Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation

  • Authors:

  • Cristina Conati;Christina Merten

  • Affiliations:

  • Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada V6Z2T4 and Department of Information and Communication Technology, University of Trento Via So ...;Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada V6Z2T4

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2007

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Abstract

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy.