電腦輔助合作學習提供學生一個分享彼此知識的空間，透過合作的方式分享知識、解決活動中的問題與任務，於科學教學中引入電腦模擬，將複雜抽象的科學概念以視覺化的方式呈現給學生，透過模擬動畫引發學生探索科學的樂趣，藉由此幫助學生了解科學概念。在轉化知識的過程中，資訊大部分都由視覺觀察所獲得，因此透過眼動追蹤，分析學生眼球運動所產生的軌跡，了解學生在合作問題解決時的注意力分佈與學習歷程。而本研究應用電腦模擬輔助學生進行科學學習活動，以眼動儀蒐集學生的眼動資料，藉由眼動追蹤分析學生的學習過程，探討在不同合作式電腦模擬下，學生在合作問題解決時注意力分佈狀況、學習成效與合作品質之差異。研究結果發現，不同操作模擬之方式會造成學生的主觀感受上有差異，認為組員未盡力解決活動問題，且造成學生在注意力分佈上之差異，雖然在學習表現上兩組差異並不大，但經由科學學習活動後，皆對科學概念有提升，而應用眼動追蹤之分析方式，能有效了解學生的學習歷程。最後，本研究依據研究結果與討論提出對未來發展與研究之建議。

Computer-supported collaborative learning provides students a space to share their knowledge and solve their problems. Computer simulations in the teaching of science education can present the complex abstracts scientific concept in a visual way to students, and through the animation, also can arouse students' pleasure in exploring science and help them understand scientific concepts. In the process of transforming knowledge, most of the information is obtained by visual observation. Therefore, the eye tracking analysis can trace students' eye movement to understand students' attention distribution and learning history when they are solving cooperative problems. The purpose of this study is to assist students to learn scientific knowledge through computer simulation in science learning activities, and collect students' eye movement data, and then use eye-tracking to analyze students' learning process to discuss the difference between the computer simulations of independent controlling and the computer simulations of synchronous controlling. The analysis of collaborative science learning using dual eye-tracking techniques show us how students learn and their visual attention, learning performances and quality of cooperation during the learning process. The result show that in two different modes of operation simulation, students’ feelings about collaborating with peers in activity are significantly different because they think their partner hasn’t worked hard to solve the problems of activities, and cause the learners’ joint attention is significantly different. Although the learning performances on two modes are not significantly different, their scientific concepts have been improved after the scientific learning activities. In the conclusion of this study, using dual eye-tracking techniques is a useful way to understand students’ behaviors in science learning, and we point out some suggestions on future research.

邱秉誠（2016）。個人電腦模擬及合作電腦模擬對於合作科學問題解決之影響（未出版之碩士論文）。國立中央大學，桃園市。
黃福坤（2006）。透過物理模擬動畫進行物理教學與學習-介紹簡易模擬動畫設計環境Easy Java Simulation。物理雙月刊，28，3，536-543。
彭郁翔（2018）。不同電腦模擬模式對於合作科學問題解決之影響：以眼動進行分析。中央大學網路學習歌技研究所學位論文，1-59。
Bravo, C., van Joolingen, W. R., & de Jong, T. (2009). Using co-lab to build system dynamics models: Students’ actions and on-line tutorial advice. Computers & Education, 53(2), 243-251.
Cheng, K. H., & Tsai, C. C. (2013). Affordances of augmented reality in science learning: Suggestions for future research. Journal of science education and technology, 22(4), 449-462.
Chang, C. J., Liu, C. C., Wu, Y. T., Chang, M. H., Chiang, S. F., Chiu, B. C., ... & Wu, S. W. (2016, January). Students' perceptions on problem solving with collaborative computer simulation. In 24th International Conference on Computers in Education, ICCE 2016 (pp. 166-168). Asia-Pacific Society for Computers in Education.
Chang, C. J., Chang, M. H., Chiu, B. C., Liu, C. C., Chiang, S. H. F., Wen, C. T., ... & Wu, S. W. (2017). An analysis of student collaborative problem solving activities mediated by collaborative simulations. Computers & Education, 114, 222-235.
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational psychologist, 49(4), 219-243.
Cornsweet, T. N. (1958). New technique for the measurement of small eye movements. JOSA, 48(11), 808-811.
De Jong, T., & Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of educational research, 68(2), 179-201.
Dillenbourg, P. (1999). Collaborative learning: Cognitive and computational approaches. advances in learning and instruction series. Elsevier Science, Inc., PO Box 945, Madison Square Station, New York, NY 10160-0757.
Dodge, R., & Cline, T. S. (1901). The angular velocity of eye movements. Psychological Review, 8, 145-157.
Donnelly, D. F., Linn, M. C., & Ludvigsen, S. (2014). Impacts and characteristics of computer-based science inquiry learning environments for precollege students. Review of Educational Research, 84(4), 572-608.
Drèze, X., & Hussherr, F. X. (2003). Internet advertising: Is anybody watching?. Journal of interactive marketing, 17(4), 8-23.
Duchowski, A. T. (2002). A breadth-first survey of eye-tracking applications. Behavior Research Methods, Instruments, & Computers, 34(4), 455-470.
Esquembre, F. (2004). Easy Java Simulations: A software tool to create scientific simulations in Java. Computer physics communications, 156(2), 199-204.
Halimi, W., Salzmann, C., & Gillet, D. (2017, April). Access to Massive Open Online Labs through a MOOC. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale (pp. 331-334). ACM.
