both factors significantly predicted creativity. The results of this study show that the importance of uniqueness is not only limited to Western culture. In addition to originality, the results showed that Indonesian college students in this study showed high importance for uniqueness, which is part of individualism. Individualism is part of Western culture (Goncalo and Shaw 2006). An explanation for the different findings between this study and previous studies about uniqueness, playfulness, diversity, and originality concern social rejection (Kim, Vincent, and

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Goncalo, 2012). In the RQ4, originality positively predicted var 10 of deep-learning strategy. However, the negative weight of attitudes of playfulness, diversity, and uniqueness showed that attitudes of conformity was significant. As a result, students were more interested in conformity and seriousness during their study. Kim, Vincent, and Goncalo (2012) posited that social rejection may promote creativity. Some people, who have independent self-concepts, react positively to rejection for their ideas. Instead of limiting them, the rejection motivates them to do better and to be more productive. In addition to uniqueness, unconventionality is also a trait of creativity. The path analysis result showed that variable 24 (“sometimes it is better not to be conventional”) was a significant predictor of learning outcomes. Runco (1996) discussed that creative persons tend to be contrarian. They choose to be different. Then, they have better abilities, strategies, and solutions. Another explanation for the negative weight of uniqueness in variable 24 is a need for adaptation. Although uniqueness is an element of creativity, there is a paradox between creativity and adaptation. A creative idea is usually unique compared to other ideas already in place. On the other hand, the creative idea should be adaptive to the society. If the new idea is too different from norms within a social context, the idea will not flourish (Cohen 2011). This is why both uniqueness and adaptation are both necessary for creativity. Adaptation is related to conformity, where people tend to behave according to a common norm. Sheldon (2011) suggested that conformity does not mix with creativity. Social forces play a significant role in creativity. A negative reaction from peers or teachers, for example, will cause students not to risk any endeavor. On the other hand, the opposite of the conformity is autonomy. With the trait of autonomy, a person is more willing to make innovations. A person

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with the autonomy trait needs less support from others for making a decision. This person has more courage and persistence for criticism. There was a different effect of attitudes toward being unconventional on persistence and learning outcomes. Support of being unconventional positively affected persistence. However, support of being unconventional negatively affected learning outcomes. It is likely that students need to value being unconventional. However, the students need to value not being unconventional to get higher learning outcomes. This study’s findings have implications for several discourses in the literature about the concept of uniqueness. The first relevant discourse about uniqueness is that uniqueness coincides with conformity. Another discourse about uniqueness is about contrarianism and post conventional contrarianism. Runco (2011) pointed out that some people tend to be contrarian in order to be innovative, despite criticism from others. This person tries to preserve originality. However, another tendency is post conventional contrarianism. Instead of being contrarian, the person is aware of the general rule. Therefore, the person is partial to conformity. The results showed age negatively affected persistence. One explanation is that fluid intelligence (Csikszentmihalyi, 1996) that is dominant in early ages, is more meaningful in the online learning program. For example, the participants’ ages ranged from 17 through 45. Such age variation reflects developmentally different mental resources. There are two cognitive abilities that differ concerning to age. One of these is fluid intelligence, which is the capacity to make quick responses and accurate computations; this ability peaks earlier on the age scale. On the contrary, crystalized intelligence, which is the mental ability that allows judgment, logical reasoning, and understanding similarities of different attributes, is an ability that develops later on the age scale. Crystallized intelligence appears to increase with increases in reflection skill

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(Csikszentmihalyi, 1996). Since the participants with different ages do the online learning together, they will encounter others with different ages and different types and levels of mental ability. Younger students may be more persistent since they have the benefit of their fluid intelligence. Also, younger students may have fewer responsibilities, like children., that can make persistence more difficult. In online activities, the different types of students’ personalities regarding their conformity need attention from the tutor. Tutors need to provide encouragement for those who are less confident with criticism. Meanwhile, the tutors may provide more challenging tasks for students who have autonomy. However, since online environments will have an increasing number of students, including in UT (Hewindati and Zuhairi 2009), providing this attention is a challenge for the tutor in online activities. Besides being unique, another trait that has negative effect on learning outcomes was variable 22, (“work can be fun if we face projects as if they were games”). The role of fun in creativity is connected to the playfulness of the persons. Csikszentmihalyi (1996) pointed out that one creativity trait is playfulness. Creative persons really enjoy what they do. Csikszentmihalyi also posited that the playfulness should be accompanied by perseverance. In this study, the perseverance is measured by the daily online activities. In contradistinction, I find no relationship between the length of students’ online activities and their learning outcome of these activities. It is likely that their learning activities are not limited to their online activities. Another explanation for the negative weight of fun or playfulness is that students are more likely to prefer seriousness. Acar and Runco (2015) disclosed that the weight of the relationship between seriousness and fluency was .99 while playfulness was .69 in a verbal test of divergent thinking. In addition, the weight of the relationship between seriousness and fluency was .99 while playfulness was .68 in a figural test of divergent thinking.

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The role of seriousness as an attitude of creativity is important since playfulness and seriousness are both important for creativity. Sullivan (2011) examined students’ ability to move between seriousness and playfulness. An environment that allows students to be playful and be serious is important to foster creativity. There should be more studies about whether the students have the flexibility or not in UT. Other studies showed that creativity persisted despite lack of playfulness. According to Kaufmann and Vosburg (2010), participants with higher negative mood had higher creative problem solving. Kaufmann (2003) showed that different moods affect different aspects of creativity. Another evidence of seriousness was the significance of daily online hour. The result of RQ4 showed that daily online hour of internet activities positively affected var 6 of deeplearning strategy. Students who work longer in the internet were more likely to focus on understanding the subject. They were also more likely to have higher learning outcomes. Seriousness is important for distance learners, especially in the online program. They have to spare their time to study with other job or family responsibilities (Daniel, 1996). A participant commented in the open ended question that creativity is required for time management in online learning. “It is hard to learn the module without creativity. We must be creative to allocate time for study and for job.” (SY, a female student). The students considered that creativity was necessary for studying while having a job . Another reason for seriousness for the distance learners in the study was that they have limited or almost no face-to-face meeting with tutors/instructors (Daniel, 1996). One participant of this study commented:

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“Online learning is difficult. We do not have a direct contact with the tutors/instructors. We need creative thinking to understand the topics.” (WK, a female student). Seriousness was as important as creativity for the students to study in the online environment. Another result of this study was that variable 24 (“being not conventional”) was a significant predictor of both learning outcomes and persistence, which means that there is a possible way to foster students’ creativity. For example, immediate feedback is also part of online tutorial at UT. The feedback is a responsibility of online tutors (Horng et al. 2005). The feedback may include answers or confirmation of their questions. The feedback may also provide increased support for developing original ideas. Students may become more confident that they can find new ways to solve their learning problems Since this study showed that being unconventional and being serious were significant factors of creativity among the students, a more challenging online environment should foster students’ creativity. The environment should allow students to keep studying without fear of a failure. Therefore, the online learning activities of UT should encourage students to try new ideas. One reason for the negative value of the attitudes of being unconventional is that students prefer to make consensus. Villalba (2011) posited that in education, it is very common that teachers and students try to find convergence. In this study, students were more valuing consensus than being unique or original. However, the preference of consensus decreases creativity. According to Cayirdag (2011) there was a negative correlation between originality and consensus. Cultural factor may influence students’ preference for conformity. A previous study by Lebedeva and Schmidt (2013) showed that students from China focused more on conversation,

