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It models student knowledge in a Hidden Markov Model as a latent variable, updated by observing the correctness of each student's interaction in which they apply the skill in question.
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.
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