Abstract
Student modelling is a centrally important issue in the construction of intelligent learning environments and intelligent tutoring systems. Without a student model, it is impossible for such a learning environment to adapt to the needs of individual learners. Unfortunately, student modelling is also a very difficult problem. It touches on many of the great issues of artificial intelligence and cognitive science: diagnosis, belief revision and truth maintenance, qualitative reasoning, mental modelling, temporal reasoning, non-monotonic and probabilistic reasoning, testing and evaluation, etc. Student modelling provides both a focus for the exploration of these issues, as well as an original twist on many of them. The original twist arises due to two main factors that are central to student modelling but are often not important in other applications. The first of these is the impossibility of keeping a completely accurate model of the learner, which forces the student model to deal with inherent uncertainty and incompleteness. The second factor is the constant revision the learner undergoes in his or her perceptions of the domain of study as the instructional interaction proceeds, a feature that presents a constantly moving target for the student modelling subsystem. In this paper I discuss the importance of student modelling, the key issues that must be tackled, and the prospects for resolving these issues in the short and long term.
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McCalla, G.I. (1996). Student Modelling: A Crucible for Research. In: Liao, T.T. (eds) Advanced Educational Technology: Research Issues and Future Potential. NATO ASI Series, vol 145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60968-8_9
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DOI: https://doi.org/10.1007/978-3-642-60968-8_9
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