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compared to students from Russia and Canada. The Chinese culture has similarity with Indonesian culture. Therefore conformity is essential for biology students at UT. Although diversity was an element of creativity, a creative person should have an appropriate attitude toward diversity. People tend to mix with others who share similarities. Diversity does not necessarily enhance creativity (Paulus, Kohn, and Dzindolet 2011). Hence, Paulus, Kohn, and Dzindolet (2011) posited that people need a positive attitude to mix with people from different backgrounds. It was an opportunity for them to foster creativity, if they have a positive attitude. They also suggest that both intrinsic and extrinsic motivation may increase the attitude. However, the same authors pointed out that intellectual and cognitive diversity have a greater effect on creativity. Demographic diversity, such as ethnicities, age, and socio-economic status, were less influential for creativity. The significance of high school grades as a form of intellectual diversity was an example of the suggestion of Paulus, Kohn, and Dzindolet (2011). Hence, the insignificance of other controlling variables was the evidence that other factors of diversity were less influential for creativity. As opposed to Paulus, Kohn, and Dzindolet (2011), cultural diversity plays a role that influences creativity. For example, Oades-Sese and Esquivel (2011) posited that the United States is a multicultural and pluralistic society that has various manifestations of creativity. In this study, although more than 40% of the participants are of Javanese ethnicity, other ethnicities were part of the student body of UT. Twenty-three percent of the participants had mixed ethnicities. Another factor of creativity that predicted learning persistence was variable 9, the attitudes of producing a lot of ideas. The result of this study showed a negative relationship between attitudes toward producing a lot of ideas and var 6 of learning strategies. The students preferred

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focusing on working with a fewer number of ideas, rather than producing more ideas. Sawyer (2006) pointed out that some people preferred focusing more on specific ideas. Students of department of biology and department of biology teacher mostly belonged to the group that wanted to focus on specific ideas. The significance of focusing on selected ideas is in line with convergent thinking. Basadur and Basadur (2011) and Sawyer (2006) connected convergent thinking with selectivity of ideas. They posit that creativity consists of both divergent and convergent thinking. In the beginning, the focus is on divergent thinking. The dominant activities in divergent thinking is brainstorming and deferring judgement of the ideas. After that, convergent thinking is more dominant. The steps include selecting which ideas are positive or negative. The second phase is an example of convergent thinking, which has characteristics of quality, judgment, and discipline. While students were more eager to focus on selected ideas rather, the role of online activities during learning was more important. According to Chibaz-Ortiz, Borroto-Carmona, and De-Almeida-Santos (2014), online learning may improve creativity. Meanwhile, Goodyear, Jones, and Thompson (2014) posited that online learning allows collaboration in learning, as at UT, where the students can improve their ideas by sharing feedback. For the Indonesian context, the significance of originality, being unconventional, fun, and diversity among UT students reveals that creativity is an element in the students’ online learning activities. However, the focus of biology teaching in Indonesia is still on memorization (Nugraini 2013; Richwana 2013). Based on Hsu, Ching, and Grabowski (2014) and Peat and Franklin (1998), the use of online learning activities still preserves the modality of memorization over making new meanings. Amidst indications that persistence and success are linked, additional

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study regarding the implementation of integrated online and laboratory sessions should benefit the effectiveness of such implementations. Online learning is an environment for creativity. A good environment is a need for developing creativity (Feldman, Csikszentmihalyi, and Gardner 1994). In this study, the online tutorial at the department of biology at UT is the environment for creativity. An example of an environment is the place where one lives. According to Csikszentmihalyi (1996), the environment may support creativity for its access to creativity, for access to domain, for access to field, and for innovative emulation. It can also be inspiring for people. Besides creativity, a controlling variable also affects learning outcome. A factor outside creativity that positively predicted students’ learning outcomes is high school GPA. The high school GPA predicts students’ learning outcomes in the university. One possible explanation is that the students who value creativity can do well both in their high school and in their future education. They can find many ways to learn, despite a different learning environment, either in a classroom situation or in a distance education situation. Those who did well at high school may do well in distance learning. Runco (1996) posited that the transformational capacity that characterizes creativity is constant throughout life. However, no information is available concerning the level of creativity among the participants in their high school. On the other hand, high school GPA negatively predicted persistence. They were less likely to succeed in the distance education program at UT. Although the high school GPA did not measure intelligence, it was the only proxy for participants’ intelligence. There was still a controversy about the connection between creativity and intelligence (Guilford 1950; Sawyer 2006). Csikszentmihalyi (1996) pointed out that people need a certain level of intelligence to be creative. However, a person with a very high IQ score

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may have less curiosity, which is critical to being creative. The result of this study showed that creativity aligns in line with intelligence, which is measured by high school GPA. The significance of high school GPA may provide information for UT and the tutor to provide more attention for students with a low high school GPA. Since UT does not have any entrance test for admission, it is likely that many admitted students have low high school GPAs. Those students need more attention for their learning success. For example, the university may provide online training for creativity. The purpose of the training is to increase persistence, since persistence is not influenced by high school GPA. The study did not show any direct relationship of creativity and difficulty of learning biology. Studies of learning difficulty in biology (Cimer 2012; Hambooaka 2007, KubikaSebitosi 2007; Nugraini 2013; Oztap, Ozay, and Oztap 2003) show that students have difficulty in understanding different interconnected concepts in biology and misconception about some concepts. However, the result of the study did not reveal about significance of the indicative items of the creativity test and students learning outcome that can explain how to solve the difficulty. The negative effect of fun toward learning outcome was different from previous studies. The results on the role of the motivational factor which is shown by participants’ choice for number 22 (Work can be fun if we face projects as if they were games), confirm that participants’ choice of fun is a motivational trait that affects creativity. The previous studies revealed that the participants’ choice of fun is a motivational trait that affects creativity. A motivational factor is important for creativity in daily life (Guilford 1950). Since the study of online learners focuses on their daily creativity (Kaufman and Begheto 2007; Runco 2000) to study biology, this study should have shown this trend among participants.

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The playfulness in online learning would allow biology students to develop ideas to understand difficult topics in biology. The significance of fun for creativity is in line with playfulness. Fun or playfulness is a situation when people are more flexible. The flexibility allows people to be open to new ideas. O’Quin and Derk (2011) described a relationship between playfulness and divergent thinking. Since the role of fun is significant, an explanation is that the students feel eager to try new ideas. According to Richvana, Dwiastuti, and Prayitno (2012), students who have the creativity training are more willing to present their thinking in the class. They are also more confident. However, the study of creativity among UT students did not cover the content of the students’ participation in the online activities. The role of fun for learning is another example of a transformation within learning. Runco (1996) posited that learners become more creative when they transform a problem that they encounter. The transformation is that the learners face the problem as a challenge. Furthermore, they may regard it as an enjoyable challenge. As a result, when the online students feel that they enjoy learning in the online program, they become more creative and have a better learning result. However, this study showed different result since fun negatively predict learning outcome. Var 17 (There is a clear benefit when one look for ideas that others will not even consider) of the attitudes of creativity was a variable that positively predicted variable 6 (regularly, I find myself thinking about ideas from lectures when I'm doing other things) of the learning strategy. Var 17 also related to attitudes toward originality like the var 23. This finding was in line with Eintwistle (2000) that one trait of deep-learning strategy was to find the pattern of the subject that they learn.

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The study showed that there was a significant but weak indirect relationship between attitudes of creativity and learning outcomes. There were two items of deep-learning predicted learning outcomes. This finding confirmed the result of Gadelrab (2011) that deep learning predicted learning outcomes.

