以眼球追蹤法探究解決結構良好問題的認知歷程：星體運動為例

Using Eye-Tracking Technology to Investigate the Cognitive Processes of Solving Well-Structured Problems on the Topic of Celestial Motion

10.6173/CJSE.202009_28(3).0004

陳怡君(Yi-Chun Chen)；楊芳瑩(Fang-Ying Yang)

天文教育 ； 訊息處理理論 ； 專家生手典範 ； 眼動分析 ； 結構良好的問題 ； Astronomy Education ； Information-Processing Theory ； Expert-Novice Paradigm ； Eye-Movement Analysis ； Well-Structured Problems

科學教育學刊

28卷3期（2020 / 09 / 01）

281 - 302

繁體中文

星體運動是天文教育的重要核心概念之一，但該概念複雜，學生常遭遇學習困難，教學上亦富有挑戰性。為此，本研究採用認知心理學中的專家生手典範，招募38位受過國民基本教育、擁有先備知識者為研究對象，以眼球追蹤技術輔以訪談，探究在解答「恆星與太陽視運動測驗」這類結構良好的問題時，高、低表現者的認知歷程差異，期許能對教學實務有所貢獻。研究發現：一、高表現者雖有展現向前思考的特徵，即會較費心地理解題幹敘述以形成問題表徵、較能確定正確答案，但也會彈性搭配「手段—目標分析」的廣泛型解題策略解題，以縮小目標範圍，更有效率地解答。二、在解答結構良好的單一選擇題時，高分組主要經歷「上而下」的認知歷程—能以所學知識辨識關鍵區域並解碼訊息，和激活解題所需的領域概念知識。因此，是由長期記憶知識結構的組織品質決定測驗表現，測驗表現與領域特定知識較有關。三、雖然在解題過程中，學生仍是以閱讀與理解文字為主，但相較於關鍵字詞、字句的資訊處理，高表現者在圖形關鍵區域的語義解碼需耗費較多心力，與花費較多凝視時間比例，這顯示圖片關鍵區域的概念理解較有難度。文末對未來相關研究提出建議，也討論了實務上包含教材設計與測驗評量等議題。

Based on the expert-novice paradigm, using eye-movement technology supplemented by interviews, this study investigated the cognitive processes of solving well-structured problems on the topic of celestial motion. By comparing the differences between high- and low-performing students' cognitive characteristics, it was hoped that this study could provide evidence-based contributions. Thirty-eight university students who were taking non-earth-science majors but who had taken introductory earth science lessons in high school were recruited. They were asked to answer eight multiple-choice questions which were selected from the entrance examination for high schools and colleges in Taiwan. The results showed that: (1) Although the high-performing students tended to think forward (making more mental efforts in the stem to determine how to represent a problem, and paying much attention to the only correct answer), they flexibly used the means-ends analysis to reduce cognitive overload to answer questions more efficiently. (2) Top-down effects were the dominant information processing, while high-performing students were solving well-structured problems. Top-down effects referred to using acquired knowledge to identify and decode the key areas providing critical information and activated the necessary domain-specific knowledge. Therefore, the students' achievements were mostly determined by the richness and quality of the domain-specific knowledge stored in students' long-term memory rather than their general abilities. (3) While solving the problems, students spent a much greater percentage of time reading the text than looking at the graphics. The high-performing students spent a greater percentage of time processing information and expended more mental efforts on the critical areas of the graphics than the critical areas of the text. This revealed that decoding semantic information of scientific visual representations was the most difficult for students. Both the theoretical and practical implications of the findings are discussed.

