Using Machine Learning to Advise a Student Model

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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.

This chapter also appeared in the Journal of Artificial Intelligence in Education, 3(4), pp. 429 (1993). Reprinted with permission of the Association for the Advancement of Computing in Education (AACE, P.O. Box 2966, Charlottesville, VA 22920 USA).