Who is the expert? analyzing gaze data to predict expertise level in collaborative applications

Authors:
Yan Liu;Pei-Yun Hsueh;Jennifer Lai;Mirweis Sangin;Marc-Antoine Nüssli;Pierre Dillenbourg
Affiliations:
IBM T.J. Watson Research Center, Yorktown Heights, NY;IBM T.J. Watson Research Center, Yorktown Heights, NY;IBM T.J. Watson Research Center, Yorktown Heights, NY;CRAFT, School of Computer and Communication Sciences, Ecole polytechnique Fédérale de Lausanne, Lausanne, Switzerland;CRAFT, School of Computer and Communication Sciences, Ecole polytechnique Fédérale de Lausanne, Lausanne, Switzerland;CRAFT, School of Computer and Communication Sciences, Ecole polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

In this paper, we analyze complex gaze tracking data in a collaborative task and apply machine learning models to automatically predict skill-level differences between participants. Specifically, we present findings that address the two primary challenges for this prediction task: (1) extracting meaningful features from the gaze information, and (2) casting the prediction task as a machine learning (ML) problem. The results show that our approach based on profile hidden Markov models are up to 96% accurate and can make the determination as fast as one minute into the collaboration, with only 5% of gaze observations registered. We also provide a qualitative analysis of gaze patterns that reveal the relative expertise level of the paired users in a collaborative learning user study.