1. 蔡介立, J.-L.,顏妙璇, M.-H.,汪勁安, C.-A.(2005)。眼球移動測量及在中文閱讀研究之應用。應用心理研究，28，91-104。
   連結：
2. Alemdag, E.,Cagiltay, K.(2018).A systematic review of eye tracking research on multimedia learning.Computers & Education,125,413-428.
3. Anderson, J. R.(2009).Cognitive psychology and its implications.New York, NY:Worth.
4. Canham, M.,Hegarty, M.(2010).Effects of knowledge and display design on comprehension of complex graphics.Learning and Instruction,20(2),155-166.
5. Chase, W. G.,Simon, H. A.(1973).The mind’s eye in chess.Visual information processing,New York, NY:
6. 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 and Mathematics Education,12(3),579-603.
7. Chen, Y.-C.,Yang, F.-Y.,Chang, C.-C.(2020).Conceptualizing spatial abilities and their relation to science learning from a cognitive perspective.Journal of Baltic Science Education,19(1),50-63.
8. Chi, M. T. H.(2006).Two approaches to the study of experts’ characteristics.The Cambridge handbook of expertise and expert performance,New York, NY:
9. Chi, M. T. H.(Ed.),Glaser, R.(Ed.),Farr, M. J.(Ed.)(1988).The nature of expertise.Hillsdale, NJ:Erlbaum.
10. Chi, M. T. H.,Glaser, R.,Rees, E.(1982).Expertise in problem solving.Advances in the psychology of human intelligence,Hillsdale, NJ:
11. Cohen, J.,Cohen, P.,West, S. G.,Aiken, L. S.(2003).Applied multiple regression/correlation analysis for the behavioral sciences.Mahwah, NJ:Erlbaum.
12. Gagné, E. D.,Yekovich, C. W.,Yekovich, F. R.(1993).The cognitive psychology of school learning.New York, NY:HarperCollins.
13. Gobet, F.,Simon, H. A.(1996).Recall of random and distorted chess positions: Implications for the theory of expertise.Memory & Cognition,24(4),493-503.
14. Goldman, S. R.,Bisanz, G. L.(2002).Toward a functional analysis of scientific genres: Implications for understanding and learning processes.The psychology of science text comprehension,London, UK:
15. Grant, E. R.,Spivey, M. J.(2003).Eye movements and problem solving: Guiding attention guides thought.Psychological Science,14(5),462-466.
16. Grant, E. R.,Spivey, M. J.(2002).Guiding attention produces inferences in diagrambased problem solving.Diagrammatic representation and inference,Berlin, Germany:
17. Groen, M.,Noyes, J.(2010).Solving problems: How can guidance concerning taskrelevancy be provided?.Computers in Human Behavior,26(6),1318-1326.
18. Hegarty, M.,Canham, M. S.,Fabrikant, S. I.(2010).Thinking about the weather: How display salience and knowledge affect performance in a graphic inference task.Journal of Experimental Psychology: Learning, Memory, and Cognition,36(1),37-53.
19. Hegarty, M.,Carpenter, P. A.,Just, M. A.(1991).Diagrams in the comprehension of scientific texts.Handbook of reading research,New York, NY:
20. Just, M. A.,Carpenter, P. A.(1980).A theory of reading: From eye fixations to comprehension.Psychological Review,87(4),329-354.
21. Kaller, C. P.,Rahm, B.,Bolkenius, K.,Unterrainer, J. M.(2009).Eye movements and visuospatial problem solving: Identifying separable phases of complex cognition.Psychophysiology,46(4),818-830.
22. Kozhevnikov, M.,Motes, M. A.,Hegarty, M.(2007).Spatial visualization in physics problem solving.Cognitive Science,31(4),549-579.
23. Lai, M.-L.,Tsai, M.-J.,Yang, F.-Y.,Hsu, C.-Y.,Liu, T.-C.,Lee, S. W.-Y.(2013).A review of using eye-tracking technology in exploring learning from 2000 to 2012.Educational Research Review,10,90-115.
24. Lelliott, A.,Rollnick, M.(2010).Big ideas: A review of astronomy education re search 1974–2008.International Journal of Science Education,32(13),1771-1799.
25. Lin, Y.-T.,Wu, C.-C.,Hou, T.-Y.,Lin, Y.-C.,Yang, F.-Y.,Chang, C.-H.(2016).Tracking students’ cognitive processes during program debugging—An eye-movement approach.IEEE Transactions on Education,59(3),175-186.
26. Logan, G. D.(1988).Toward an instance theory of automatization.Psychological Review,95(4),492-527.
27. Lowe, R. K.(1996).Background knowledge and the construction of a situational representation from a diagram.European Journal of Psychology of Education,11(4),377-397.
28. Lowe, R. K.(1993).Constructing a mental representation from an abstract technical diagram.Learning and Instruction,3(3),157-179.
29. National Research Council(2012).A framework for K-12 science education: Practices, crosscutting concepts, and core ideas.Washington, DC:National Academies Press.
30. Obaidellah, U.,Al Haek, M.,Cheng, P. C.-H.(2018).A survey on the usage of eye-tracking in computer programming.ACM Computing Surveys,51(1),1-61.
31. Perini, L.(2005).Convention, resemblance and isomorphism: Understanding scientific visual representations.Studies in Multidisciplinarity,2,37-47.
32. Plummer, J. D.(2014).Spatial thinking as the dimension of progress in an astronomy learning progression.Studies in Science Education,50(1),1-45.
33. Rayner, K.(2009).The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search.Quarterly Journal of Experimental Psychology,62(8),1457-1506.
34. Rayner, K.(1998).Eye movements in reading and information processing: 20 years of research.Psychological Bulletin,124(3),372-422.
35. Reingold, E. M.,Charness, N.,Pomplun, M.,Stampe, D. M.(2001).Visual span in expert chess players: Evidence from eye movements.Psychological Science,12(1),48-55.
36. Reingold, E. M.,Charness, N.,Schultetus, R. S.,Stampe, D. M.(2001).Perceptual automaticity in expert chess players: Parallel encoding of chess relations.Psychonomic Bulletin & Review,8(3),504-510.
37. Snow, R. E.(1980).Aptitude processes.Aptitude, learning, and instruction,Hillsdale, NJ:
38. Sternberg, R. J.(2009).Cognitive psychology.Belmont, CA:Wadsworth.
39. Thomas, L. E.,Lleras, A.(2007).Moving eyes and moving thought: On the spatial compat ibility between eye movements and cognition.Psychonomic Bulletin & Review,14(4),663-668.
40. 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.
41. Vicente, K. J.,de Groot, A. D.(1990).The memory recall paradigm: Straightening out the historical record.American Psychologist,45(2),285-287.
42. Yoon, D.,Narayanan, N. H.(2004).Predictors of success in diagrammatic problem solving.Diagrammatic representation and inference: Third International Conference March 22–24, 2004. Proceedings,Berlin, Germany:
43. 國家教育研究院(2018年11月2日)。十二年國民基本教育課程綱要—國民中小學暨普通型高級中等學校：自然科學領域。查詢日期：2020年9月16日，檢自https://www.naer.edu.tw/ezfiles/0/1000/attach/63/pta_18538_240851_60502.pdf。[National Academy for Educational Research. (2018, November 2). Shiernian guomin jiben jiaoyu kecheng gangyao—Guomin zhongxiaoxue ji putongxing gaoji zhongdeng xuexiao: Ziran kexue lingyu. Retrieved September 16, 2020, from https://www.naer.edu.tw/ezfiles/0/1000/attach/63/pta_18538_240851_60502.pdf]