國立臺灣師範大學博碩士論文全文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	鄭嘉惠 Cheng, Chia-Hui
論文名稱：	從認知歷程角度探討學生線上學習以及論證表現 Moving from Performance-centered Study to Cognitive and Processing Analysis on Students’ Online Learning and Argument Performances
指導教授：	楊芳瑩 Yang, Fang-Ying
學位類別：	博士 Doctor
系所名稱：	科學教育研究所 Graduate Institute of Science Education
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	140
中文關鍵詞：	科學教育、社會性科學議題、論證表現、科學認識觀、眼球追蹤技術
英文關鍵詞：	science education, socio-scientific issues, argument performance, epistemic beliefs in science, eye tracking method
DOI URL：	http://doi.org/10.6345/NTNU202000939
論文種類：	學術論文
相關次數：	點閱：182 下載：14
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

本論文的主要目標為探討在社會性科學議題的網路課程中，學生的學習歷程與論證表現。本論文根據研究架構分為四個不同的研究議題，第一個研究為模型建立的理論研究，以Mplus7檢驗三種不同科學認識觀的結構模型，了解三種科學認識觀所包含各面向之間的關連性及預測結構。第二個研究為研究方法的回顧性研究，探討當前眼球追蹤技術應用於科學教育研究的趨勢，然後根據文獻回顧的結果，提出未來運用眼球追蹤技術於科學教育中的研究架構，並根據所提出的建議設計後續的研究內容。第三個研究為實徵研究，探討大學生科學認識觀與論證結構的學習歷程與理解之關係。研究結果指出，當大學生持有越高程度的「確定性」信念時，會對於論證結構付出較少的認知注意力，導致較差的學習表現。第四個研究亦為實徵研究，欲了解融入社會性科學議題的生物醫學網路課程中，大學生的認知學習歷程、論證表現以及個人因素(例如：先備知識、科學認識觀、網路搜尋行為等)對於學習的影響。研究結果發現，學生的認知學習歷程以及論證表現和個人因素皆有其相關性存在。各研究結果皆支持此論文的研究架構，且根據研究結果提出教育上的意涵以及未來研究建議。

The ultimate goal of this thesis was to investigate how students reason and evaluate a biomedical socio-scientific issue (SSI). To reach the goal, we prepared several studies that were theoretically, methodologically and empirically related. Previous studies have indicated that some psychological factors may affect students’ cognitive process during learning and their argument performances. Among these factors, epistemic beliefs in science have been frequently mentioned and discussed. In literature, various forms of epistemic beliefs related to reasoning can be found. However, the associations between different epistemic beliefs have not been thoroughly examined. In study 1 (Chapter 2), we examined the associations among beliefs about the nature of knowledge, beliefs about the justification for knowing in science and Internet-specific justification, and then tested a structural model of these epistemic beliefs . In this thesis, a key method for empirical studies was the eye tracking method. Although the eye tracking method has been used by psychological and educational researchers, how this method can be applied specifically to investigate processes of science learning has not been systemically examined. Therefore, the second study of this thesis (presented in Chapter 3) was a methodologically literature review to analyze the research issue, research design and learning dimensions of studies in science education, which apply the eye tracking method. Based on the review result, we applied an inherent eye tracking design to explore information processing behaviors associated with the learning activities involved in the thesis research. Given that the ultimate goal of the study was related to the practice of argumentative reasoning on a SSI, it was hypothesized that the personal epistemic beliefs in science should interact with the understanding about the argument structure. We conducted empirical studies to test the interactions. Accordingly, in Study 3 (Chapter 4), the associations among different types of epistemic beliefs in science, learning of argument structure and understanding of the argument structure were analyzed. At last, another empirical study as presented in Study 4 (Chapter 5) was designed to investigate how students reasoned about a biomedical issue involved in the study. Factors explored in Study 1-3 were taken into consideration in the design. In Study 4, an online learning environment was created first, which allowed students to learn basic scientific knowledge, read the socio-scientific issue with selected articles, search related information through the Internet, and present their opinions. University learners were asked to learn and evaluate the biomedical issue discussed in the study in the online learning environment. Afterwards, we examined the effects of epistemic beliefs, students’ information processing behaviors during the online activities and the uses of argument components in the context of the biomedical issue. The result showed that students’ attention to the online SSI lesson and the web search result were positively correlated with the change in argument performance. Especially, attention to warrant for the opposing opinion positively predicted the change. Interactions among argument performances, visual attention during learning and epistemic beliefs in science were found. Based on the study results as presented in Chapter 2 to 5, suggestions for future research and implications for science education were provided in Chapter 6.

Acknowledgement ⅰ
摘要  ⅱ
Abstract  ⅲ
Table of Contents  ⅴ
List of Tables  ⅹ
List of Figures  ⅻ

Chapter 1:  Introduction  1
 Background  1
 Thesis organization  7
References  9

Chapter 2:  Investigating Structural Relationships among Senior High School Students’ Beliefs about Knowledge, Justification for Knowing, and Internet-Specific Justification in the Domain of Science     12
Abstract  12
 Introduction  13
1  Scientific epistemic beliefs  13
2  Justification for knowing in science  15
3  Internet-specific epistemic justification  16
 Method  17
1  Participants  17
2  Instruments  18
2.1  Beliefs about the nature of knowledge in science  18
2.2  Beliefs about nature of knowing in science  19
2.3  Beliefs about Internet-specific epistemic justification in science  20
3  Procedure  21
4  Analytical approach  21
 Result  23
1  Preliminary analyses  23
2  Hypothesized model testing  25
3  Mediation testing  26
 Discussion  28
References  30
Appendix A.  Beliefs about the Nature of Scientific Knowledge in Science  34
Appendix B.  Beliefs about Justification for Knowing in Science  35
Appendix C.  Internet-specific Justification  36