For example, one of the learning strategy items was var 6

(“regularly, I find myself thinking about ideas from lectures when I'm doing other things”). This finding is in line with a study of Richardson (2003) among students of UK Open University who conducted an online program. This item of learning strategy was affected by var 23 (Being original can be useful at work or at school) of the attitudes of creativity. Another variable of deep-learning strategy predicting learning outcome was var 10 (“When I'm working on a new topic, I try to see in my own mind how all the ideas fit together”). According to Entwistle (2000), this is an item that relates to the activity of finding pattern among the content of the learning material. The item of learning strategy was affected by three attitudes of creativity. The item of learning strategy was negatively affected by var 4 (Diversity is a good quality in an organization that wants to be innovative), and positively affected by var 9 (If we produce a big number of ideas, we are more likely to find some valuable solutions and ideas) and var 17 (There is a clear benefit when one look for ideas that others will not even consider). The finding of this study about the significance of deep-learning strategy confirmed the findings of Thang (2011) about learning strategy of distance learning students at Open University of Malaysia. Thang found that in the observation on scales of learning strategies, the students of distance learning used more deep-learning strategy than on-campus students. However, there is a previous study which disclosed that strategic-learning strategy was more dominant among online learners. Richardson’s (2003) study on students conducting online courses in the UK Open University found a significant positive association between strategic-

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learning strategy and learning outcome. He also found a significant negative association between surface learning and learning outcome. Almost all students answered the two open ended questions, except three who did not answer. Most answers were that they agreed with the creativity. One student felt that he was not creative. The themes of the students’ answers were: Finding new methods of learning, understanding the topics, time management, and application. A student found that creativity is needed to find a new method of online learning. “Creativity may result in discovery of new method of online learning which is more appropriate for students. In the online discussion, students compete to ask and answer questions. We need creativity to make our questions and answer unique” according to DR, a female student. They also feel that creativity allows them to have different points of view (DN, a female student). These answers revealed the attitudes of originality, which was the focus of variable 17 and variable 23 of the rCAB test of attitudes of creativity. Creativity was required to understand the topic. The students learn by connecting different ideas. YH, a male student, posited that he needed to connect different questions and fact to answer questions from the tutor. This is an example of an item of deep-learning strategy which is about relating ideas. The result of RQ4 showed that variable 10 (When I'm working on a new topic, I try to see in my own mind how all the ideas fit together) of learning strategies positively predicted learning outcome. In addition, var 10 of deep-learning strategy was predicted by var 23 of attitudes of creativity which is about attitudes of originality. Time management is a benefit of creativity in online learning. For example, RA a male student pointed out “creativity is necessary to learn effectively and efficiently”. Time

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management is an item of strategic-learning strategy. However, the quantitative technique in this study did not show any significance of strategic learning. Understanding the topic is also a benefit of creativity. FY said that she was curious about “what, why, and how” in discussions. This is an example of deep-learning strategy according to Entwistle (2000). Entwistle pointed out that deep-learners focus on understanding the meaning. The significance of variable 6 of learning strategies (Regularly, I find myself thinking about ideas from lectures when I'm doing other things) in RQ4 supports this idea. Variable 6 is related to the students’ interest in ideas (Speth, Namur, and Lee, 2007). Application is one element of creativity. AMD, a male student answered that “creativity is not only finding new ideas. It should also find better and useful ideas”. Runco (2004) and Kaufman and Beghetto (2007) pointed out that creative ideas included application in a meaningful way.

Implications for Future Studies This study leads to certain implications for studies in the field of distance learning. The first concerns the role of deep-learning strategy in distance learning (Holmberg 1995). The second has to do with the effect of online learning environment on students’ creativity. The third is the connection between learning technology and pedagogy. The significance of variable 6 (Regularly, I find myself thinking about ideas from lectures when I'm doing other things) and variable 10 (When I'm working on a new topic, I try to see in my own mind how all the ideas fit together) of deep-learning strategy in RQ4 showed that there was an indirect relationship between attitudes of creativity and learning outcomes. However, there should be more studies since the result was of low significance.

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Although this study showed that creativity is important for online learners at UT, there is a need to investigate how students use the online environment and how this use can influence creativity. Friedman and Friedman (2013) suggested the use of social media to increase students’ creativity. The social media allows students to produce content for the online environment and it allows more flexibility to adjust content. However, as Hsu, Ching, and Grabowski (2014) pointed out, there should be studies about whether students use the flexible online environment such as Web 2.0 for creating meaning, or for downloading the content. The applicability of creativity in UT questions the connection between technology and pedagogy. Distance education depends on more online activities (Moore and Kearsley 2012; Taylor 2001). This trend requires the distance learning institution to consider the relationship between education technology and its implementation of pedagogy (Anderson and Dron 2011). Therefore, when UT decides to include fostering students’ creativity, the management should adjust its policy in preparing the faculty and media experts as well as the student support system. A creative person has to have a capability to switch back and forth between convergent and divergent thinking. Therefore, the role of variable 9 is important for persistence. Gauba and Kaufman (2006), in describing the shift from one form of thought to the other, observed that divergent thinking that is more implicit shifts into convergent thinking that is more explicit. In the divergence step, the person finds implicit regularities or associations. In the convergent step, the person develops explicit planning, reasoning, and deduction. Sawyer (2006) added that the connection between the divergent and convergent thinking is complex. A successful creative person has to shift between two different kinds of thinking at a certain point.

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Implications for Practices This study contains implications for UT management. Recommendations for UT management to implement best practices are that UT management should consider: 1. Development of methods to foster student creativity 2. Development of methods to foster deep-learning approach 3. Adjustment of online activities to increase support for student creativity An implication for this study is that UT needs to consider conducting creativity training for its students, especially for biology students. The students may take advantage of the training to improve their persistence and their learning achievement. The university will improve its accountability with higher students’ persistence and learning achievement. The creativity training may include students at both the Department of Biology and the Department of Biology Teacher at UT, since the differences were not significant. An important part of creativity training is the content of online learning. The implementation of online learning in UT should promote creativity. Moore and Kearsley (2012) suggested the use of online collaboration, such as Web 2.0 application, to encourage creative interactions in distance learning. For example, the course developer should promote uniqueness, fun, originality, and diversity. Fasko (2001) suggested that a conducive learning environment is important for creativity. The online learning environment at UT should consider students’ uniqueness, fun, originality, and diversity. Since the study showed significant attitudes of creativity, the focus of the training for the UT students should focus on factors that predict learning achievement. As indicated in this study, diversity, fun, originality, and uniqueness were the factors of creativity influencing students’ learning success. Therefore, the focus of the training should be on those factors. In addition,

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other traits of creativity, which are problem definition, analogy, and idea testing (de Haan 2009; Dunbar 1997; Lawson 2001) should be part of the training. For example, Mumford, ReiterRalmon, and Redmond (1994) found a positive relationship of problem definition and originality. College students, who have to generate problems before doing an assignment of making a market survey, have higher quality and originality of their ideas compared to the control group. Mumford et al. (1994) posited that the former group has a broader range of ideas compared to those who did not participate in the problem definition process. The change of student type and number requires more study of how best to train the UT biology students for creativity. They live in different locations across the country. There are not enough studies on how to conduct creativity training in this field. An example of the creativity training for biology students at the University of Wisconsin (Claude-Hansen et al. 2008) was in a classroom setting. Claude-Hansen et al. (2008) used case studies and problem solving about antibiotic resistance. On the other hand, Clase et al. (2009) planned to use virtual reality for training creativity among college students. Shelmet, Shields, and Huggins (2008) made an online learning program for biology that includes training for creativity. However, the study did not engage in any creativity measurement. Then, the new program for improving creativity will influence the course planning. Preparation of learning material in distance education institutions conducted by a team of subject matter experts, media experts, and instructional designers (Daniel 1996; Holmberg 1995; Peters 2005). The inclusion of efforts to increase creativity requires training for the team. For UT, the effort to improve students’ creativity will include regional offices throughout the country. Besides fostering creativity, another implication of this study is fostering deep-learning strategy among students. This study revealed that items of deep-learning strategy that the student

