Bayesian knowledge tracing
Bayesian Knowledge Tracing is an algorithm used in many intelligent tutoring systems to model each learner's mastery of the knowledge being tutored.
It models student knowledge in a Hidden Markov Model as a latent variable, updated by observing the correctness of the each student interaction in which they apply the skill in question.[1]
BKT assumes that student knowledge is represented as a set of binary variables, one per skill, where the skill is either mastered by the student or not. Observations in BKT are also binary: a student gets a problem/step either right or wrong. Intelligent tutoring systems often uses BKT for mastery learning and problem sequencing. In its most common implementation, BKT has only skill-specific parameters. [2]
References
- ^ Corbett, A. T.; Anderson, J. R. (1995). "Knowledge tracing: Modeling the acquisition of procedural knowledge". User Modeling and User-Adapted Interaction. 4 (4): 253–278.
- ^ Yudelson, M.V.; Koedinger, K.R.; Gordon, G.J. (2013). "Individualized bayesian knowledge tracing models". Artificial Intelligence in Education.
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