Chapter 3:  A Review on the Designs of Eye-Tracking Studies in Science Education  37
Abstract  37
 Introduction  38
 Method  40
 Results  43
1  Research issues  43
2  Research design  46
2.1  Participants  47
2.2  Learning materials  48
2.3  Science subjects  49
2.4  Performance measures  49
2.5  The sampling rates of the eye tracking systems  50
2.6  Eye movement measures  50
2.7  Learning dimensions  51
 Discussion  53
1  The collateral studies  53
2  The embedded studies  57
3  The inherent studies  60
 Suggestions for future research  64
 Research design of the study  65
References  66
Articles included in the review analysis  67
Appendix A.  Analysis of selected articles in the reviewed study  71

Chapter 4: Examining Relationships among Epistemic Beliefs in Science, Learning of Argument structure and Understanding of the Argument Structure  77
Abstract  77
 Introduction  78
1  Argumentation in science education  79
2  Assessing recognition of argument structure by the eye tracking method  80
3  Epistemic beliefs and argumentation  81
 Method  83
1  Participants  83
2  Materials and equipment  83
2.1  Beliefs about the nature of knowledge and justification for knowing in science  83
2.2  Materials used in the argument reading treatment  84
2.3  Tracking visual attention pattern  85
2.4  The identification tests for argument components  86
3  Procedure  87
4  Data analyses  88
 Result  88
1  Descriptive and correlational analyses for performances of pre- and post-tests, personal epistemic beliefs in science and eye movement measures during the argument reading treatment  88
2  Comparison between students who improved and did not improve their identification tests for the argument components  91
3  Partial correlation analysis between eye movement measures and scores in post-test  96
 Discussion  98
References  100

Chapter 5:  University Students’ Visual Attention during Learning and Performance of an Online Argument-based Bio-medical Lesson  103
Abstract  103
 Introduction  104
 Method  107
1  Participants  107
2  Online argument-based bio-medical lesson  107
3  Data collection  108
3.1  Questionnaires to assess epistemic beliefs in science  108
3.2  Tests for prior knowledge (pre-test) and learning achievement (post-test)  108
3.3  Web search result  109
3.4  Tracking visual attention  109
3.5  Use of argument components  110
4  Procedure  111
5  Data analyses  112
 Result  112
1  First research question: What were the patterns of students’ visual attention during the online learning?  112
2  Second research question: Before and after the online search, how did students use the argument components to reason about the study issue? Would the online search task affect the argument performance?  113
3  Third research question: How were individual factors, including performance on prior knowledge test and learning achievement, epistemic beliefs in science, and number of searched websites, related to the argument performances?  116
4  Fourth research question: How did learners’ visual attention during learning in the online lesson interact with the uses of argument components?  117
5  Fifth research question: What were the relationships between change in the uses of argument components and measured variables?  121
6  Sixth research question: Which measured variables could predict the change in the argument performance?  121
7  Seventh research question: What are the relations between learners’ epistemic beliefs and visual attention during learning in the online lesson?  125
 Discussion  127
References  129
Appendix A  The AOIs on reading pages of the Applied Biology lesson  131
Appendix B  The AOIs on the introduction pages for Issue Investigation  135

Chapter 6:  Summary  137
 Conclusion  137
 Suggestions for future research and implications in science education  139
References  140
                                