Higuch, K., Yonetani, R., & Sato, Y. (2016, May). Can eye help you?: effects of visualizing eye fixations on remote collaboration scenarios for physical tasks. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5180-5190). ACM.
Hoegl, M., & Gemuenden, H. G. (2001). Teamwork quality and the success of innovative projects: A theoretical concept and empirical evidence. Organization science, 12(4), 435-449.
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.
Huey, E. B. (1908). The psychology and pedagogy of reading. The Macmillan Company.
Jacob, R. J., & Karn, K. S. (2003). Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. In The mind's eye (pp. 573-605). North-Holland.
Jermann, P., & Nüssli, M. A. (2012, February). Effects of sharing text selections on gaze cross-recurrence and interaction quality in a pair programming task. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (pp. 1125-1134). ACM.
Johnson, D. W., & Johnson, R. T. (1996). Cooperative learning and traditional American values: An appreciation. NASSP bulletin, 80(579), 63-65.
Just, M. A., & Carpenter, P. A. (1976). Eye fixations and cognitive processes. Cognitive psychology, 8(4), 441-480.
Khine, M. S., & Saleh, I. M. (2010). New science of learning. New York: Springer Science and Business Media.
Lin, K. Y., Yu, K. C., Hsiao, H. S., Chu, Y. H., Chang, Y. S., & Chien, Y. H. (2015). Design of an assessment system for collaborative problem solving in STEM education. Journal of Computers in Education, 2(3), 301-322.
Liu, C. Y., Wu, C. J., Wong, W. K., Lien, Y. W., & Chao, T. K. (2017). Scientific modeling with mobile devices in high school physics labs. Computers & Education, 105, 44-56.
Marwan, N., & Kurths, J. (2002). Nonlinear analysis of bivariate data with cross recurrence plots. Physics Letters A, 302(5-6), 299-307.
Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics reports, 438(5-6), 237-329.
McConkie, G. W., Kerr, P. W., Reddix, M. D., & Zola, D. (1988). Eye movement control during reading: I. The location of initial eye fixations on words. Vision research, 28(10), 1107-1118.
Nüssli, M. A., Jermann, P., Sangin, M., & Dillenbourg, P. (2009, June). Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data. In Proceedings of the 9th international conference on Computer supported collaborative learning-Volume 1 (pp. 78-82). International Society of the Learning Sciences.
Nüssli, M. A. (2011). Dual eye-tracking methods for the study of remote collaborative problem solving (No. THESIS). EPFL.
Olsen, J. K., Rummel, N., & Aleven, V. (2017). Learning Alone or Together? A Combination Can Be Best!. Philadelphia, PA: International Society of the Learning Sciences.
Olsen, J. K., & Finkelstein, S. (2017). Through the (thin-slice) looking glass: An initial look at rapport and co-construction within peer collaboration. Philadelphia, PA: International Society of the Learning Sciences.
Pidd, M. (1998). Computer simulation in management science(Vol. 4). Chichester: Wiley.
Pietinen, S., Bednarik, R., Glotova, T., Tenhunen, V., & Tukiainen, M. (2008, March). A method to study visual attention aspects of collaboration: eye-tracking pair programmers simultaneously. In Proceedings of the 2008 symposium on Eye tracking research & applications (pp. 39-42). ACM.
Räihä, K. J., Aula, A., Majaranta, P., Rantala, H., & Koivunen, K. (2005, September). Static visualization of temporal eye-tracking data. In IFIP Conference on Human-Computer Interaction (pp. 946-949). Springer, Berlin, Heidelberg.
Rayner, K., & Pollatsek, A. (1989). The psychology of reading. Englewood Cliffs, NJ: Prentice Hall.
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological bulletin, 124(3), 372.
Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In Computer supported collaborative learning (pp. 69-97). Springer, Berlin, Heidelberg.
Rutten, N., Van Joolingen, W. R., & Van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136-153.
Salvucci, D. D., & Goldberg, J. H. (2000, November). Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium on Eye tracking research & applications(pp. 71-78). ACM.
Sanders, M. S., & McCormick, E. J. (1987). Human factors in engineering and design. McGRAW-HILL book company.
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.
Shen, J., Lei, J., Chang, H. Y., & Namdar, B. (2014). Technology-enhanced, modeling-based instruction (TMBI) in science education. In Handbook of research on educational communications and technology (pp. 529-540). Springer, New York, NY.
Shriver, M. D., Jin, L., Chakraborty, R., & Boerwinkle, E. (1993). VNTR allele frequency distributions under the stepwise mutation model: a computer simulation approach. Genetics, 134(3), 983-993.
Solso, R. L. (1994). Cognition and the Visual Arts, MIT Press, Cambridge, MA.
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.
Velichkovsky, B., Pomplun, M., & Rieser, J. (1996). Attention and communication: Eye-movement-based research paradigms. In Advances in Psychology (Vol. 116, pp. 125-154). North-Holland.
Villamor, M., & Rodrigo, M. M. (2017). Impact of Both Prior Knowledge and Acquaintanceship on Collaboration and Performance: A Pair Program Tracing and Debugging Eye-Tracking Experiment. In Proceedings of the 25th International Conference in Computers in Education (pp. 182-187).
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.
White, S. J. (2008). Eye movement control during reading: Effects of word frequency and orthographic familiarity. Journal of Experimental Psychology: Human Perception and Performance, 34(1), 205.