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

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.


Published in:
Proceeding ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo, 898-901
Presented at:
ICME'09 International conference on Multimedia and Expo , Piscataway, NJ, USA, June 29, 2009
Year:
2009
Publisher:
IEEE Press
ISBN:
978-1-4244-4290-4
Keywords:
Laboratories:




 Record created 2011-02-09, last modified 2018-09-13

External link:
Download fulltext
URL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)