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

Détails

ID Serval
serval:BIB_F9EC38E0EAC3
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data
Périodique
Journal of Learning Analytics
Auteur⸱e⸱s
Sharma K., Chavez-Demoulin V., Dillenbourg P.
ISSN
1929-7750
Statut éditorial
Publié
Date de publication
03/12/2017
Peer-reviewed
Oui
Volume
4
Numéro
3
Pages
140-164
Langue
anglais
Résumé
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.
Mots-clé
Eye-tracking, Dual eye-tracking, Extreme value theory, Computer Supported Collaborative learning, Learning Analytics, Collaboration quality
Open Access
Oui
Création de la notice
12/03/2017 11:46
Dernière modification de la notice
20/08/2019 17:25
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