An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data

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Kshitij Sharma
Valérie Chavez-Demoulin
Pierre Dillenbourg


The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in Statistics, called the Extreme Value Theory (EVT). EVT claims that the bulk of the normal distribution is mostly comprised of uninteresting variations while the most extreme values convey more information. We applied EVT to eye-tracking data collected during online collaborative problem solving with the aim of predicting the quality of collaboration. We compare our previous approach, based on central trends, with an EVT approach focused on extreme episodes of collaboration. The latter occurred to provide a better prediction of the quality of collaboration.

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How to Cite
Sharma, K., Chavez-Demoulin, V., & Dillenbourg, P. (2017). An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data. Journal of Learning Analytics, 4(3), 140–164.
Research Papers
Author Biographies

Kshitij Sharma, 1. University of Lausanne, Switzerland 2. École Polytechnique Fédérale de Lausanne, Switzerland

1. Post doctoral researcher at the Department of Operations, Faculty of Business and Economics, University of Lausanne, Switzerland.

2. Post doctoral researcher at the Computer Human Interaction lab in Learning and Instruction, School of Computer and Communication Sciences, EPFL, Switzerland

Valérie Chavez-Demoulin, University of Lausanne

Full Professor at the Department of Operations, Faculty of Business and Economics, University of Lausanne, Switzerland.

Pierre Dillenbourg, École Polytechnique Fédérale de Lausanne, Switzerland

Full Professor at the School of Computer and Communication Sciences, EPFL, Switzerland.

Director of the Computer Human Interaction lab in Learning and Instruction.


Abernethy, B., & Russell, D. G. (1987). The relationship between expertise and visual search strategy in a racquet sport. Human Movement Science, 6(4), 283–319.

Charness, N., Reingold, E. M., Pomplun, M., & Stampe, D. M. (2001). The perceptual aspect of skilled performance in chess: Evidence from eye movements. Memory & Cognition, 29(8), 1146–1152.

Chavez-Demoulin, V., & Davison, A. C. (2012). Modelling time series extremes. REVSTAT - Statistical Journal, 10(1), 109–133.

Cherubini, M., & Dillenbourg, P. (2007). The effects of explicit referencing in distance problem solving over shared maps. In Proceedings of the 2007 international ACM conference on Supporting group work (pp. 331–340). ACM.

Coles, S. (2001). An introduction to statistical modeling of extreme values. London: Springer.

Duchowski, A. T., Cournia, N., Cumming, B., McCallum, D., Gramopadhye, A., Greenstein, J., … Tyrrell, R. A. (2004). Visual deictic reference in a collaborative virtual environment. In Proceedings of the 2004 symposium on Eye tracking research & applications. New York, NY, USA: ACM.

Grant, E. R., & Spivey, M. J. (2003). Eye movements and problem solving guiding attention guides thought. Psychological Science, 14(5), 462–466.

Griffin, Z. M., & Bock, K. (2000). What the eyes say about speaking. Psychological Science, 11(4), 274–279.

Jermann, P., Mullins, D., Nüssli, M. A., and Dillenbourg, P. (2011). Collaborative gaze footprints: Correlates of interaction quality. In Connecting Computer-Supported Collaborative Learning to Policy and
Practice: CSCL2011 Conference Proceedings. (Vol. 1, No. EPFL-CONF-170043, pp. 184-191). International Society of the Learning Sciences.

Jermann, P., & Nüssli, M.-A. (2012). 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.

Jermann, P., Nüssli, M.-A., & Li, W. (2010). Using dual eye-tracking to unveil coordination and expertise in collaborative Tetris. In Proceedings of the 24th BCS Interaction Specialist Group Conference (pp. 36–44). British Computer Society.

Kahrimanis, G., Chounta, I. A., and Avouris, N. (2010). Study of correlations between logfile-based metrics of interaction and the quality of synchronous collaboration. Guest Editors, 24.

