本研究欲探討關鍵訊息篩選在Python程式閱讀中扮演的角色，以眼動指標作為預測變項來預測各題的答題表現，並比較在程式閱讀理解中答對與答錯的人其視覺行為之差異。實驗使用Tobii 4C眼動設備，即時眼動凝視分析軟體RG 2.0收集眼動資料。實驗對象為60名北部大學生，採用邏輯式迴歸分析訊息相關程度與眼動指標對答題表現之預測力，獨立樣本t檢定分析各題答對組與答錯組之間的視覺注意力差異，遲滯序列分析(LSA)和掃視路徑(scan path)則討論答對組與答錯組的視覺行為模式差異，其中LSA與scan path以網頁版進階眼動視覺化資料分析系統WEDA 2.0進行資料分析與視覺化處理。研究發現邏輯式迴歸預測模型整體預測正確率均達六成以上，且答錯的預測正確率高達八至九成。在各題Python程式的預測模型均納入與掃視相關的眼動指標，訊息相關區域的不同與答題表現亦有相關：在關鍵區域中掃視幅度較短的人較有較高的機率答對，不相關的區域掃視幅度較短則有較高的機率答錯。受試者會依據各題的答題表現分成答對與答錯兩組，在視覺注意力的分布，答對組比答錯組更傾向將注意力集中於變數上，從研究中亦發現題型的不同也會導致變數有階級的關係。從LSA與scan path發現答對組注意力集中於變數的數值上，答錯組則是注意力涵蓋整個程式頁面，且答對組會忽略無關區域的資訊，同時會連結並整合關鍵訊息區域內的訊息，答錯組則會閱讀無關區域的內容。本研究建議程式教育可以著重於變數的記憶與追蹤，而未來研究則能夠進一步探討掃視與程式學習之間的關係。此外本研究的迴歸預測模型提供更精準的視覺行為判斷，可以做為未來學習鷹架開發的依據。

This study was to explore the role of critical information selection in Python program reading by using eye-tracking measures to predict reading performances and comparing the visual behaviors between correct group and incorrect group. We used Tobii 4C, Real Gaze 2.0 and Web-based Eye-tracking Data Analyzer (WEDA 2.0) to collect and analyze eye-tracking data. The participants were 60 college students in north Taiwan. In this study, we first examined the prediction model using eye-tracking measures on correlated-information areas to predict reading performance by logistic regression analyses. Second, independent t tests, scan paths and lag sequential analyses were used to compare the differences of attention distributions and transfer patterns between the two groups. The results indicate that significant logistic regression models can be obtained for all reading questions. The correction rates of the prediction models are all above 0.6 for predicting a correct answer and range from 0.8 to 0.9 for predicting an incorrect answer. Saccadic measures are included in all prediction models. Specifically, the participants who has shorter saccade amplitude in critical areas and longer saccade amplitudes in irrelevant areas have a higher probability to answer correctly. Additionally, the correct group tends to focus more on processing variables than the incorrect group. It also shows a hierarchical relationship between variables and program types. The LSA and scan-path analyses show that the correct group significantly checked the values of loop variables, while the incorrect group kept scanning the whole program. The findings of this study reveal that novice programming learners may have difficulties in memorizing variable values and selecting critical information in programs. Future studies are suggested in this paper.

十二年國民基本教育課程綱要國民中學暨普通型高級中等學校—科技領域(民107年9月20日)。
吳昭容（2019）。眼球追蹤技術在幾何教育的應用與限制。臺灣數學教育期刊，6（2），1-25。doi: 10.6278/tjme.201904_6(2).001
高等教育教司(民108年1月22日)。大學程式設計教學計畫推動有成【新聞群組】。取自https://www.edu.tw/News_Content.aspx?n=9E7AC85F1954DDA8&s=54EFCA956F11AE63
楊芳瑩、蔡孟蓉、劉子鍵(民106)。數位學習的眼球追蹤研究：原理與實例介紹。載於宋曜廷(主編)，進階數位學習研究方法(33-61頁)。臺北市：高等教育文化出版。
蔡孟蓉、許衷源、白宏達、鄭博元、徐柏棻 (2019)。一種眼動凝視之即時辨識方法及採用該方法之眼動凝視即時辨識系統。中華名國專利號 I679558。台北：經濟部智慧財產局。
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). UK: Cambridge University Press.
