Using Machine Learning to Advise a Student Model

  • Beverly Park Woolf
  • Tom Murray
Part of the NATO ASI Series book series (volume 125)


Human learning is complex, dynamic, and non-monotonic. Currently it cannot be accurately modeled or measured, and present-day student models are too simplistic and too static to reason effectively about it. This paper explores several machine learning mechanisms which might enhance the functionality of a student model. Human learning experiments are described demonstrating the spontaneous nature of learning, for which action-oriented student model components are needed. An existing student model, built as part of a physics tutoring system, is described which begins to handle non-monotonic reasoning, makes little commitment to a static model of student knowledge, and uses a Multi-layered representation of inferences about student knowledge. The paper asks how a learning mechanism might inform such a student model and represent the dynamicism and spontaneity of human learning.


machine learning non-monotonic reasoning physics tutoring 


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  1. 1.
    Bruner, J. S.:. The ontogenesis of speech acts. Journal of Child Language, 2, pp. 1–20 (1975)CrossRefGoogle Scholar
  2. 2.
    Cherniac, E. and McDermott, D.: Introduction to Artificial Intelligence. Reading, MA: Addison-Wesley 1986Google Scholar
  3. 3.
    Cohen, P. and Gruber, T.: Reasoning About Uncertainty, A Knowledge Representation Perspective. COINS Technical Report 85–24. Amherst, MA: University of Massachusetts at Amherst 1985Google Scholar
  4. 4.
    DeJong, G.: An introduction to explanation-based learning. In: Exploring Artificial Intelligence ( H. Schrobe and American Association for Artificial Intelligence, eds.). San Mateo, CA: Morgan Kaufmann 1988Google Scholar
  5. 5.
    DeJong, G. F. and Mooney, R. J.: Explanation-based learning: an alternative view. Machine Learning, 1 (2), pp. 145–176 (1986)Google Scholar
  6. 6.
    Eskay, M. and Zweban, M.: Learning search control for constraint-based scheduling. Proceedings of the 8th National Conference on Artificial Intelligence, pp. 908–915. Menlo Park, CA: AAAI 1990Google Scholar
  7. 7.
    Fennema, C. L. Jr.: Interweaving Reason, Action and Perception. Ph.D. Dissertation, University of Massachusetts at Amherst. (Also available as COINS Technical Report 91–56, Amherst, MA: University of Massachusetts at Amherst ) 1991Google Scholar
  8. 8.
    Murray, T. and Woolf, B.: A knowledge acquisition framework for intelligent learning environments. SIGART Bulletin, 2 (2), pp. 1–13 (1991)CrossRefGoogle Scholar
  9. 9.
    Murray, T. and Woolf, B.: Tools for teacher participation in ITS design. In: Proceedings of the 2nd International Conference on Intelligent Tutoring Systems, Montreal, Quebec (C. Frasson, G. Gauthier and G. McCalla, eds.), pp. 593–600, Lecture Notes in Computer Science, Vol. 608, Berlin: Springer-Verlag 1992CrossRefGoogle Scholar
  10. 10.
    Piaget, J.: Genetic Epistemology. New York, NY: Norton 1971Google Scholar
  11. 11.
    Self, J.: Formal approaches to student modelling. Chapter in this volume.Google Scholar
  12. 12.
    Self, J.: User modeling in open learning systems. In: Tutoring and Monitoring Facilities for European Open Learning (J. Whiting and D. Bell, eds.). pp. 219–237, Amsterdam: Elsevier 1987Google Scholar
  13. 13.
    Soloway, E.: Learning = Interpretation + Generalization: A Case Study in Knowledge Directed Learning. Ph.D. thesis, Computer and Information Science Department, University of Massachusetts 1978Google Scholar
  14. 14.
    VanLehn, K.: Student modeling. In: Foundations of Intelligent Tutoring Systems (M. Poison and J. Richardson, eds.), pp. 55–78, Hillsdale, NJ: Lawrence Erlbaum Associates 1988Google Scholar
  15. 15.
    Winkels, R.: User modeling. In: EUROHELP, Developing Intelligent Help Systems ( J. Breuker, ed.). Amsterdam: EC 1990Google Scholar
  16. 16.
    Winston, P. H.: Learning structural descriptions from examples. In: The Psychology of Computer Vision, (P. H. Winston, ed.), pp. 157–210 New York, NY: McGraw-Hill 1975Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Beverly Park Woolf
    • 1
  • Tom Murray
    • 1
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherstUSA

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