Chapter1:
Bell, P., & Linn, M.C. (2000). Scientific arguments as learning artifacts: Designing for learning from the web with KIE. International Journal of Science Education, 22, 797-817.
Berland, L., & Reiser, B. (2009). Making sense of argumentation and explanation. Science Education, 93(1), 26-55.
Bråten, I., Brandmo, C., & Kammerer, Y. (2018). A Validation Study of the Internet-Specific Epistemic Justification Inventory With Norwegian Preservice Teachers. Journal of Educational Computing Research, 0(0), 1-24
Bråten, I., Strømsø, H. I., & Samuelstuen, M. S. (2005). The relationship between Internet-specific epistemological beliefs and learning within Internet technologies. Journal of Educational Computing Research, 33, 141-171.
Chen, Y. C., & Yang, F. Y. (2014). Probing the relationship between process of spatial problems solving and science learning: An eye tracking approach. International Journal of Science & Mathematics Education, 12(3), 579-603.
Chin, C. C., Yang, W. C., & Tuan, H. L. (2016). Argumentation in a socioscientific context and its influence on fundamental and derived science literacies. International Journal of Science and Mathematics Education, 14(4), 603-617.
Chiu, Y. L., Tsai, C. C., & Liang, J. C. (2015). Testing measurement invariance and latent mean differences across gender groups in college students’ Internet-specific epistemic beliefs. Australasian Journal of Educational Technology, 31, 486-499.
Conley, A. M., Pintrich, P. R., Vekiri, I. & Harrison, D. (2004). Changes in epistemological beliefs in elementary science students. Contemporary Educational Psychology, 29(2), 186-204.
Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287-312.
Duschl, R., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38, 39-72.
Erduran, S., Simon, S., & Osborne, J. (2004). TAPping into argumentation: Developments in the application of Toulmin's argument pattern for studying science discourse. Science education, 88(6), 915-933.
Ferguson, L.E., & Bråten, I. (2013). Student profiles of knowledge and epistemic beliefs: Changes and relations to multiple-text comprehension. Learning and Instruction, 25, 49-61.
Greene, J. A., & Yu, S. B. (2015). Educating critical thinkers: The role of epistemic cognition. Policy Insights from the Behavioral and Brain Sciences, 3, 45-53.
Greene, J. A., Azevedo, R., & Torney-Purta, J. (2008). Modeling epistemic and ontological cognition: Philosophical perspectives and methodological directions. Educational Psychologist, 43, 142-160.
Ho, H. Y., Chang, T. L., Lee, T. N., Chou, C. C., Hsiao, S. H., Chen, Y. H., & Lu, Y. L. (2019). Above-and below-average students think differently: Their scientific argumentation patterns. Thinking Skills and Creativity, 34, 100607.
Hofer, B. K. (2000). Dimensionality and disciplinary differences in personal epistemology. Contemporary educational psychology, 25(4), 378-405.
Lai, M. L., Tsai, M. J., Yang, F. Y., Hsu, C. Y., Liu, T. C., Lee, S. W. Y., Lee, M. H., Chiou, G. L., Liang, J. C., & Tsai, C. C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational research review, 10, 90-115.
Larson, A., & Britt, A. (2009). Improving students’ evaluation of informal arguments. The Journal of Experimental Education, 77(4), 339-365.
Lee, W. C., Chiu, Y. L., Liang, J. C., & Tsai, C. C. (2014). Exploring the structural relationships between high school students’ Internet-specific epistemic beliefs and their utilization of online academic help seeking. Computers in Human Behavior, 36, 391-400.
Liang, J. C. & Tsai, C. C. (2010). Relational analysis of college science-major students’ epistemological beliefs toward science and conceptions of learning science. International Journal of Science Education, 32(17), 2273-2289.
Ministry of Education. (2014). The curriculum guidelines of 12-year compulsory education. Taipei: Author.
National Research Council (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
Noroozi, O. (2018). Considering students’ epistemic beliefs to facilitate their argumentative discourse and attitudinal change with a digital dialogue game. Innovations in Education and Teaching International, 55(3), 357-365.
Nussbaum, E.M., & Sinatra, G.M. (2003). Argument and conceptual engagement. Contemporary Educational Psychology, 28, 384-395.
Nussbaum, E. M., Sinatra, G. M., & Poliquin, A. (2008). Role of epistemic beliefs and scientific argumentation in science learning. International Journal of Science Education, 30(15), 1977-1999.
OECD (2013). PISA 2015 Draft Science Framework. OECD Publishing.
OECD (2015). PISA 2018 Draft Global Competence Framework. OECD Publishing.
Oulton, C., Dillon, J., & Grace, M. M. (2004). Reconceptualizing the teaching of controversial issues. International Journal of Science Education, 26(4), 411-423.
Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology, 62(8), 1457-1506.
Sadler, T. D., Romine, W. L., & Topçu, M. S. (2016). Learning science content through socio-scientific issues-based instruction: A multi-level assessment study. International Journal of Science Education, 38(10), 1622-1635.
Sampson, V., Enderle, P., & Grooms, J. (2013). Argumentation in science education. The Science Teacher, 80(5), 30.
Toulmin, S. (1958). The uses of argument. Cambridge: Cambridge University Press.
Tsai, C. Y. (2015). Improving students' PISA scientific competencies through online argumentation. International Journal of Science Education, 37(2), 321-339.
Yang, F. Y. (2017). Examining the reasoning of conflicting science information from the information processing perspective—an eye movement analysis. Journal of Research in Science Teaching, 54(10), 1347-1372
Yang, F. Y., & Tsai, C. C. (2010). Reasoning about science-related uncertain issues and epistemological perspectives among children. Instructional Science, 38, 325-354.
Yang, F. Y., Bhagat, K. K., & Cheng, C. H. (2019). Associations of epistemic beliefs in science and scientific reasoning in university students from Taiwan and India. International Journal of Science Education, 41(10), 1347-1365.
Yang, F. Y., Chen, Y. H., & Tsai, M. J. (2013). How university students evaluate online information about a socio-scientific issue and the relationship with their epistemic beliefs. Journal of Educational Technology & Society, 16(3), 385-399.
Yang, F. Y., Huang, R. T., & Tsai, C.C. (2016). The effects of epistemic beliefs in science and gender difference on university students’ science-text reading: An eye-tracking study. International Journal of Science and Mathematics Eduction, 14, 473-498.