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used were interest in ideas and relating ideas (Speth, 2007). The focus of the program to foster deep learning should include these items. For example, the course design should encourage critical thinking while reducing memorization (Thang, 2005). Implications of this study about creativity will not only affect students; it will also affect the distance education institution. This study suggests that distance education institutions will need to adapt their pedagogy in order to have online tutors serve as facilitators rather than content providers. According to Peters (2010), the use of virtual technology requires the management of the distance education institution to understand the new pedagogy. For example, in Web 2.0, students may also become the producers of knowledge. The preparation of learning material and learning support system at UT should realize the change and adapt to maximize students’ success. Horng et al. (2005) suggested that the role of an online tutor should be that of a facilitator, rather than that of a lecturer. The change of the tutors’ role means that the tutors would use more student-oriented approach. The significance of variable 9 and its relationship to convergent thinking has a relationship with the organizational culture. Mouchiroud and Lubart (2006) noticed that convergent-thinking tasks would influence an organization to become more centralized. Such an organization would have a more obvious structure of leader-follower. In an educational organization, this structure manifests in a relationship between instructor and learner, although in a more student-oriented learning paradigm, students have a more active role in learning. The students learn through the self-instructional learning material in a distance learning university like UT (Belawati 1998: Daniel 1996). However, the inclusion of internet technology such as the Web 2.0 will bring about a greater participation of students in content production. Although the

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learner-oriented paradigm is becoming more common, the effect of new technology on how students learn is an important part of fostering creativity in students. Areas for Future Research Some areas of future research may be elaborations on the results of the study. As opposed to the implications for future studies, this section includes specific studies to address some of issues within the study. The areas of future research are (a) observation of aspects of creativity, (b) collaborative learning activities in online environments, (c) relationship between attitudes of creativity and learning strategies, and (d) the use of Web 2.0. First, the particular aspects of creativity discussed in this study are not yet well understood or measured. For instance, scholars have not yet developed a scale to measure students’ use of analogy, generation of ideas, or testing of ideas (de Haan 2009; Dunbar 1997; Lawson 2001), which are elements of creativity for this study’s biology students. This study focused on creativity in general. Although it revealed the most important predictors of creativity, those predictors were not specifically addressed in respect to the creativity for biology learners. A more comprehensive study of creativity of biology students in online learning is necessary. There is also a need to conduct a study about what distance learners do in online activities. According to Goodyear, Jones, and Thompson (2014), collaborative study is important in online activities. They also pointed out the role of Web 2.0, where students share ideas and contents. In addition, Ziegler and Diehl (2009) disclosed that the students use the online media for sharing ideas. This study may also use qualitative measurement to find out how students take advantage of the online media, including Web 2.0 A topic for research on what distance learners do in online learning is a study of collaborative learning activities among UT students. According to Chibaz-Ortiz, Borroto-

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Carmona, and De-Almeida-Santos (2014), collaborative learning significantly increase students’ creativity. Another example of collaborative learning is found in a study by Amhag (2013). Amhag studied collaborative learning and higher-order thinking as elements of creativity. This study of collaborative learning and higher-order thinking may explain the role of the deeplearning strategy. Besides collaboration, the possibility of using Web 2.0 to enrich the online learning environment of UT is another possibility for future studies. Alias’s (2013) and Sulaiman’s (2013) studies on using Facebook for learning purposes may become an example for UT. The application can be for biology students or for other departments at UT. The use of Web 2.0 allows students to use richer media for producing and sharing knowledge among them. The learning process includes the transformation of knowledge (Beghetto and Kaufman, 2007; Runco 2004). It would also be of interest to learn whether and how students contribute knowledge and transform knowledge in the Web 2.0 context. The relevance of creativity for laboratory practices of the biology students at UT is another area for future research. Biology students at UT have to attend laboratory practices in partner universities around the UT regional offices. A problem here is that students have different levels of competency for conducting the laboratory activities (Hewindati and Zuhairi 2009). A study of students’ creativity and its relationship with creativity may contribute to increasing students’ success in laboratory activities. Another area for future research is students’ problem solving skills. Mumford and Vessey (2011) pointed out steps of creative problem solving. Creativity within the mini-C paradigm is about how a person finds out a new way to improve personal productivity or achievement in daily life (Beghetto and Kaufman, 2007; Runco 2004). For biology students in distance

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education, this problem solving is mostly about how to learn better. A study about how students solve problems using creativity skills should be a topic for further studies to improve persistence and learning outcomes. The study of attitudes of creativity in the context of online learning may also be applied to other departments at UT. Although they have different subjects to learn with different characteristics, the application of creativity needs further investigation. In addition, the result of this study of creativity may have an impact on other universities. For instance, UT has a cooperative relationship with partner universities for conducting laboratory practice for biology students (Hewindati and Zuhairi 2009). This cooperation is the beginning for broader cooperation in applying creativity. Since creativity will benefit students until after their graduation, a longitudinal study for the effect of creativity on future careers will provide more information about the significance of creativity. Guilford (1950) suggested a role of creativity for those who enter work force after graduating from education. Creative skills are important for design, planning, and problem solving in various occupations. Consequently, a study about how the effect of different levels of creativity among the graduates of both the department of biology and the department of biology teacher of UT are important. The result will reaffirm the study about creativity among students of biology students of UT. The result of a relationship between attitudes of creativity and learning strategy requires more studies. One possibility is to study a relationship between creativity and learning strategy using creativity tests. Another possibility is to study a relationship between attitudes of creativity and other learning strategies such as strategic-learning and surface learning.

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The study of various attitudes of creativity measurements should include the counterindicative items of rCAB test. This study showed that counterindicative items did not show that those items predicted either students learning outcomes or learning persistence. Previous study by Girvina (2013) did not show significant results either. Limitations There are factors that constitute limitations on this study. The first is that only students who were active in the online tutorial replied the survey. The second is that there was a percentage imbalance between two departments. The third is small sample size. There is a low response rate. Lastly, there is limited generalizability, A selection bias might exist in the study. Given that the delivery of the survey was through email, it is likely that students who did not check their email regularly would not have replied to the survey. There is a possibility that the students who replied to the survey were those who were confident of participating in the online tutorial. It was also likely that students who did not participate regularly in the online tutorial were less likely to answer the survey. The second is that there was a percentage imbalance between two departments. There were only eight students from the Biology Teacher Department, compared to 94 students from the Biology department. There can be less variability in the data, since there were far more students from the Biology Department than students from the Biology Teacher Training Department. The sample size and response rate of the participants were small. There were 286 students in the Biology Department and Biology Teacher Training Department at UT. Some participants were students from other departments of the Faculty of Science who took the course.

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Despite several reminders through email and telephone call, there were only 102 students responded. The response rate was 30%. The generalizability of this study was limited. This study only aimed specifically at biology students in distance learning in Indonesia. At present, only UT has a distance learning program for biology in higher education in Indonesia. This study was about the online tutorial activity in the distance learning program, which was only a part of the distance learning program at UT.

