Motivation Diagnosis in Intelligent Tutoring Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1452)


Despite being of crucial importance in Education, the issue of motivation has been only very recently explicitly addressed in Intelligent Tutoring Systems (ITS). In the few studies done, the main focus has been on motivational planning (i.e. how to plan the instruction in order to motivate the student). In this paper we argue that motivation diagnosis (i.e. how to detect the student’s motivational state) is of crucial importance for creating ‘motivating’ ITSs, and that more research is needed in this area. After an introduction, we review some relevant research on motivation diagnosis, and then we suggest directions which further research in this area might take. Although the issues discussed here are still poorly understood, this paper attempts to encourage research in the ITS community in what we believe is one of the most important aspects of instruction.


Motivational State Motivational Planning Intelligent Tutor System Computer Instruction Sentic Modulation 
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.
    Goleman, D. Emotional intelligence: Why it can matter more than IQ. Boomsbury, London, 1996. First published by Bantam Books, New York, 1995. 86, 88, 88Google Scholar
  2. 2.
    Weiner, B. Human motivation: metaphors, theories, and research. Sage Publications Inc., 1992. 86Google Scholar
  3. 3.
    Williams, M. and Burden, R. L. Psychology for language teachers: A social constructivist approach. Cambridge University Press, Cambridge, UK, 1997. 87Google Scholar
  4. 4.
    Lepper, M., Woolverton, M., Mumme, D., and Gurtner, J. Motivational techniques of expert human tutors: lessons for the design of computer-based tutors. In S.P. Lajoie and S.J. Derry, editors, Computers as cognitive tools. Lawrence Erlbaum, 1993. 87, 88Google Scholar
  5. 5.
    del Soldato, T. Motivation in tutoring systems. Tech. Rep. CSRP 303, School of Cognitive and Computing Sciences, The University of Sussex, UK, 1994. 87, 87, 89, 89, 89, 90, 90, 92, 93Google Scholar
  6. 6.
    Matsubara, Y. and Nagamachi, M. Motivation system and human model for intelligent tutoring. In Claude Frasson, Gilles Gauthier, and Alan Lesgold, editors, Proceedings of the Third International Conference in Intelligent Tutoring Systems, pages 139–147. Springer-Verlag, 1996. 87, 89Google Scholar
  7. 7.
    Davis, F. La comunicación no verbal, volume 616 of El Libro de Bolsillo. Alianza Editorial, Madrid, Spain, 1976. Translated by Lita Mourglier from: “Inside Intuition-What we Knew About Non-Verbal Communication”; McGraw-Hill Book Company, New York. 87, 92Google Scholar
  8. 8.
    Picard, R.W. Affective computing. Technical Report 321, M.I.T. Media Laboratory Perceptual Computing Section, Cambridge, Massachussetts, November 1995. Report available on-line on November 19, 1997 at 88, 91Google Scholar
  9. 9.
    Rosenthal, R. et. al. The PONS test: Measuring sensitivity to nonverbal cues. In P. McReynolds, editor, Advances in Psychological Assessment. Jossey-Bass, San Francisco, 1977. 88Google Scholar
  10. 10.
    Lepper, M. R. and Chabay, R. W. Socializing the intelligent tutor: Bringing empathy to computer tutors. In Heinz Mandl and Alan Lesgold, editors, Learning Issues for Intelligent Tutoring Systems, chapter 10, pages 242–257. Springer-Verlag, 1988. 88, 88Google Scholar
  11. 11.
    Issroff, K. and del Soldato, T. Incorporating motivation into computer-supported collaborative learning. In Paiva, A., and Self, J., editors Proceedings of the European Conference on Artificial Intelligence in Education, 1996 Brna et al. [23], pages 284–290. 88Google Scholar
  12. 12.
    Arshad, F. The Design of Knowledge-based Advisors for Learning. PhD thesis, School of Education, University of Leeds, UK, 1990. 89Google Scholar
  13. 13.
    Whitelock, D. and Scanlon, E. Motivation, media andmotion:Reviewing a computer supported collaborative learning experience. In Paiva, A., and Self, J., editors Proceedings of the European Conference on Artificial Intelligence in Education, 1996 Brna et al. [23], pages 276–283. 89Google Scholar
  14. 14.
    Gardner, R. C. Social psychology and second language learning: the role of attitudes and motivation. London: Edward Arnold, 1985. 89Google Scholar
  15. 15.
    O’Bryen, P. Using questionnaires to assess motivation in second language classrooms. University of Hawaií Working Papers in ESL, 14(2), 1996. 89Google Scholar
  16. 16.
    Briggs, P., Burford, B., and Dracup, C. Self confidence as an issue for usermodeling. In Proceedings of the Fifth International Conference on User Modeling, Kailua-Kona, Hawaii, 1996. User Modeling, Inc. 90Google Scholar
  17. 17.
    Keller, J. M. Strategies for stimulating the motivation to learn. Performance and Instruction Journal, 26:1–7, 1987. 90Google Scholar
  18. 18.
    Hioe, W. and Campbell, D. J. An expert system for the diagnosis of motivationrelated job performance problems: Initial efforts. DICS publication no. TRA3/88, Department of Information Systems and Computer Science, National University of Singapore, 1988. 91Google Scholar
  19. 19.
    Picard, R. W. Affective Computing. The MIT Press, Cambridge, Massachusetts, 1997. 91, 91, 91, 91, 93Google Scholar
  20. 20.
    Essa, I. and Pentland, A. Coding, analysis, interpretation and recognition of facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):757–763, July 1997. 91CrossRefGoogle Scholar
  21. 21.
    Roy, D. and Pentland, A. Automatic spoken affect analysis and classification. In Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pages 363–367, Killington, VT, Oct 1996. 91Google Scholar
  22. 22.
    Eckhard H., H. Attitude and pupil size. Scientific American, 212:46–54, 1965. 92Google Scholar
  23. 23.
    Brna, P., Paiva, A., and Self, J., editors Proceedings of the European Conference on Artificial Intelligence in Education, 1996. 95, 95Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  1. 1.Department of Artificial IntelligenceThe Univerisity of EdinburghEdinburgh

Personalised recommendations