Bayesian Knowledge Tracing
Bayesian knowledge tracing (BKT) is a model that estimates, after each problem a student attempts, the probability that the student has mastered the underlying skill. Introduced by Corbett and Anderson for intelligent tutoring systems, it is a two-state hidden Markov model: the latent variable is whether the skill is learned or not, and observed correct/incorrect responses update that latent state through Bayesian inference. With just four parameters — initial knowledge, learning, slip, and guess — BKT drives the mastery decisions that tell a tutor when a student can move on.
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Sources
- Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. DOI: 10.1007/BF01099821 ↗
- Baker, R. S. J. d., Corbett, A. T., & Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In Intelligent Tutoring Systems (ITS 2008), LNCS 5091, 406–415. DOI: 10.1007/978-3-540-69132-7_44 ↗
How to cite this page
ScholarGate. (2026, June 22). Bayesian Knowledge Tracing for Modeling Skill Mastery. ScholarGate. https://scholargate.app/en/education/bayesian-knowledge-tracing
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