Chapter2:
Abd-El-Khalick, F. (2013). Teaching with and about nature of science, and science teacher knowledge domains. Science & Education, 22(9), 2087-2107.
Bentler, P. M. (1990). Comparative ﬁt indexes in structural models. Psychological Bulletin, 107, 238-246.
Brandmo, C., & Bråten, I. (2018). Investigating relations between beliefs about justification for knowing, interest, and knowledge across two socio-scientific topics. Learning and Indiidual Differences, 62, 89-97.
Bråten, I., Brandmo, C., & Kammerer, Y. (2018). A Validation Study of the Internet-Specific Epistemic Justification Inventory With Norwegian Preservice Teachers. Journal of Educational Computing Research, 0(0), 1-24.
Bråten, I., Ferguson, L. E., Strømsø, H. I., & Anmarkrud, Ø. (2013). Justification beliefs and multiple documents comprehension. European Journal of Psychology of Education, 28(3), 879-902.
Bråten, I., Ferguson, L. E., Strømsø, H. I., & Anmarkrud, Ø. (2014). Students working with multiple conflicting documents on a scientific issue: Relations between epistemic cognition while reading and sourcing and argumentation in essays. British Journal of Educational Psychology, 84(1), 58-85.
Bråten, I., Strømsø, H. I., & Samuelstuen, M. S. (2005). The relationship between Internet-specific epistemological beliefs and learning within Internet technologies. Journal of Educational Computing Research, 33, 141-171.
Chen, J. A., & Pajares, F. (2010). Implicit theories of ability of Grade 6 science students: Relation to epistemological beliefs and academic motivation and achievement in science. Contemporary Educational Psychology, 35(1), 75-87.
Chiu, Y. L., Tsai, C. C., & Liang, J. C. (2015). Testing measurement invariance and latent mean differences across gender groups in college students’ Internet-specific epistemic beliefs. Australasian Journal of Educational Technology, 31, 486-499.
Chiu, Y. L., Liang, J. C., & Tsai, C. C. (2013). Internet-specific epistemic beliefs and self-regulated learning in online academic information searching. Metacognition Learning, 8, 235-260.
Conley, A. M., Pintrich, P. R., Vekiri, I. & Harrison, D. (2004). Changes in epistemological beliefs in elementary science students. Contemporary Educational Psychology, 29(2), 186-204.
Ferguson, L. E., Bråten, I., & Strømsø, H. I. (2012). Epistemic cognition when students read multiple documents containing conﬂicting scientiﬁc evidence: A think-aloud study. Learning and Instruction, 22, 103-120.
Ferguson, L.E., & Bråten, I. (2013). Student profiles of knowledge and epistemic beliefs: Changes and relations to multiple-text comprehension. Learning and Instruction, 25, 49-61.
Greene, J. A., Azevedo, R., & Torney-Purta, J. (2008). Modeling epistemic and ontological cognition: Philosophical perspectives and methodological directions. Educational Psychologist, 43, 142-160.
Hofer, B. K. (2016). Epistemic cognition as a psychological construct: Advancements and challenges. Handbook of epistemic cognition, 31-50.
Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67, 88-140.
Hsu, C. Y., Tsai, M. J., Hou, H. T., & Tsai, C. C. (2014). Epistemic beliefs, online search strategies, and behavioral patterns while exploring socioscientific issues. Journal of Science Education and Technology, 23(3), 471-480.
Kizilgunes, B., Tekkaya, C., & Sungur, S. (2009). Modeling the relations among students' epistemological beliefs, motivation, learning approach, and achievement. The Journal of educational research, 102(4), 243-256.
Ku, K.Y.L., Lai, E.C.M., & Hau, K.T. (2014). Epistemological beliefs and the effect of authority on argument-counterargument integration: An experiment. Thinking Skills and Creativity, 13, 67-79.
Lederman, N. G. (2007). Nature of science: Past, present, and future. Handbook of research on science education, 2, 831-879.
Lee, W. C., Chiu, Y. L., Liang, J. C., & Tsai, C. C. (2014). Exploring the structural relationships between high school students’ Internet-specific epistemic beliefs and their utilization of online academic help seeking. Computers in Human Behavior, 36, 391-400.
Liang, J. C., & Tsai, C. C. (2010). Relational analysis of college science‐major students’ epistemological beliefs toward science and conceptions of learning science. International Journal of Science Education, 32(17), 2273-2289.
Liang, J. C., Lee, M. H., & Tsai, C. C. (2010). The Relations Between Scientific Epistemological Beliefs and Approaches to Learning Science Among Science-Major Undergraduates in Taiwan. Asia-Pacific Education Researcher, 19(1), 43-59.
Lin, Y. C., Liang, J. C., & Tsai, C. C. (2012). The relationships between epistemic beliefs in biology and approaches to learning biology among biology-major university students in Taiwan. Journal of Science Education and Technology, 21(6), 796-807.
Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoﬀ values for ﬁt indexes and dangers in overgeneralizing Hu and Bentler's (1999) ﬁndings. Structural Equation Modeling: A Multidisciplinary Journal, 11, 320-341.
Mason, L., & Scirica, F. (2006). Prediction of students’ argumentation skills about controversial topics by epistemological understanding. Learning and Instruction , 16, 492-509.
Mason, L., Boldrin, A., & Ariasi, N. (2010). Epistemic metacognition in context: evaluating and learning online information. Metacognition and learning, 5(1), 67-90.
Mason, L., Boscolo, P., Tornatora, M. C., & Ronconi, L. (2013). Besides knowledge: A cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science. Instructional Science, 41(1), 49-79.
Mason, L., Gava, M., & Boldrin, A. (2008). On warm conceptual change: The interplay of text, epistemological beliefs, and topic interest. Journal of Educational Psychology, 100(2), 291-309.
Muthén, L.K., & Muthén, B. (2012). Mplus user’s guide Version 7. Los Angeles, CA: Muthén & Muthén.
National Research Council (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
Noroozi, O. (2018). Considering students’ epistemic beliefs to facilitate their argumentative discourse and attitudinal change with a digital dialogue game. Innovations in Education and Teaching International, 55(3), 357-365.
Nussbaum, E. M., Sinatra, G. M., & Poliquin, A. (2008). Role of epistemic beliefs and scientific argumentation in science learning. International Journal of Science Education, 30(15), 1977-1999.
OECD (2018). Preparing our youth for an inclusive and sustainable world: The OECD PISA global competence framework. Paris: OECD.
Trevors, G.J., Kendeou, P., Bråten, I., & Braasch, J.L.G. (2017). Adolescents’ epistemic profiles in the service of knowledge revision. Contemporary Educational Psychology, 49, 107-120.
Tsai, C. C., Ho, H. N. J., Liang, J. C., & Lin, H. M. (2011). Scientific epistemic beliefs, conceptions of learning science and self-efficacy of learning science among high school students. Learning and Instruction, 21(6), 757-769.
Wu, Y. T., & Tsai, C. C. (2011). High school students’ informal reasoning regarding a socio‐scientific issue, with relation to scientific epistemological beliefs and cognitive structures. International Journal of Science Education, 33(3), 371-400.
Yang, F. Y., Bhagat, K. K., & Cheng, C. H. (2019). Associations of epistemic beliefs in science and scientific reasoning in university students from Taiwan and India. International Journal of Science Education, 41(10), 1347-1365.
Yang, F. Y., Chang, C. C., Chen, L. L., & Chen, Y. C. (2016). Exploring learners’ beliefs about science reading and scientific epistemic beliefs, and their relations with science text understanding. International Journal of Science Education, 38, 1591-1606.
Yang, F. Y., Huang, R. T., & Tsai, I. J. (2016). The effects of epistemic beliefs in science and gender differences on university students’ science-text reading: An eye-tracking study. International Journal of Science and Mathematics Education, 14, 473-498.

Chapter3:
Baker, D. R. (1991). A Summary of Research in Science Education - 1989. Scicnce Education 75(3), 255-256.
Bandura, A. (1971). Social learning theory. Morristown, NJ: General Learning Press.
Flavell, J. H. (1979). Metacognition and cognitive monitoring. American Psychologist, 34(10), 906–911.
Good, R. (1985). The Domain of Science Education. Science Education, 69(2), 139-41.
Greene, J. A., Hutchison, L. A., Costa, L. J., & Crompton, H. (2012). Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic. Contemporary Educational Psychology, 37(4), 307-320.
Hofer, B. & Pintrich, P. (1997). The development of epistemological theories: Beliefs about knowledge and knowing ad their relation to learning. Review of Educational Research, 67, 88-140.
Iqbal, S. T., Zheng, X. S., & Bailey, B. P. 2004. Task evoked pupillary response to mental workload in human-computer interaction. In Proceedings of the ACM Conference on Human Factors in Computing Systems. ACM, New York, 1477–1480.
Kintsch, W. (1998). Comprehension: a paradigm for cognition Cambridge University Press. Cambridge, MA.
Kendeou, P., & O’Brien, E. J. (in press). The knowledge revision components (KReC) framework: Processes and mechanisms. In D. N. Rapp & J. L. G. Braasch (Eds.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences. Cambridge, MA: MIT University Press.
Lai, M. L., Tsai, M. J., Yang, F. Y., Hsu, C. Y., Liu, T. C., Lee, S. W. Y., ... & Tsai, C. C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90-115.
Maria, K., & MacGinitie, W. (1987). Learning from texts that refute the reader's prior knowledge. Literacy Research and Instruction, 26(4), 222-238.
Mayer, R. E. (Ed.). (2005). The Cambridge handbook of multimedia learning. Cambridge: Cambridge University Press.
Paivio, A. (1990). Mental representations: A dual coding approach. Oxford University Press.
Piaget, J. (1952). The origins of intelligence in children (M. Cook, Trans.). New York, NY: International Universities Press.
Rayner, K. (1998). Eye Movements in Reading and Information Processing: 20 Years of Research. Psychological Bulletin, 124(3), 372-422.
Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology, 62(8), 1457-1506.
Rayner, K., Chace, K. H., Slattery, T. J., & Ashby, J. (2006). Eye movements as reflections of comprehension processes in reading. Scientific studies of reading, 10(3), 241-255.
Roller, H.D. (February 22, 1970). Has science a climate? Oklahoma City, OK: Sunday Oklahoman.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296.
Taber, K. S. (2009). Progressing science education: constructing the scientific research programme into the contingent nature of learning science. Dordrecht: Springer.
Taber, K. S., & Akpan, B. (Eds.). (2016). Science education: An international course companion. Springer.
Yen, M. H., & Yang, F. Y. (2016). Methodology and application of eye-tracking techniques in science education. In Science Education Research and Practices in Taiwan (pp. 249-277). Singapore: Springer Singapore.
Articles included in the review analysis
Ariasi, N., & Mason, L. (2014). From covert processes to overt outcomes of refutation text reading: The interplay of science text structure and working memory capacity through eye fixations. International Journal of Science and Mathematics Education, 12(3), 493-523.
Ariasi, N., Hyönä, J., Kaakinen, J. K., & Mason, L. (2017). An eye‐movement analysis of the refutation effect in reading science text. Journal of Computer Assisted Learning, 33(3), 202-221.
Chen, Y. C., & Yang, F. Y. (2014). Probing the relationship between process of spatial problems solving and science learning: An eye tracking approach. International Journal of Science & Mathematics Education, 12(3), 579-603.
Chien, K. P., Tsai, C. Y., Chen, H. L., Chang, W. H., & Chen, S. (2015). Learning differences and eye fixation patterns in virtual and physical science laboratories. Computers & Education, 82, 191-201.
Chuang, H. H., & Liu, H. C. (2012). Effects of different multimedia presentations on viewers’ information-processing activities measured by eye-tracking technology. Journal of Science Education and Technology, 21(2), 276-286.
Hinze, S. R., Williamson, V. M., Shultz, M. J., Williamson, K. C., Deslongchamps, G., & Rapp, D. N. (2013). When do spatial abilities support student comprehension of STEM visualizations?. Cognitive processing, 14(2), 129-142.
Ho, H. N. J., Tsai, M. J., Wang, C. Y., & Tsai, C. C. (2014). Prior knowledge and online inquiry-based science reading: Evidence from eye tracking. International Journal of Science and Mathematics Education, 12(3), 525-554.
Hochpöchler, U., Schnotz, W., Rasch, T., Ullrich, M., Horz, H., McElvany, N., & Baumert, J. (2013). Dynamics of mental model construction from text and graphics. European journal of psychology of education, 28(4), 1105-1126.
Jarodzka, H., Balslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2012). Conveying clinical reasoning based on visual observation via eye-movement modelling examples. Instructional Science, 40(5), 813-827.
Jian, Y. C. (2016). Fourth graders' cognitive processes and learning strategies for reading illustrated biology texts: eye movement measurements. Reading Research Quarterly, 51(1), 93-109.
Jian, Y. C. (2017). Eye-movement patterns and reader characteristics of students with good and poor performance when reading scientific text with diagrams. Reading and Writing, 30(7), 1447-1472.
Jian, Y. C., & Ko, H. W. (2017). Influences of text difficulty and reading ability on learning illustrated science texts for children: An eye movement study. Computers & Education, 113, 263-279
Jian, Y. C. (2018). Reading Instructions Influence Cognitive Processes of Illustrated Text Reading Not Subject Perception: An Eye-Tracking Study. Frontiers in Psychology, 9, 2263.
