Date of Original Version

7-2013

Type

Conference Proceeding

Rights Management

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39112-5_18

Abstract or Description

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.

DOI

10.1007/978-3-642-39112-5_18

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Published In

Lecture Notes in Computer Science, 7926, 171-180.