Inferring Learning from Gaze Data during Interaction with an Environment to Support Self-Regulated Learning

  • Daria Bondareva
  • Cristina Conati
  • Reza Feyzi-Behnagh
  • Jason M. Harley
  • Roger Azevedo
  • François Bouchet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)


In this paper, we explore the potential of gaze data as a source of information to predict learning as students interact with MetaTutor, an ITS that scaffolds self-regulated learning. Using data from 47 college students, we show that a classifier using a variety of gaze features achieves considerable accuracy in predicting student learning after seeing gaze data from the complete interaction. We also show promising results on the classifier ability to detect learning in real-time during interaction.


student modeling eye-tracking self-regulated learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daria Bondareva
    • 1
  • Cristina Conati
    • 1
  • Reza Feyzi-Behnagh
    • 2
  • Jason M. Harley
    • 2
  • Roger Azevedo
    • 2
  • François Bouchet
    • 2
  1. 1.University of British ColumbiaCanada
  2. 2.McGill UniversityCanada

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