Bayman, P., & Mayer, R. E. (1988). Using conceptual models to teach BASIC computer programming. Journal of Educational Psychology, 80(3), 291.
Busjahn, T., Bednarik, R., Begel, A., Crosby, M., Paterson, J. H., Schulte, C., …Tamm, S. (2015). Eye movements in code reading: relaxing the linear order. Proceedings of the 2015 IEEE 23rd International Conference on Program Comprehension, 255-265.
Busjahn, T., Schulte, C., & Busjahn, A. (2011). Analysis of code reading to gain more insight in program comprehension. Proceedings of the 11th Koli Calling International Conference on Computing Education Research (pp. 1-9). New York: ACM.
Cetin, I. (2015). Students’ understanding of loops and nested loops in computer programming: An APOS theory perspective. Canadian Journal of Science, Mathematics and Technology Education, 15(2), 155-170.
Chiou, G. L., Hsu, C. Y., & Tsai, M. J. (2019). Exploring how students interact with guidance in a physics simulation: Evidence from eye-movement and log data analyses. Interactive Learning Environments. https://doi.org/10.1080/10494820.2019.1664596
Crosby, M. E., & Stelovsky, J. (1990). How do we read algorithms? A case study. Computer, 23(1), 25-35.
Du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57-73.
Guéhéneuc, Y. G., Kagdi, H., & Maletic, J. I. (2009). Working session: Using eye-tracking to understand program comprehension. Proceedings of the 2009 IEEE 17th International Conference on Program Comprehension, 278-279.
Hsu, C. Y., Chiou, G. L., & Tsai, M. J. (2016, August). A pilot study on developing and validating a fixation-based scaffolding learning system. Poster presented at 2016 International Conference of East-Asian Association for Science Education. Tokyo, Japan.
Hsu, C. Y., Chiou, G. L., & Tsai, M. J. (2018). Visual behavior and self-efficacy of game playing: an eye movement analysis. Interactive Learning Environments, 1-11.
Jbara, A., & Feitelson, D. G. (2017). How programmers read regular code: a controlled experiment using eye tracking. Empirical Software Engineering, 22(3), 1440-1477.
Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329.
Kaakinen, J. K., Hyönä, J., & Keenan, J. M. (2002). Perspective effects on online text processing. Discourse Processes, 33(2), 159-173.
Kaakinen, J. K., & Hyönä, J. (2014). Task relevance induces momentary changes in the functional visual field during reading. Psychological Science, 25(2), 626-632.
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40(1), 1-17.
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. https://doi.org/10.1016/j.edurev.2013.10.001
Li, D. R., Wu, Y. B., Hsu, C. Y., & Tsai, M. J. (2018, August). Using eye-tracking technology to explore how university students read and comprehend computer program. Paper presented at the International Conference of Innovative Technologies and Learning (ICITL 2018), Portoroz, Slovenia.
Lin, Y. T., Wu, C. C., Hou, T. Y., Lin, Y. C., Yang, F. Y., & Chang, C. H. (2015). Tracking students’ cognitive processes during program debugging—An eye-movement approach. IEEE Transactions on Education, 59(3), 175-186.
Liu, H. C., Lai, M. L., & Chuang, H. H. (2011). Using eye-tracking technology to investigate the redundant effect of multimedia web pages on viewers’ cognitive processes. Computers in human behavior, 27(6), 2410-2417.
Liversedge, S. P., & Findlay, J. M. (2000). Saccadic eye movements and cognition. Trends in cognitive sciences, 4(1), 6-14.
