Facial Signs of Affect During Tutoring Sessions

  • Dirk Heylen
  • Mattijs Ghijsen
  • Anton Nijholt
  • Rieks op den Akker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3784)


An emotionally intelligent tutoring system should be able to taking into account relevant aspects of the mental state of the student when providing feedback. The student’s facial expressions, put in context, could provide cues with respect to this state. We discuss the analysis of the facial expression displayed by students interacting with an Intelligent Tutoring System and our attempts to relate expression, situation and mental state building on Scherer’s component process model of emotion appraisal.


Facial Expression Haptic Device Facial Expression Recognition Tutoring System Intelligent Tutoring System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hospers, M., Kroezen, E., Nijholt, A., op den Akker, R., Heylen, D.: 9. In: An agent-based intelligent tutoring system for nurse education. Applications of intelligent agents in health care, pp. 143–159. Birkhäuser, Basel (2003)Google Scholar
  2. 2.
    Kole, S.: Tactile, spoken, and visual interaction within an intelligent tutoring system. Master’s thesis, Department of Electical Engineering, Mathematics, and Computer Science, University of Twente, Enschede (2004)Google Scholar
  3. 3.
    Heylen, D., Vissers, M., op den Akker, R., Nijholt, A.: Affective feedback in a tutoring system for procedural tasks. In: André, E., Dybkjaer, L., Minker, W., Heisterkamp, P. (eds.) Affective Dialogue Systems, pp. 244–253. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    D’Mello, S.K., Craig, S.D., Gholson, B., Franklin, S., Picard, R., Graesser, A.: Integrating affect sensors in an intelligent tutoring system. In: Affective Interactions, Workshop at IUT (2005)Google Scholar
  5. 5.
    Lisetti, C., Schiano, D.: Facial expression recognition: where human computer interaction, artificial intelligence, and cognitive science intersect. Pragmatics and Cognition 8, 185–235 (2000)CrossRefGoogle Scholar
  6. 6.
    Picard, R.: Affective Computing. The MIT Press, Cambridge (2000)Google Scholar
  7. 7.
    Goodwin, M., Goodwin, C.: Gesture and coparticipation in the activity of searching for a word. Semiotica 62, 51–75 (1986)CrossRefGoogle Scholar
  8. 8.
    Scherer, K.: 14. Approaches to Emotions. In: On the Nature and Function of Emotion: A Component Process Approach, pp. 293–318. Lawrence Erlbaum Associates, Hillsdale (1984)Google Scholar
  9. 9.
    Scherer, K.: Toward a dynamic theory of emotion: The component process model of affective states (1987) Copy retrieved [02-26-2004] from,
  10. 10.
    Wehrle, T., Kaiser, S., Schmidt, S., Scherer, K.: Studying the dynamics of emotional expression using synthesized facial muscle movements. Journal of Personality and Social Psychology 78, 105–119 (2000)CrossRefGoogle Scholar
  11. 11.
    Hospers, M., Kroezen, L.: I.N.E.S. intelligent nursing education software. Master’s thesis, Departement of Computer Science University of Twente (2002)Google Scholar
  12. 12.
    Ekman, P., O’Sullivan, M.: Facial expression: Methods, means, and moues. In: Feldman, R.S., Rimé, B. (eds.) Fundamentals of nonverbal behavior, pp. 163–199Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dirk Heylen
    • 1
  • Mattijs Ghijsen
    • 2
  • Anton Nijholt
    • 1
  • Rieks op den Akker
    • 1
  1. 1.University of Twente 
  2. 2.University of AmsterdamThe Netherlands

Personalised recommendations