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

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

Sharma, Kshitij  
•
Chavez-Demoulin, Valerie
2017
Journal of Learning Analytics

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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Type
research article
DOI
10.18608/jla.2017.43.8
Author(s)
Sharma, Kshitij  
Chavez-Demoulin, Valerie
Date Issued

2017

Published in
Journal of Learning Analytics
Volume

4

Issue

3

Start page

140

End page

164

Subjects

Eye-tracking

•

Dual eye-tracking

•

Extreme value theory

•

Computer Supported Collaborative learning

•

Learning Analytics

•

Collaboration quality

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CHILI  
Available on Infoscience
May 1, 2018
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/146257
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