McNeil, Frey, R., & Embrechts, P. (2015). Quantitative Risk Management:Concepts, Techniques and Tools. Princeton University Press.

Meier, A., Spada, H., & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2(1), 63–86.

Meyer, A. S., Sleiderink, A. M., & Levelt, W. J. (1998). Viewing and naming objects: Eye movements during noun phrase production. Cognition, 66(2), B25–B33.

Nüssli, M.-A. (2011). Dual eye-tracking methods for the study of remote collaborative problem solving. PhD Thesis, École Polytechnique Fédérale de Lausanne.

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.

Richardson, D. C., Dale, R., & Kirkham, N. Z. (2007). The art of conversation is coordination common ground and the coupling of eye movements during dialogue. Psychological Science, 18(5), 407–413.

Richardson, D. C., Dale, R., & Tomlinson, T. M. (2009). Conversation, Gaze Coordination, and Beliefs About Visual Context. Cognitive Science, 33(8), 1468–1482.

Ripoll, H., Kerlirzin, Y., Stein, J.-F., & Reine, B. (1995). Analysis of information processing, decision making, and visual strategies in complex problem solving sport situations. Human Movement Science, 14(3), 325–349.

Sangin, M. (2009). Peer knowledge modeling in computer supported collaborative learning. PhD Thesis, École Polytechnique Fédérale de Lausanne.

Schneider, B., & Blikstein, P. (2015). Comparing the Benefits of a Tangible User Interface and Contrasting Cases as a Preparation for Future Learning. In 11th Int. Conf. Comput. Supported Collaborative Learning, Gothenburg, Sweden.

Sharma, K., Caballero, D., Verma, H., Jermann, P., & Dillenbourg, P. (2015). Looking AT versus Looking THROUGH: A Dual Eye-Tracking Study in MOOC Context. In Proceedings of 11th International Conference of Computer Supported Collaborative Learning, Gothenburg, Sweden, CSCL.
Sharma, K., Chavez-Demoulin, V., & Dillenbourg, P. (2016). Non-stationary modeling of tail-dependence of two subjects concentration. (Submitted).

Sharma, K., D’Angelo, S., Gergle, D., & Dillenbourg, P. (2016). Visual Augmentation of Deictic Gestures in MOOC videos. In International Conference of the Learning Sciences. ISLS.

Sharma, K., Jermann, P., Nüssli, M.-A., & Dillenbourg, P. (2012). Gaze Evidence for different activities in program understanding. In 24th Annual conference of Psychology of Programming Interest Group.

Sharma, K., Jermann, P., Nüssli, M.-A., & Dillenbourg, P. (2013). Understanding collaborative program comprehension: Interlacing gaze and dialogues. In Computer Supported Collaborative Learning (CSCL 2013).

Stein, R., & Brennan, S. E. (2004). Another person’s eye gaze as a cue in solving programming problems. In Proceedings of the 6th international conference on Multimodal interfaces (pp. 9–15). ACM.

Thomas, L. E., & Lleras, A. (2007). Moving eyes and moving thought: On the spatial compatibility between eye movements and cognition. Psychonomic Bulletin & Review, 14(4), 663–668.

Van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25(3), 785–791.

Van Gog, T., Kester, L., Nievelstein, F., Giesbers, B., & Paas, F. (2009). Uncovering cognitive processes: Different techniques that can contribute to cognitive load research and instruction. Computers in Human Behavior, 25, 325–331.

Van Gog, T., & Scheiter, K. (2010). Eye tracking as a tool to study and enhance multimedia learning. Learning and Instruction, 20(2), 95–99.

Wise, A., Shaffer, D. W. (2015). Why Theory Matters More than Ever in the Age of Big Data. Journal of Learning Analytics, 2(2),

Zelinsky, G. J., & Murphy, G. L. (2000). Synchronizing visual and language processing: An effect of object name length on eye movements. Psychological Science, 11(2), 125–131.