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CHAPTER VI: CONCLUSION The study explored four key research questions. In RQ1, there was a significant difference between the students who persisted and who did not persist on variable 24 (Sometimes is better not to be conventional) such that student who persisted had reported higher mean. . However, this result was weak since 94 out of 102 participants persisted. In RQ2, var 22 negatively predicted creativity and high school GPA positively predicted creativity. There were three factors in RQ3 that affected students’ persistence, which high school GPA, age, and var 24 (being unconventional). Attitudes of being unconventional positively affected persistence. However, age and high school GPA negatively affected persistence. In the RQ3, there were no indirect relationships between creativity and persistence through learning strategies. In the RQ4, var 22 and var 24 (attitudes of being unconventional) negatively predicted learning outcomes. Two control variables, which were high school GPA and daily online activity, positively affected learning outcome. There were two learning strategy items of deep-learning, which were var 6 and var 10 positively affected learning outcomes. Three variables of attitudes of creativity, which were var 4, var 9, var 17, as well as daily online hour, positively affected var 6 of deep-learning strategy. Var 23 of attitudes of creativity positively affected var 10 of learning strategy. Therefore, attitudes of creativity predicted learning outcome and there were indirect relationships between attitudes of creativity and learning outcome through learning strategies in the RQ4. The result confirmed previous studies about the role of creativity among biology students (Muirhead 2007; Richvana, Dwiastuti, and Prayitno 2012; Utami 2010). The result also confirms that attitudes ofcreativity is important for students in online and distance learning. Diversity (variable 4 of rCAB test) predicted students’ learning outcome. However, this study did not show that control variables predicted students learning outcome, except for high

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school GPAs. Therefore, further studies regarding effect of diversity on students’ learning outcome at UT is necessary. Variable 17 (There is a clear benefit when one look for ideas that others will not even consider) of the attitudes of creativity positively predicted variable 10 (When I'm working on a new topic, I try to see in my own mind how all the ideas fit together) of deep- learning strategy. Var 17 was also focused on originality. Therefore, students who valued originality were more likely to study with deep-learning strategy. Another indicative item of attitudes of creativity that predicted learning outcomes was variable 22 (“work can be fun if we face projects as if they were games”). The negative value of the variable means that students thought that seriousness was important in their study. The seriousness was supported by significance of daily online hour. Attitudes of originality (variable 23 of rCAB test) significantly predicted learning outcome. The variable had a positive relationship with learning outcome. Students who valued originality higher were more likely to succeed. Variable 24 of the rCAB creativity test, (“being unconventional”), was a predictor of persistence as well as learning outcomes. The study showed that students who behaved unconventionally were likely to be more persistent than those who behaved conventionally. On the other hand, this study showed that being unconventional had a negative value for predicting learning outcomes. A control variable that predicted learning outcomes was high school GPA. The significance of high school GPAs required further confirmation, since previous studies (Csikszentmihalyi 1996; Guilford 1950; Sawyer 2006) had shown there to be no direct

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relationship between intelligence and creativity. On the other hand, the RQ 3 showed that high school GPA negatively predicted persistence. Age negatively predicted persistence. Younger students were more likely to persist than older students. Younger students may be more persistent since they have the benefit of their fluid intelligence (Csikszentmihalyi, 1996). Learning strategies predicted learning outcomes. Two items of ASSIST test for learning approaches that significantly predicted learning outcomes were parts of deep-learning. The results confirms that students with deep-learning strategy were more likely to have higher learning outcomes (Gadelrab, 2011; Thang, 2011). This study confirmed that attitudes of creativity positively predicted learning persistence. This predictor was variable 24. The result of this study aligned with Runco (2004), who posited that creativity is necessary to transform knowledge during learning. Since attitudes of creativity predicted learning outcomes, there should be more studies about how to increase the creativity of UT students. The effort to increase students’ creativity requires more studies on other elements of creativity, such as problem finding, idea generation, analogy, and idea testing. Future studies of these elements of attitudes of creativity will improve understanding of creativity in biology students. There is need to study how students learn in online learning. The study would provide more information about how students apply creativity in their learning process. An idea for further study is to use qualitative methodology. This study showed that UT might conduct training to improve students’ creativity as a means for improving students’ persistence and learning outcomes. Such an effort would require preparation by the university organization. The units that need preparation include the course

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team, learning support team, and the online tutors. They need training in the paradigm of creativity and how elements of this paradigm may be implemented in online learning. The preparation may include the application of Web 2.0 technology that will enable students more flexibility to produce and reuse content.

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REFERENCES: Abd-Allah, U. F. (2006). Innovation and creativity in Islam. Retrieved from: http://www.nawawi.org/ Al-karasneh, S. M., & Saleh, A. M. J. (2010). Islamic perspective of creativity: A model for teachers of social studies as leaders. Procedia-Social and behavioral sciences, 2(2), 412426. Acar, S., & Runco, M. A. (2015). Thinking in multiple directions: Hyperspace categories in divergent thinking. Psychology of Aesthetics, Creativity, and the Arts, 9(1). 41-53. Alias S. B. (2013). Effectiveness of Facebook based learning to enhance creativity among Islamic studies students by employing Isman Instructional design model. TOJET: The Turkish Online Journal of Educational Technology – January 2013. 12(1).. 60-67. Amabile, T. M. (1983). The Social Psychology of Creativity. Springer Series in Social Psychology. New York, NY: Springer-Verlag. Amhag, L. (2013). Creativity in and between collaborative peer-assessment in higher distance education. Creativity Education, 4(70. 94-104 Anderson, T. A., & Dron, J. (2011). Three generations of distance education pedagogy. Review of Research in Open and Distance Learning. 12(3). 80-97 Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008. Needham, MA: The Sloan Consortium. Atkinson, S. (2004). A comparison of the relationship between creativity, learning style preference and achievement at GCSE and degree level in the context of design and technology project work. Loughborough, UK: Loughborough University.

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Belawati, T. (1998). Mediated counseling services: An effort to increase student’s persistence in distance education. Jakarta: Universitas Terbuka. Beghetto, R. A., & Kaufman, J. C. (2007). Toward a broader conception of creativity: A case for “mini-C” creativity. Psychology of Aesthetics: Creativity and the Arts, 2(1). 73-79 Boyd, G. (1993). A theory of distance education for the cyberspace era. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 201-218). New York: Routledge. Catropa, D. (2013, February 24). Big (MOOC) data. Inside higher education. Retrieved from http://www.insidehighered.com/blogs/stratedgy/big-mooc-data. Cayirdag, N. (2011). Attribution and creativity. In M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp. 96-100). San Diego: Academic. Chibaz-Ortiz, F., Borroto-Carmona, G., & De-Almeida-Santos, F. (2014). Managing creativity in collaborative learning environments: A DL project. Media Education Research Journal. 22(43). 143-151. Cimer, A.(2012). What makes biology learning difficult and effective: Students’ views. Educational Research and Reviews, 7(3). 61-71 Csiskszentmihalyi, M. (1994). The domain of creativity. In D. H. Feldman, M Csikszentmihalyi, & H. Gardner (Eds.), Changing the world: A framework for the study of creativity (pp. 135-158). Westport, CT: Praeger. Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. 1st Ed. New York: Harper Perennials. Cohen, L. M. (2011). Adaptation, adaptiveness, and creativity. In M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp. 9-17). San Diego: Academic.

