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

Kshitij Sharma
Valérie Chavez-Demoulin
Pierre Dillenbourg

Abstract


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.


Keywords


Eye-tracking; Dual eye-tracking; Extreme value theory; Computer Supported Collaborative learning; Learning Analytics; Collaboration quality

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References


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DOI: http://dx.doi.org/10.18608/jla.2017.43.8

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