Liversedge, S. P., Paterson, K. B., & Pickering, M. J. (1998). Eye movements and measures of reading time. In G. Underwood (Eds.), Eye guidance in reading and scene perception (pp. 55-75). New York, NY: Elsevier Science Ltd.
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational psychologist, 38(1), 43-52.
Neider, M. B., & Zelinsky, G. J. (2006). Scene context guides eye movements during visual
search. Vision research, 46(5), 614-621.
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), 1-58.
Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429-434.
Peitek, N., Siegmund, J., Apel, S., Kästner, C., Parnin, C., …Brechmann, A.(2020). A Look into Programmers’ Heads. IEEE Transactions on Software Engineering, 46(4), 442-462.
Qian, Y., & Lehman, J. (2017). Students’ misconceptions and other difficulties in introductory programming: a literature review. ACM Transactions on Computing Education, 18(1), 1-24.
Rayner, K. (1975). The perceptual span and peripheral cues in reading. Cognitive sychology, 7(1), 65–81.
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.
Rodeghero, P., & McMillan, C. (2015). An Empirical Study on the Patterns of Eye Movement during Summarization Tasks. 2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2015), 11-20.
Rogalski, J., & Samurçay, R. (1990). Acquisition of programming knowledge and skills. In J. M. Hoc, T. R.G. Green, R. Samurçay & D. J. Gilmore (Eds.), Psychology of programming (pp. 157-174). Cambridge, MA: Academic Press.
Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye- tracking protocols. Proceedings of the 2000 symposium on Eye tracking research & applications, 71-78.
Sleeman, D., Putnam, R. T., Baxter, J., & Kuspa, L. (1986). Pascal and high school students: A study of errors. Journal of Educational Computing Research, 2(1), 5-23.
Sorva, J., Karavirta, V., & Malmi, L. (2013). A review of generic program visualization systems for introductory programming education. ACM Transactions on Computing Education (TOCE), 13(4), 1-64.
Sternberg, R. J., & Sternberg, K. (2016). Cognitive Psychology. Canada: Nelson Education.
Sweller, J. (2016). Working memory, long-term memory, and instructional design. Journal of Applied Research in Memory and Cognition, 5(4), 360-367.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296.
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, M.-J., Hsu, P.-F. & Pai, H.-T. (2018, June). Lag sequential analysis in Eye-Tracking Data Analyzer (EDA) for educational researchers. Poster presented at the 4th International Symposium on Educational Technology (ISET 2018), Osaka, Japan.
Vainio, V., & Sajaniemi, J. (2007). Factors in novice programmers' poor tracing skills. ACM SIGCSE Bulletin, 39(3), 236-240.
Wang, C. Y., Tsai, M. J., & Tsai, C. C. (2016). Multimedia recipe reading: Predicting learning outcomes and diagnosing cooking interest using eye-tracking measures. Computers in Human Behavior, 62, 9-18.
Wang, X. M., & Hwang, G. J. (2017). A problem posing-based practicing strategy for facilitating students’ computer programming skills in the team-based learning mode. Educational Technology Research & Development, 65, 1655-1671.
Winslow, L. E. (1996). Programming pedagogy—A psychological overview. ACM SIGCSE Bulletin, 28(3), 17-22.
Wong, S. Y., Cheung, H. & Chen, H. C. (1998). The advanced programmer's reliance on program semantics: Evidence from some cognitive tasks. International Journal of Psychology, 33(4), 259-268.
Wu, C. J., Liu, C. Y., Yang, C. H., & Jian, Y. C. (2020). Eye movements reveal children’s deliberative thinking and predict their performance in arithmetic word problems. European Journal of Psychology of Education. doi: https://doi.org/10.1007/s10212-020-00461-w
Yenigalla, L., Sinha, V., Sharif, B., & Crosby, M. (2016). How Novices Read Source Code in Introductory Courses on Programming: An Eye-Tracking Experiment. In Dylan, D. S., & Cali, M. F. (Eds.), Lecture Notes in Artificial Intelligence: Vol. 9744. Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience (pp. 120-131). New York: Springer-Verlag New York, Inc.