Student Modeling and Machine Learning

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Sison , Raymund ; Shimura , Masamichi (1998)
  • Publisher: HAL CCSD
  • Subject: student modelling | machine learning | [ INFO.EIAH ] Computer Science [cs]/Technology for Human Learning
After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine learning. (http://aied.inf.ed.ac.uk/members98/archive/vol_9/sison/full.html)
  • References (108)
    108 references, page 1 of 11

    Anderson, J. R. & Reiser, B. (1985). The LISP tutor. Byte, 10, 159-175.

    Anderson, J. R., Boyle, C., Corbett, A., & Lewis, M. (1990). Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42, 7-49.

    Anderson, J. R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.

    Anderson, J. R. (1989). A theory of the origins of human knowledge. Artificial Intelligence, 40, 313-351.

    Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.

    Baffes, P. & Mooney, R. (1993). Symbolic revision of theories with m-of-n rules. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence.

    Baffes, P. & Mooney, R. (1996). Refinement-based student modeling and automated bug library construction. Journal of Artificial Intelligence in Education, 7(1), 75- 116.

    Bierman, D., Kamsteeg, P., & Sandberg, J. (1992). Student models, scratch pads, and simulation. In E. Costa (Ed.), New Directions for Intelligent Tutoring Systems. Berlin: Springer Verlag.

    Booker, L., Goldberg, D., & Holland, J. (1989). Classifier systems and genetic algorithms. Artificial Intelligence, 40, 235-282.

    Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth.

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