Jian, Y. C. (2019). Reading instructions facilitate signaling effect on science text for young readers: an eye-movement study. International Journal of Science and Mathematics Education, 17(3), 503-522.
Jung, Y. J., Zimmerman, H. T., & Pérez-Edgar, K. (2018). A methodological case study with mobile eye-tracking of child interaction in a science museum. TechTrends, 62(5), 509-517.
Korbach, A., Brünken, R., & Park, B. (2017). Measurement of cognitive load in multimedia learning: A comparison of different objective measures. Instructional Science, 45(4), 515-536.
Lenzner, A., Schnotz, W., & Müller, A. (2013). The role of decorative pictures in learning. Instructional Science, 41(5), 811-831.
Lim, S., Kim, Y., & Yang, I. (2014). An analysis of students' understanding process about an illustraion and text related to the earth system: An eye-tracking study. International Information Institute (Tokyo). Information, 17(8), 3613.
Lin, L., Lee, C. H., Kalyuga, S., Wang, Y., Guan, S., & Wu, H. (2017). The effect of learner-generated drawing and imagination in comprehending a science text. The Journal of Experimental Education, 85(1), 142-154.
Lindner, M. A., Eitel, A., Strobel, B., & Köller, O. (2017). Identifying processes underlying the multimedia effect in testing: An eye-movement analysis. Learning and instruction, 47, 91-102.
Lin, Y. Y., Holmqvist, K., Miyoshi, K., & Ashida, H. (2017). Effects of detailed illustrations on science learning: an eye-tracking study. Instructional science, 45(5), 557-581.
Mason, L., Pluchino, P., & Tornatora, M. C. (2013). Effects of picture labeling on science text processing and learning: Evidence from eye movements. Reading Research Quarterly, 48(2), 199-214.
Mason, L., Pluchino, P., & Tornatora, M. C. (2015). Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning. Contemporary Educational Psychology, 41, 172-187.
Mason, L., Pluchino, P., Tornatora, M. C., & Ariasi, N. (2013). An eye-tracking study of learning from science text with concrete and abstract illustrations. The Journal of Experimental Education, 81(3), 356-384.
Mason, L., Tornatora, M. C., & Pluchino, P. (2013). Do fourth graders integrate text and picture in processing and learning from an illustrated science text? Evidence from eye-movement patterns. Computers & Education, 60(1), 95-109.
Mason, L., Tornatora, M. C., & Pluchino, P. (2015). Integrative processing of verbal and graphical information during re-reading predicts learning from illustrated text: An eye-movement study. Reading and Writing, 28(6), 851-872.
Novick, L. R., Stull, A. T., & Catley, K. M. (2012). Reading phylogenetic trees: The effects of tree orientation and text processing on comprehension. BioScience, 62(8), 757-764.
Or-Kan, S. (2017). Processing academic science reading texts through context effects: Evidence from eye movements. Eurasia Journal of Mathematics, Science & Technology Education, 13(3).
Rau, M. A., Michaelis, J. E., & Fay, N. (2015). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers & Education, 82, 460-485.
Roach, V. A., Fraser, G. M., Kryklywy, J. H., Mitchell, D. G., & Wilson, T. D. (2016). The eye of the beholder: Can patterns in eye movement reveal aptitudes for spatial reasoning?. Anatomical sciences education, 9(4), 357-366.
Saparova, D., & Nolan, N. S. (2016). Evaluating the appropriateness of electronic information resources for learning. Journal of the Medical Library Association: JMLA, 104(1), 24.
Saß, S., Schütte, K., & Lindner, M. A. (2017). Test-takers’ eye movements: Effects of integration aids and types of graphical representations. Computers & Education, 109, 85-97.
Schneider, B., & Pea, R. (2013). Real-time mutual gaze perception enhances collaborative learning and collaboration quality. International Journal of Computer-supported Collaborative Learning, 8(4), 375-397.
Scrimin, S., & Mason, L. (2015). Does mood influence text processing and comprehension? Evidence from an eye‐movement study. British Journal of Educational Psychology, 85(3), 387-406.
Torkar, G., Veldin, M., Glažar, S., & Podlesek, A. (2018). Eurasia: journal of mathematics & technology education, 14(6), 2265-2276.
Trevors, G., Feyzi-Behnagh, R., Azevedo, R., & Bouchet, F. (2016). Self-regulated learning processes vary as a function of epistemic beliefs and contexts: Mixed method evidence from eye tracking and concurrent and retrospective reports. Learning and Instruction, 42, 31-46.
Tsai, M. J., Hou, H. T., Lai, M. L., Liu, W. Y., & Yang, F. Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58(1), 375-385.
Tsai, P. Y., Yang, T. T., She, H. C., & Chen, S. C. (2019). Leveraging College Students’ Scientific Evidence-Based Reasoning Performance with Eye-Tracking-Supported Metacognition. Journal of Science Education and Technology, 1-15.
Vilppu, H., Mikkilä‐Erdmann, M., Södervik, I., & Österholm‐Matikainen, E. (2017). Exploring eye movements of experienced and novice readers of medical texts concerning the cardiovascular system in making a diagnosis. Anatomical sciences education, 10(1), 23-33.
Yang, F. Y., Chang, C. Y., Chien, W. R., Chien, Y. T., & Tseng, Y. H. (2013). Tracking learners' visual attention during a multimedia presentation in a real classroom. Computers & Education, 62, 208-220.
Yang, F. Y., Huang, R. T., & Tsai, I. J. (2016). The effects of epistemic beliefs in science and gender difference on university students’ science-text reading: an eye-tracking study. International Journal of Science and Mathematics Education, 14(3), 473-498.
Yang, F. Y. (2017). Examining the reasoning of conflicting science information from the information processing perspective—an eye movement analysis. Journal of Research in Science Teaching, 54(10), 1347-1372.

