Abstract
This paper presents an experimental evaluation of eye gaze data as a source for modeling user’s learning in Interactive Simulations (IS). We compare the performance of classifier user models trained only on gaze data vs. models trained only on interface actions vs. models trained on the combination of these two sources of user interaction data. Our long-term goal is to build user models that can trigger adaptive support for students who do not learn well with ISs, caused by the often unstructured and open-ended nature of these environments. The test-bed for our work is the CSP applet, an IS for Constraint Satisfaction Problems (CSP). Our findings show that including gaze data as an additional source of information to the CSP applet’s user model significantly improves model accuracy compared to using interface actions or gaze data alone.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Holzinger, A., Kickmeier-Rust, M.D., Wassertheurer, S., Hessinger, M.: Learning performance with interactive simulations in medical education: Lessons learned from results of learning complex physiological models with the HAEMOdynamics SIMulator. Computers & Education 52, 292–301 (2009)
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)
Kardan, S., Conati, C.: Exploring Gaze Data for Determining User Learning with an Interactive Simulation. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 126–138. Springer, Heidelberg (2012)
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)
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)
Muir, M., Conati, C.: An Analysis of Attention to Student – Adaptive Hints in an Educational Game. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 112–122. Springer, Heidelberg (2012)
Toker, D., Conati, C., Steichen, B., Carenini, G.: Individual User Characteristics and Information Visualization: Connecting the Dots through Eye Tracking. In: Proc. of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), Paris, France (to appear, 2013)
Mathews, M., Mitrovic, A., Lin, B., Holland, J., Churcher, N.: Do Your Eyes Give It Away? Using Eye Tracking Data to Understand Students’ Attitudes towards Open Student Model Representations. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 422–427. Springer, Heidelberg (2012)
Qu, L., Johnson, W.L.: Detecting the Learner’s Motivational States in An Interactive Learning Environment. In: Proceedings of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pp. 547–554. IOS Press, Amsterdam (2005)
Steichen, B., Carenini, G., Conati, C.: User-Adaptive Information Visualization - Using eye gaze data to infer visualization tasks and user cognitive abilities. In: Proceedings of the International Conference on Intelligent User Interfaces, IUI 2013 (to appear, 2013)
D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: A gaze-reactive intelligent tutoring system. Int. J. Hum.-Comput. Stud. 70, 377–398 (2012)
Trivedi, S., Pardos, Z.A., Heffernan, N.T.: Clustering students to generate an ensemble to improve standard test score predictions. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 377–384. Springer, Heidelberg (2011)
Gong, Y., Beck, J.E., Ruiz, C.: Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 102–113. Springer, Heidelberg (2012)
Shih, B., Koedinger, K.R., Scheines, R.: Unsupervised Discovery of Student Strategies. In: Proceedings of the 3rd International Conference on Educational Data Mining, pp. 201–210 (2010)
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)
Kardan, S.: Data mining for adding adaptive interventions to exploratory and open-ended environments. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 365–368. Springer, Heidelberg (2012)
Zhang, C., Zhang, S.: Association Rule Mining. LNCS (LNAI), vol. 2307. Springer, Heidelberg (2002)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 10–18 (2009)
Thabtah, F.: A Review of Associative Classification Mining. The Knowledge Engineering Review 22, 37–65 (2007)
Ben-David, A.: About the relationship between ROC curves and Cohen’s kappa. Eng. Appl. Artif. Intell. 21, 874–882 (2008)
Baker, R.S.J.d., Pardos, Z.A., Gowda, S.M., Nooraei, B.B., Heffernan, N.T.: Ensembling predictions of student knowledge within intelligent tutoring systems. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 13–24. Springer, Heidelberg (2011)
Kardan, S., Conati, C.: Providing Adaptive Support in an Exploratory Learning Environment by Mining User Interaction Data. In: Proceedings of the 5th International Workshop on Intelligent Support for Exploratory Environments (ISEE 2012) (2012); In Conjunction with the 11th International Conference on Intelligent Tutoring Systems (ITS 2012), Chania, Greece (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kardan, S., Conati, C. (2013). Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-38844-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
Online ISBN: 978-3-642-38844-6
eBook Packages: Computer ScienceComputer Science (R0)