Motivation system and human model for intelligent tutoring

  • Yukihiro Matsubara
  • Mitsuo Nagamachi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1086)


In this paper, we focus on the student's “motivation level” in his learning situation, and we propose the ergonomic design of ITS framework as the motivation system. The aim of this system is motivating the student for his/her learning process to give the appropriate encouragement, praise or reproach messages. It is important to represent the student's internal psychological state. Therefore, we propose the human model which consists of several element for pshycological characteristics and prepares the fuzzy if-then rules to infer the message policy as the adequate strategy knowledge. In general, it is difficult to identify the fuzzy if-then rules and membership functions, so we introduce the automatic fuzzy rule acquisition system, called FREGA, which is our new knowledge acquisition system combining ID3 method and Genetic Algorithm. Finally, we give the estimation of this system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Araki, D., and et al, Inductive decision tree learning from numerical data, Journal of Japanese Society for Artificial Intelligence, Vol. 7, No.6, pp.992–1000 (1992).Google Scholar
  2. 2.
    Kunisa, A., Tsuchiya, T., Matsubara, Y. and et al., An expert system for sales forecast on the basis of a decision tree, Proceedings of the Second China-Japan International Symposium on Industrial Management, pp. 355–360 (1993).Google Scholar
  3. 3.
    Matsubara, Y. and Nagamachi, M., A motivation model for intelligent tutoring system, Proceedings of 11th Congress of the International Ergonomics Association, pp.634–636 (1991).Google Scholar
  4. 4.
    Matsubara, Y. and Nagamachi, M., Ergonomic design of intelligent tutoring system: the decision method for tutoring strategy based on the motivation model, Proceedings of the 3rd Pan-Pacific Conference on Occupational Ergonomics, pp.585–589 (1994).Google Scholar
  5. 5.
    Payne, S.J., Methods and mental models in theories of cognitive skill, In Self, J. (eds.), Artificial Intelligence and Human Learning, Chapmann and Hall Computing, pp.69–87 (1988).Google Scholar
  6. 6.
    Quinlan, J.R., Learning efficient classification procedures and their application to chess end games, In Michalski, R.S., Carbonell, J.G., and Mitchell, T.M. (eds.), Machine Learning — An Artificial Intelligence Approach, Springer-Verlag, pp. 463–482 (1984).Google Scholar
  7. 7.
    Sleeman, D., Brown, J.S., Intelligent tutoring systems, Academic Press (1982).Google Scholar
  8. 8.
    Soldato, T.D., Detecting and reacting to the learner's motivation state, In Frasson, C. and et al. (eds.), Intelligent Tutoring Systems, Lecture Notes in Computer Science 608, Springer-Verlag, pp.567–574 (1992).Google Scholar
  9. 9.
    Thomas, L., and et al., Cognitive modeling and the development of an ITS, Proceedings of 11th Congress of the International Ergonomics Association, pp.616–618 (1991).Google Scholar
  10. 10.
    Wenger, E., Artificial intelligence and tutoring systems, Morgan Kaufmann (1987).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Yukihiro Matsubara
    • 1
  • Mitsuo Nagamachi
    • 1
  1. 1.Faculty of EngineeringHiroshima UniversityHigashi-HiroshimaJapan

Personalised recommendations