Chapter4:
Bell, P., & Linn, M. C. (2000). Scientiﬁc arguments as learning artifacts: Designing for learning from the Web with KIE. International Journal of Science Education, 22(8), 797-817.
Berland, L. K., & Reiser, B. J. (2009). Making sense of argumentation and explanation. Science Education, 93(1), 26-55.
Bråten, I., Ferguson, L. E., Strømsø, H. I., & Anmarkrud, Ø. (2014). Students working with multiple conflicting documents on a scientific issue: Relations between epistemic cognition while reading and sourcing and argumentation in essays. British Journal of Educational Psychology, 84(1), 58-85.
Bricker, L. A., & Bell, P. (2008). Conceptualizations of argumentation from science studies and the learning sciences and their implications for the practices of science education. Science Education, 92(3), 473-498.
Conley, A. M., Pintrich, P. R., Vekiri, I. & Harrison, D. (2004). Changes in epistemological beliefs in elementary science students. Contemporary Educational Psychology, 29(2), 186-204.
Duschl, R. (2008). Science education in three-part harmony: Balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32, 268-291.
Duschl, R. A., & Grandy, R. E. (2008). Reconsidering the character and role of inquiry in school science: Framing the debates. In R. A. Duschl & R. E. Grandy (Eds.), Teaching scientiﬁc inquiry: Recommendations for research and implementation (pp. 1-37). Rotterdam: Sense Publishers.
Duschl, R., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38, 39-72.
Erduran, S., Simon, S., & Osborne, J. (2004). TAPping into argumentation: Developments in the application of Toulmin's argument pattern for studying science discourse. Science education, 88(6), 915-933.
Ferguson, L.E., & Bråten, I. (2013). Student profiles of knowledge and epistemic beliefs: Changes and relations to multiple-text comprehension. Learning and Instruction, 25, 49-61.
Figueira, M. J. S., Nardi, R., & Cortela, B. S. C. (2019, August). Introducing scientific argumentation practices in physics teacher’s undergraduate curricula. In Journal of Physics: Conference Series (Vol. 1286, No. 1, p. 012038). IOP Publishing.
Grooms, J., Sampson, V., & Enderle, P. (2018). How concept familiarity and experience with scientific argumentation are related to the way groups participate in an episode of argumentation. Journal of Research in Science Teaching, 55(9), 1264-1286.
Ho, H. Y., Chang, T. L., Lee, T. N., Chou, C. C., Hsiao, S. H., Chen, Y. H., & Lu, Y. L. (2019). Above-and below-average students think differently: Their scientific argumentation patterns. Thinking Skills and Creativity, 34, 100607.
Iordanou, K., Kendeou, P., & Beker, K. (2016). Argumentative reasoning. In Handbook of epistemic cognition (pp. 51-65). Routledge.
Jiménez-Aleixandre, M. P., & Erduran, S. (2007). Argumentation in science education: An overview. In Argumentation in science education (pp. 3-27). Springer, Dordrecht.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye ﬁxations to comprehension. Psychological Review, 87, 329-355.
Lai, M. L., Tsai, M. J., Yang, F. Y., Hsu, C. Y., Liu, T. C., Lee, S. W. Y., Lee, M. H., Chiou, G. L., Liang, J. C., & Tsai, C. C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational research review, 10, 90-115.
Larson, A., & Britt, A. (2009). Improving students’ evaluation of informal arguments. The Journal of Experimental Education, 77(4), 339-365.
Liversedge, S. P., Paterson, K. B., & Pickering, M. J. (1998). Eye movements and measures of reading time. In Eye guidance in reading and scene perception (pp. 55-75). Elsevier Science Ltd.
National Assessment of Educaional Progress (2013). The Nation’s Report Card.
National Research Council (1996). National Science Education Standards. Washington, DC: National Academy Press.
Noroozi, O. (2016). Considering students’ epistemic beliefs to facilitate their argumentative discourse and attitudinal change with a digital dialogue game. Innovations in Education and Teaching International, 1-9.
Noroozi, O., Kirschner, P., Biemans, H. J. A., & Mulder, M. (2017). Promoting argumentation competence: Extending from first- to second-order scaffolding through adaptive fading. Educational Psychology Review, 1-24.
Noroozi, O., McAlister, S., & Mulder, M. (2016). Impacts of a digital dialogue game and epistemic beliefs on argumentative discourse and willingness to argue. The International Review of Research in Open and Distributed Learning, 17.
Nussbaum, E. M., Sinatra, G. M., & Poliquin, A. (2008). Role of epistemic beliefs and scientific argumentation in science learning. International Journal of Science Education, 30(15), 1977-1999.
Nussbaum, E.M., & Bendixen, L.M. (2003). Approaching and avoiding arguments: The role of epistemological beliefs, need for cognition, and extraverted personality traits. Contemporary Educational Psychology, 28, 573-595.
Nussbaum, E.M., & Sinatra, G.M. (2003). Argument and conceptual engagement. Contemporary Educational Psychology, 28, 384-395.
Osborne, J., Collins, S., Ratcliffe, M., Millar, R., & Duschl, R. (2003). What ‘ideas-about-science’ should be taught in school science? A Delphi study of the expert community. Journal of Research in Science Teaching, 40(7), 692-720.
Rayner, K. (2009). The thirty ﬁfth Sir Frederick Bartlett lecture: Eye movements and attention during reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62(8), 1457-1506.
Sampson, V., Enderle, P., & Grooms, J. (2013). Argumentation in science education. The Science Teacher, 80(5), 30.
Stathopoulou, C., & Vosnidou, S. (2007). Conceptual change in physics and physics-related epistemological beliefs: A relationship under scrutiny. In S. Vosnidou, A. Baltas, & X. Vamvakoussi (Eds.), Re-framing the problem of conceptual change in learning and instruction (pp. 145-163). Amsterdam, The Netherlands: Elsevier.
Toulmin, S. (1958). The uses of argument. Cambridge: Cambridge University Press.
Yang, F. Y. (2005). Student views concerning evidence and the expert in reasoning a socio‐scientific issue and personal epistemology. Educational Studies, 31(1), 65-84.
Yang, F. Y. (2017). Examining the reasoning of conflicting science information from the information processing perspective—an eye movement analysis. Journal of Research in Science Teaching, 54(10), 1347-1372.
Yang, F. Y., & Tsai, C. C. (2010). Reasoning about science-related uncertain issues and epistemological perspectives among children. Instructional Science, 38(4), 325-354.
Yang, F. Y., Bhagat, K. K., & Cheng, C. H. (2019). Associations of epistemic beliefs in science and scientific reasoning in university students from Taiwan and India. International Journal of Science Education, 41(10), 1347-1365.

