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)

Abstract

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.

Keywords

student modeling eye-tracking self-regulated learning 

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References

  1. 1.
    Azevedo, R., Behnagh, R., Duffy, M., Harley, J., Trevors, G.: Metacognition and self-regulated learning in student-centered leaning environments. In: Theoretical Foundations of Student-centered Learning Environments, 2nd edn., pp. 171–197 (2012)Google Scholar
  2. 2.
    D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: A gaze-reactive intelligent tutoring system. Int. J. Hum.-Comput. Stud. 70, 377–398 (2012)CrossRefGoogle Scholar
  3. 3.
    Kardan, S., Conati, C.: Exploring gaze data for determining user learning with an interactive simulation. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 126–138. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Anderson, J.R., Gluck, K.: What role do cognitive architectures play in intelligent tutoring systems. In: Cognition & Instruction: Twenty-five Years of Progress, pp. 227–262 (2001)Google Scholar
  5. 5.
    Conati, C., Merten, C.: Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation. Knowledge-Based Systems 20, 557–574 (2007)CrossRefGoogle Scholar
  6. 6.
    Qu, L., Johnson, W.L.: Detecting the learner’s motivational states in an interactive learning environment. In: Proc. of 12th Int. Conf. on Artificial Intelligence in Education (2005)Google Scholar
  7. 7.
    Muldner, K., Christopherson, R., Atkinson, R., Burleson, W.: Investigating the Utility of Eye-Tracking Information on Affect and Reasoning for User Modeling. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 138–149. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Sibert, J.L., Gokturk, M., Lavine, R.A.: The reading assistant: eye gaze triggered auditory prompting for reading remediation. In: Proc. of the 13th Annual ACM Symposium on User Interface Software and Technology, pp. 101–107 (2000)Google Scholar
  9. 9.
    Kinnebrew, J.S., Biswas, G.: Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution. In: Proc. of EDM, 5th Int. Conf. on Educational Data Mining, pp. 57–64 (2012)Google Scholar
  10. 10.
    Bouchet, F., Azevedo, R., Kinnebrew, J.S., Biswas, G.: Identifying Students’ Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning. In: Proc. of EDM, 5th Int. Conf. on Educational Data Mining, pp. 65–72 (2012)Google Scholar
  11. 11.
    Sabourin, J.L., Mott, B.W., Lester, J.C.: Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models. In: Proc. of EDM, 5th Int. Conf. on Educational Data Mining, pp. 156–159 (2012)Google Scholar
  12. 12.
    Azevedo, R., et al.: The Effectiveness of Pedagogical Agents’ Prompting and Feedback in Facilitating Co-adapted Learning with MetaTutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 212–221. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Goldberg, J.H., Helfman, J.I.: Comparing information graphics: a critical look at eye tracking. In: Proc. of BELIV, 3rd Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization, pp. 71–78 (2010)Google Scholar
  14. 14.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The J. of Machine Learning Research 3, 1157–1182 (2003)MATHGoogle Scholar
  15. 15.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Ben-David, A.: About the relationship between ROC curves and Cohen’s kappa. Engineering Applications of Artificial Intelligence 21, 874–882 (2008)CrossRefGoogle Scholar
  17. 17.
    Kardan, S., Conati, C.: Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 215–227. Springer, Heidelberg (2013)Google Scholar

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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