110

Daniel, J. S. (1996). Mega-universities and knowledge media: Technology strategies for higher education.1st Ed. London : Kogan Page. Daniel, J. S. (2013). A guide to quality in online learning. Academic Partnership. Retrieved from http://www.academicpartnerships.com/sites/default/files/A%20Guide%20to%20Quality %20in%20Online%20Learning.pdf De Haan, R. L. (2009). Teaching creativity and inventive problem solving in science. CBE—Life Sciences Education, 8(Fall). 172-181 Diki, D. (2013). Creativity for learning biology in higher education. LUX: A journal of transdisciplinary writing and research from Claremont Graduate University, 3(1). 1-11 Dow, G. T., & Mayer, R. E. (2004). Teaching students to solve insight problems: Evidence for domain specificity in creativity training. Creativity Research Journal, 16(4). De Dreu, C. K., Bechtoldt, M. N., & Nijstad, B. A. (2006). Team personality diversity, group creativity, and innovativeness in organizational teams. Available from Internet: http://www. susdiv. org/uploadfiles/RT3. 2_PP_Carsten. pdf. Dunbar, K. (1997). How scientists think: Online creativity and conceptual change in science. In T. B. Ward, S. M. Smith, & S. Vaid (Eds.), Conceptual structures and processes: Emergence, discovery, and change (pp. 461-493). Washington, DC: APA Press. Entwistle, N., Tait, H., & McCune, V.(2000). Patterns of response to an approaches to studying inventory across contrasting groups and contexts. European Journal of Psychology of Education, 15(1).33-48 Fasko, D. (2001). Education and creativity. Creativity Research Journal, 13(3).317-327 Field, A. (2009). Discovering statistics using SPSS. 3rd eds. New York, NY: Sage publications.

111

Fischer G. & Sugimoto M. (2006) Supporting self-directed learners and learning communities with sociotechnical environments. International Journal of Research and Practice in Technology Enhanced Learning (RPTEL) 1, 31–64. Franklin, S., & Peat, M. (1998). Online learning; the first year biology way. In Proceedings of ASCILITE Conference (pp. 241-249). Friedel, C. R., & Rudd, R. D. (2006). Creative thinking styles of undergraduate agriculture students. Journal of Agricultural Education, 47(4). 199-211. Goodyear, P., Jones, C., & Thompson, K.(2014). Computer-supported collaborative learning: Instructional approaches, group process and educational designs. In: Handbook of Research on Educational Communications and Technology. J. M. Spector et al. (Eds). 4th Ed.(pp439-451) New York, NY: Springer. Girvina, M. V. (2013). Investigating leadership characteristics and attitudes toward creativity according to agency context for agriculture extension agents in Uruguay. (Unpublished doctoral dissertation). Oklahoma State University, Stillwater, OK. Guilford, J. P. (1950). Creativity research: Past, present and future. American Psychologist, 5(9). 33-65 Hamm, S., & Robertson, I. (2010). Preferences for deep-surface learning: A vocational education case study using a multimedia assessment activity. Australasian Journal of Educational Technology, 26(7). 951-965 Hewindati, Y. T., & Zuhairi, A. (2009). Conducting biological science practicum at a distance at Universitas Terbuka, Indonesia. AAOU Journal, 4(1). 47-58. Haamboaka, C. (2007). Nature and causes of learning difficulties in genetics at high school level in Zambia. Journal of International Development and Cooperation, 13(1). 1-9

112

Holmberg, B. (2005). Theory and practice of distance education (2nd ed.). New York: Routledge. Horng, J., Hong, J., Chanlin, L., Chang, S., Chu. H. (2005). Creative teachers and creative teaching strategies. International Journal of Consumer Studies, 29(4).352-358 Hristovski, D., Passos, A. (2012). Sport Performance as a Domain of Creative Problem Solving for Self-Organizing Performer-Environment Systems. The Open Sports Sciences Journal, 2012, 5, (Suppl 1-M4) 26-35 Hsu, Y. C., Ching, Y. H. & Grabowski, B. L. Web 2.0 applications and practices for learning through collaboration. In: Handbook of Research on Educational Communications and Technology. J. M. Spector et al. (Eds). 4th Ed. (pp.747-758).New York, NY: Springer. International Monetary Fund. (2013). World Economic Outlook Database, October 2013. Retrieved from https://www.imf.org/external/pubs/ft/weo/2013/02/weodata/index.aspx Jacobson, K. G. (2000). Central tensions: A critical framework for examining high school mathematics and mathematics education. Paper presented at the Annual Meeting of the American Educational Research Association. New Orleans, LA, April 24-28, 2000 Kachigan, S. M. (1991). Multivariate statistical analysis: A conceptual introduction (2nd ed.). New York: Radius Press. Kaufmann, G. ( 2003). Expanding the Mood–Creativity Equation. Creativity Research Journal. Vol. 15. Nos 2 & 3. 131-135 Kaufmann, G. &Vosburg, S. K. (1997), Paradoxical' Mood Effects on Creative Problemsolving, Cognition and Emotion, Vol 11(2), 151-170. Kelly, H. A. (2014). Path analysis of educator perceptions of open educational resources using the technology acceptance model. International Review of Research in Open and Distance Learning, 15(2).26-42.

113

Khaleefa, O. H., Erdos, G., & Ashria, I. H. (1996). Creativity in an Indigenous Afro‐Arab Islamic Culture: The Case of Sudan. The Journal of Creative Behavior, 30(4), 268-282. Kharkurin, A. V., & Motalleebi, W. S. N. S. (2008). The impact of culture on the creative potential of American, Russian, and Iranian college students. Creativity Research Journal. Vol. 20. No. 4. 404-411. Kim, S. H., Vincent, L. C., & Goncalo, J. A. (2012). Outside advantage: Can social rejection fuel creative thought? Journal of Experimental Psychology: General, 142(3), 605-611 Kubika-Sebitosi, E. (2007). Understanding genetics and inheritance in rural schools. Educational Research, 41(2). 56-61 Knowles, M. S., Holton, E. F., & Swanson, R. A. (1998). The Adult learner: The definitive classic in adult education and human resources development (5th ed.), Houston: Gulf Publishing Co. Kovačić, A., Bubaš, G., & Orehovački, T. (2012). Thinking outside the task: Learner autonomy and creativity in Web 2.0-based dialogic spaces. na. Retrieved from: http://bib.irb.hr/datoteka/588031.Kovacic_Bubas_Orehovacki.pdf. Lan, L. & Kaufman, J. C. (2012). American and Chinese Similarities and Differences in Defining and Valuing Creative Products. The Journal of Creative Behavior. 46(4). Lawson A. E. (2001). Promoting creative and critical thinking skills in college biology. Bioscience, 27(1). 13-24. Lazonder, A. W. Inquiry learning. In: Handbook of Research on Educational Communications and Technology. J. M. Spector et al. (Eds). 4th Ed.(pp453-464) New York, NY: Springer.

114

Lebedeva, N., & Schmidt, p. (2012). Values and attitudes towards innovation among Canadian, Chinese and Russian students. HSE (Sociology) Working Paper. Retrieved from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2008139 Licuanan, B. F., Dailey, L. R. and Mumford, M. D. (2007). Idea evaluation: Error in evaluating highly original ideas Journal of Creative Behavior. 41(1).7-33. Lin, Y. S. (2011). Fostering creativity through education–a conceptual framework of creative pedagogy. Creative education, 2(03), 149. Lones, P. S. (1999). Learning as creativity: implications for adult learners. Adult Learning, 11(4).9-12 Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. 4th ed. Marwah, NJ : Routledge. Luschei, T. F., Dimyati, S., Padmo, D., (2008). Maintaining e3-learning while transitioning to online instruction: The case of the Open University of Indonesia. Distance Education, 29(2). 165-174. Maas, W. (2009). Creativity in Biological Research. Perspectives in biology and medicine, 52(4), 579-584. Martin, F., Saljo, R. (1976). On qualitative differences in learning: Outcome and process. British Journal of Educational Psychology, 46(1)4-11. Martins, L.L.. & Shalley, C.E. (2011). Creativity in virtual work: Effects of demographic differences, Small Group Research, 42, 536–61. Mintu-Wimsatt, A., Sadler, T., & Ingram, K. (2007). Creativity in online courses: Perceptions of MBA students. MERLOT Journal of Online Learning and Teaching, 3(4). 324-330. Mishra P., Fahnoe C., Henriksen D. (2013). Creativity, self-directed learning and the

115

architecture of technology rich environments. TechTrends. January/February 2013. 57(1).10-13 Moore, M. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 20-35). New York: Routledge. Moore, M. G., & Kearsley, G. (2012). Distance education: A system view of online learning (3rd ed.). Belmont, CA: Wadsworth. Mouchiroud, C. & Lubart, T. (2006). Past, Present, and Future Perspectives on Creativity in France and French-Speaking Switzerland. In: . In: The International Handbook of Creativity. J. C Kaufman & R. J. Sternberg (Eds). (pp96-123). New York, NY: Cambridge University Press. Mumford, M. D., Antes, A. L., Caughron, J. J., Connelly, S., & Beeler, C. (2010). Cross-field differences in creative problem-solving skills: A comparison of health, biological, and social sciences. Creativity research journal, 22(1), 14-26. Mumford, M. D., Reiter-Palmon, R., & Redmond, M. R. (1994). Problem construction and cognition: Applying problem representations in ill-defined domains. Mumford, M. D., & Vessey, W. B. (2011). Heuristics: Strategies in Creative Problem Solving. In: M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp. 601-607). San Diego: Academics. Necka, E., Grohman, M., and Slabosz, A. (2006). Creativity studies in Poland. In: The International Handbook of Creativity. J. C Kaufman & R. J. Sternberg (Eds). (pp.270306). New York, NY: Cambridge University Press.

116

Nijstad B. A., De Dreu, C. K. W., Rietzschel, E. F., & Baas, M. (2010). The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence. European Review of Social Psychology, 21(1). 34-77. Nugraini, S. H., Choo, K. A., Hin, H. S., Hoon, T. S. Impact of E-AV biology website for learning about renewable energy. The Turkish Online Journal of Educational Technology. 12(2). 376-386. Oades-Sese, G. V. & Esquivel, G. B., (2011). Cultural Diversity and Creativity. In M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp. 335-341). San Diego: Academic. Organization of Economic Cooperation and Development (OECD). (2014). Country Classification for aid and repayment terms. Retrieved from http://www.oecd.org /tad/xcred/country-classification.htm. O’Quin, K.& Derk, P. (2011). Humor and creativity. In M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp. 628-635). San Diego: Academic. Oztap, H., Ozay, E., & Oztap, F. (2003). Teaching cell division to secondary school students: An investigation of difficulties experienced by Turkish teachers: Case studies. Journal of Biological Education, 38(1), 13-15. Paulus, P.B., Kohn, N. & Dzindolet, M. (2011). Teams. In: M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp. 446-452). San Diego: Academics. Pearce, H. (2013). Encouraging creativity in college students as a skill for the 21st century. Literacy Information and Computer Education Journal, 4(1). 963-972 Peters, O. (2010). Distance education in transition: New trends and challenges. Oldenburg, DE: BIS Verlag.

117

Pishgadham, R., Khodadady, E., Zabihi, R. (2011). Learner creativity in foreign language achievement. European Journal of Educational Studies, 3(3). 465-473. Population Reference Bureau. (2012). Population and Economic Development. Retrieved from www.prb.org Ratnaningsih, D. J., Saefudin, A., Wijayanto, H. (2007). Analysis of students’ retention in distance education: Case study of the department of economics, Indonesia Open University. Jurnal Universitas Terbuka, 9(2). Ratnaningsih, D. J. (2013). Open and distance education systems: Do they enhance graduates’ soft skills? The results from 2009 Universitas Terbuka Tracer Study. Open Praxis, 5(4). 289-299 Ricahrdson, J. T. E. (2003). Students’ perceptions of academic quality and approaches to studying in distance education. British Education Research Journal. 31(1).7-27. Richvana, A., Dwisastuti, S., Prayitno, B. A. (2012). Influence of learning model group investigation to biology results study contemplation from the level creativity at student class X SMAN 2 Karanganyar. Jurnal Pendidikan Biologi. 4(1).1-14. Rietzschel, E. F., Nijstad, B. A., & Stroebe, W. (2010). The selection of creative ideas after individual idea generation: Choosing between creativity and impact. British journal of psychology, 101(1), 47-68. Runco, M. A. (2004). Everyone has creative potential. In R. J. Sternberg, E. L. Grigorenko, & J. L. Singer (Eds.), Creativity: From potential to realization (pp. 21-30). Washington, DC: American Psychological Association. Runco, M. A. (2007). Creativity: Theories and themes: Research, Development, and Practice. Burlington, MA: Elsevier.

118

Runco, M. A., & Chand, I. (1995). Creativity and Cognition. Educational Psychology, 7(3).243-267 Runco, M. A. (2012). Attitudes and ValuesToward Creativity.Creativity Testing Services. (rCAB)©. Retrieved from http://creativitytesting services.com Runco, M. A., Illies, J. J., & Eisenman, R. (2005). Creativity, originality,and appropriateness: What do explicit instructions tell us about their relationships? The Journal of Creative Behavior, 39(2), 137-148. Sharan, Y., & Sharan, S. (1990). Group investigation expands cooperative learning. Educational leadership, 47(4), 17-21. Shelmet, M., Shields, C., & Huggins, J. (2008). BIO 151: Applied biology-developing creative learning partnership with Blackboard vista. MERLOT Journal of Online Learning and Teaching, 4(1).128-137. Shen, J., Lei, J., Chang, H., and Namdar, B. (2014). Technology-enhanced, modellig-based instruction (TMBI) in science education. In: Handbook of Research on Educational Communications and Technology. J. M. Spector et al. (Eds). 4th Ed.(pp529-540). New York, NY: Springer. Smarabawa.I, Arnyana, I., Setiawan. (2013). Pengaruh model pembelajaran Sains Teknologi Masyarakat terhadappemahaman konsep biologi dan ketrampilan berpikir kreatif siswa SMA (The effect of Science/Technology/Society toward the understanding of biological concept by high school students). Journal Program Pascasarjana Universitas Pendidikan Ganesha Program Studi IPA. 3(1). Retrieved from: http://id.portalgaruda.org/?ref=browse&mod=viewarticle&article=259420

