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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Individualized Bayesian Knowledge Tracing Models
verfasst von : Michael V. Yudelson, Kenneth R. Koedinger, Geoffrey J. Gordon
Erschienen in: Artificial Intelligence in Education
Verlag: Springer Berlin Heidelberg
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Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that student-specific variability in the data, when accounted for, could enhance model accuracy [5,6,8]. In this work, we revisit the problem of introducing student-specific parameters into BKT on a larger scale. We show that student-specific parameters lead to a tangible improvement when predicting the data of unseen students, and that parameterizing students’ speed of learning is more beneficial than parameterizing a priori knowledge.