Chapter5:
Darley, W. K., Blankson, C., & Luethge, D. J. (2010). Toward an integrated framework for online consumer behavior and decision making process: A review. Psychology & marketing, 27(2), 94-116.
Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287-312.
Erduran, S., Simon, S., & Osborne, J. (2004). TAPing into argumentation: Developments in the use of Toulmin’s argument pattern in studying science discourse. Science Education, 88(6), 915-933.
Oulton, C., Dillon, J., & Grace, M. M. (2004). Reconceptualizing the teaching of controversial issues. International Journal of Science Education, 26(4), 411-423.
Roscoe, R. D., Grebitus, C., O'Brian, J., Johnson, A. C., & Kula, I. (2016). Online information search and decision making: Effects of web search stance. Computers in Human Behavior, 56, 103-118.
Sadler, T. D. (2004). Informal reasoning regarding socioscientiﬁc issues: A critical review of research. Journal of Research in Science Teaching, 41, 513-536.
Sadler, T. D., and Donnelly, L. A., 2006. “Socioscientiﬁc Argumentation: The Effects of Content Knowledge and Morality.” International Journal of Science Education 28(12), 1463-1488.
Sadler, T. D., Romine, W. L., & Topçu, M. S. (2016). Learning science content through socio-scientific issues-based instruction: A multi-level assessment study. International Journal of Science Education, 38(10), 1622-1635.
Yang, F. Y., & Tsai, C. C. (2010). An epistemic framework for scientiﬁc reasoning in informal contexts. In L. D. Bendixen & F. C. Feucht (Eds.), Personal epistemology in the classroom (pp. 124–162). Cambridge, UK: Cambridge University Press.
Yang, F. Y., Huang, R. T., & Tsai, C.C. (2016). The effects of epistemic beliefs in science and gender difference on university students’ science-text reading: An eye-tracking study. International Journal of Science and Mathematics Eduction, 14, 473-498.
Zeidler, D. L. (2001). Participating in program development: Standard F. In D. Siebert & W. McIntosh (Eds.), College pathways to the science education standards (pp. 18 – 22). Arlington, VA: National Science Teachers Press.
Zeidler, D. L., & Keefer, M. (2003). The role of moral reasoning and the status of socioscientiﬁc issues in science education: Philosophical, psychological and pedagogical considerations. In D. L. Zeidler (Ed.), The role of moral reasoning on socioscientiﬁc issues and discourse in science education. Dordrecht: Kluwer Academic Publishers.
Zeidler, D. L., Osborne, J., Erduran, S., Simon, S., & Monk, M. (2003). The role of argument and fallacies during discourse about socioscientiﬁc issues. In D. L. Zeidler (Ed.), The role of moral reasoning on socioscientiﬁc issues and discourse in science education. Dordrecht: Kluwer Academic Press.

Chapter6:
Hofer, B. K. (2016). Epistemic cognition as a psychological construct: Advancements and challenges. Handbook of epistemic cognition, 31-50.
Yang, F. Y., Bhagat, K. K., & Cheng, C. H. (2019). Associations of epistemic beliefs in science and scientific reasoning in university students from Taiwan and India. International Journal of Science Education, 41(10), 1347-1365.
Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67, 88-140.
Sampson, V., Enderle, P., & Grooms, J. (2013). Argumentation in science education. The Science Teacher, 80(5), 30.

簡易檢索 / 詳目顯示

相關論文