Abstract
This paper explores the value of eye-tracking data to assess user learning with interactive simulations (IS). Our long-term goal is to use this data in user models that can generate adaptive support for students who do not learn well with these types of unstructured learning environments. We collected gaze data from users interacting with the CSP applet, an IS for constraint satisfaction problems. Two classifiers built upon this data achieved good accuracy in discriminating between students who learn well from the CSP applet and students who do not, providing evidence that gaze data can be a valuable source of information for building user modes for IS.
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References
Shute, V.J.: A comparison of learning environments: All that glitters. In: Computers as Cognitive Tools, pp. 47–73. Lawrence Erlbaum Associates, Inc., Hillsdale (1993)
Kardan, S., Conati, C.: A Framework for Capturing Distinguishing User Interaction Behaviours in Novel Interfaces. In: Proc. of the 4th Int. Conf. on Educational Data Mining, Eindhoven, The Netherlands, pp. 159–168 (2011)
Conati, C., Merten, C.: Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation. Knowledge-Based Systems 20, 557–574 (2007)
Amershi, S., Conati, C.: Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining, 18–71 (2009)
Keith, R.: Eye movements and cognitive processes in reading, visual search, and scene perception. In: Eye Movement Research Mechanisms, Processes, and Applications, pp. 3–22. North-Holland (1995)
Rayner, K.: Eye movements in reading and information processing: 20 years of research. Psychological Bulletin; Psychological Bulletin 124, 372–422 (1998)
Rong-Fuh, D.: Examining the validity of the Needleman–Wunsch algorithm in identifying decision strategy with eye-movement data. Decision Support Systems 49, 396–403 (2010)
Simola, J., Salojärvi, J., Kojo, I.: Using hidden Markov model to uncover processing states from eye movements in information search tasks. Cognitive Systems Research 9, 237–251 (2008)
Courtemanche, F., Aïmeur, E., Dufresne, A., Najjar, M., Mpondo, F.: Activity recognition using eye-gaze movements and traditional interactions. Interacting with Computers 23, 202–213 (2011)
Loboda, T.D., Brusilovsky, P., Brunstein, J.: Inferring word relevance from eye-movements of readers. In: Proc. of the 16th Int. Conf. on Intelligent User Interfaces, pp. 175–184. ACM, New York (2011)
Loboda, T.D., Brusilovsky, P.: User-adaptive explanatory program visualization: evaluation and insights from eye movements. User Modeling and User-Adapted Interaction 20, 191–226 (2010)
Iqbal, S.T., Adamczyk, P.D., Zheng, X.S., Bailey, B.P.: Towards an index of opportunity: understanding changes in mental workload during task execution. In: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 311–320. ACM, New York (2005)
Muldner, K., Christopherson, R., Atkinson, R., Burleson, W.: Investigating the Utility of Eye-Tracking Information on Affect and Reasoning for User Modeling. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 138–149. Springer, Heidelberg (2009)
Knoepfle, D.T., Wang, J.T., Camerer, C.F.: Studying Learning in Games Using Eye-tracking. J. of the European Economic Association 7, 388–398 (2009)
Hegarty, M., Mayer, R.E., Monk, C.A.: Comprehension of Arithmetic Word Problems: A Comparison of Successful and Unsuccessful Problem Solvers. J. of Educational Psychology 87, 18–32 (1995)
Canham, M., Hegarty, M.: Effects of knowledge and display design on comprehension of complex graphics. Learning and Instruction 20, 155–166 (2010)
Jarodzka, H., Scheiter, K., Gerjets, P., van Gog, T.: In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction 20, 146–154 (2010)
Tsai, M.-J., Hou, H.-T., Lai, M.-L., Liu, W.-Y., Yang, F.-Y.: Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education 58, 375–385 (2012)
Eivazi, S., Bednarik, R.: Predicting Problem-Solving Behavior and Expertise Levels from Visual Attention Data. In: The 2nd Workshop on the Eye Gaze in Intelligent Human Machine Interaction, Palo Alto, California, USA, pp. 9–16 (2011)
Amershi, S., Carenini, G., Conati, C., Mackworth, A.K., Poole, D.: Pedagogy and usability in interactive algorithm visualizations: Designing and evaluating CIspace. Interacting with Computers 20, 64–96 (2008)
Goldberg, J.H., Helfman, J.I.: Comparing Information Graphics: A Critical Look at Eye Tracking. Presented at the BELIV 2010, Atlanta, GA, USA (2010)
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Kardan, S., Conati, C. (2012). Exploring Gaze Data for Determining User Learning with an Interactive Simulation. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds) User Modeling, Adaptation, and Personalization. UMAP 2012. Lecture Notes in Computer Science, vol 7379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31454-4_11
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DOI: https://doi.org/10.1007/978-3-642-31454-4_11
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