119

Son, M. J. (2012). A study of Korean students’ creativity in science using structural equation modeling. PhD dissertation. University of Arizona. Speth, C. A., Namur, D. M., & Lee, D. J. ( 2007). Using the ASSIST short form for evaluating an information technology application: Validity and reliability issues. Informing Science Journal, 10. 107-119 Speth, C. A., & Lee, D. J. (2013). Insights from using the Approaches to Studying as an evaluation tool for educational resource development. Global Journal of Human Social Science, 13(9).32-40 Sulaiman, F. (2013). The effectiveness of PBL online on physics students’ creativity and critical thinking: A case study at Universiti Malaysia Sabah. International Journal of Education and Research. Vol. 1. No. 3.1-18 Sullivan, F. R. (2011). Serious and Playful Inquiry: Epistemological Aspects of Collaborative Creativity. Educational Technology& Society, 14 (1), 55–65. Tait, H., Entwistle, N., & McCune, V. (1998). ASSIST: A reconceptualization of the Approaches to Studying Inventory. In C. Rust (Ed.), Improving Student Learning: Improving Students as Learners (pp. 262-271). Oxford, UK: Oxford Brookes University. Tapilow, F. S., & Juanda, E. (2009). How interactive multimedia affected students’ cognition in learning biology at the middle and higher education level? Paper given at International Conference on Rural Information and Communication Technology. June 17-18, 2009. Bandung, Indonesia. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s Alpha. International Journal of Medical Education, 2. 53-55

120

Taylor, J. (2001). Fifth generation distance education. Retrieved from http://www.detya.gov.au /highered/hes.htm The World Bank. (2013). Gini Index. Retrieved from http://data.worldbank.org/indicator /SI.POV.GINI/ Torrance, E. P. (1995). Insights about creativity: Questioned, rejected, ridiculed, ignored. Educational Psychology Review, 7(3). 313-322. Utami. R. P., Noviar, D., Agustina, E. H. (2010). Aplikasi model VCT (Value Clarification Technique) berbasis local wisdom sebagai upaya internalisasi pendidikan karaketer untuk meningkatkan kreatiitas berpikir dan hasil belajar biologi siswa. In Seminar Nasional VII Pendidikan Biologi. Yogyakarta. 9(1).105-111. Villalba, E. (2011). Critical thinking. In M. A. Runco & R. S. Pritzker (Eds.), Encyclopedia of creativity (pp.323-325). San Diego: Academic. Vygotsky, L. S. (2004). Imagination and creativity in childhood: Chapter 1. Creativity and imagination. Journal of Russian and East European Psychology, 42(1).7-97 Wang, H C., Fussell, S. R., & Cosley, D. (2011 March 19-23). From diversity to creativity: Stimulating group brainstorming with cultural differences and conversationally-retrieved pictures. Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing, Hangzhou, China. Widhiyantoro, T., Indrowati, M., and Probosari, R.M. (2012). The effectiveness of guided discovery method application toward creative thinking skill at the tenth grade students of SMAN 1 Teras Boyolali in the academic year 2011/2012. Jurnal Pendidikan Biologi. 4(3). 89-99.

121

Wolpert, L. (1996). Creativity in Biology: A major case and a minor case. Creativity Research Journal. 2(2 & 3). 233-237. Yager Robert E.,Choi AeRan & Akcay H.2009. Comparing Science Learning Among 4th-, 5th-, and 6th- Grade Students: STS Versus Texbook-Based Instruction. Journal of Elementary Science Education.21(2). 15-24. Ziegler, R., Diehl, M., & Zijlstra, G. (2000). Idea Production in nominal and virtual groups: Does computer-mediated communication improve group brainstorming? Group Processes and Intergroup relations, 3(2), 141-158.

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Appendix A. List of instruments rCAB Attitudes and Values Test

Runco (2012)

Learning Strategy

Speth et al. (2007)

Demographic data

Information about age, ethnicity, yearly family income, high school GPA, and daily online activities

Appendix B: Attitudes and Values Part of the Runco Creativity Assessment Battery (rCAB)© 2012 Directions: Use the a-d scale (given below) to indicate how much you agree or disagree with a certain statement. You may need to approximate. Please indicate how you really think and behave, not how you would like to. Remember--no names are used. Your responses are confidential. Again, you may need to approximate. For each item, circle the response option that is THE CLOSEST to being accurate. Here are the options: (a) = totally DISAGREE (b) = mostly disagree (c) = mostly agree (d) = totally AGREE To what degree do you agree with each of the following? 1. Even if some method has worked well in the past, it is a good idea to question and perhaps change it on a regular basis. 2. One of the advantages of developing expertise is that you can make useful assumptions and

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work more quickly. 3. Time is often wasted when everyone involved in a project shares each of his or her own ideas. 4. Diversity is a good thing to have in an organization that wants to be innovative. 5. When solving problems it is often beneficial to postpone judgment about possible solutions. 6. Maybe it is good for mad scientist to be strange, but for the rest of us its best to go along with the crowd. 7. Solutions and ideas are often improved by considering a variety of perspectives. 8. It isn't enough to just find an original idea. That idea is only worth something if you test it, verify it, implement it. 9. If you produce a large number of ideas, you are likely to find some high quality ideas and solutions. 10. Problem solving and innovation benefit from shifts in perspective. 11. It can be useful to collect data and obtain new information before solving a problem. 12. Any group work, and all projects should have a person of authority who constantly insures that no time is wasted exploring every option. 13. It is best to stick with a “tried and true” approach to innovation, once you find something that works. 14. Good insights often result from concentrating on a problem. It is best not to take time off when immersed in a project. 15. I look for ways to isolate myself so I can concentrate and think deeply about my work. 16. Useful ideas can often be found if you change the problem; don’t just look for solutions to the problem as it is presented.

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17. There is clear benefit to thinking about ideas that other people will not consider. 18. I avoid working outside my area of expertise. I do not want to be a beginner again and again. 19. The important thing at work is to find out what will gain the approval of other people (supervisors, co-workers, clients). 20. It is useful to tolerate people who have different views, even if we are trying to solve a particular problem. 21. It is difficult for me to work with people who have very different backgrounds. 22. Work can be fun if you approach projects playfully, like they are games and have fun. 23. Originality can be very useful at work or in school. 24. Sometimes it is best to be unconventional. 25. I am tolerant of people who are different, bohemian, contrarian, odd.

Appendix C. Learning strategy test (Speth et. al, 2007).

1. Often I find myself wondering if the work I am doing here is really worthwhile. 2. When I'm reading an article or book, I try to find out for myself exactly what the author means. 3. I organize my study time carefully to make the best use of it. 4. I concentrate on learning just those bits of information I have to know to pass. 5. I look carefully at instructor's comments to see how to get higher grades next 6. Regularly, I find myself thinking about ideas from lectures when I'm doing other things. 7. I'm pretty good at getting down to schoolwork whenever I need to. 8. Much of what I'm studying makes little sense. It's like unrelated bits and pieces. 9. I put a lot of effort into studying because I'm determined to do well.

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10. When I'm working on a new topic, I try to see in my own mind how all the ideas fit together. 11. I don't find it at all difficult to motivate myself. 12. Often I find myself questioning things I hear in lectures or read in books. 13. I manage to find conditions for studying which allow me to get on with my work easily. 14. Often I feel I'm drowning in the sheer amount of material we have to deal with. 15. Ideas in course books or articles often set me off on long chains of thought of my own. 16. I often worry about whether I'll ever be able to cope with the school work properly. 17. When I read, I examine the details carefully to see how they fit in with what's being said. 18. I often have trouble in making sense of the things I have to remember.

Appendix D. Demographic information No.

Activity

1

My daily online learning activities are

2

My age is:

3

My yearly family income is

Male

Female

4 hours

< 21

21-25

26-30

>30

< $5000

$5000 – $10,000

$ 10,100 – $15,000

5. My total high school GPA is …….. 6. My ethnicity is…. 7. Explain why creativity is required in the online tutorial ………. 8. Explain how the online tutorial helps you to improve your creativity

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