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 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 use BKT for mastery learning and problem sequencing. In its most common implementation, BKT has only skill-specific parameters. Method. There are 4 model parameters used in BKT: Assuming that these parameters are set for all skills, the following formulas are used as follows: The initial probability of a student formula_13 mastering skill formula_14 is set to the p-init parameter for that skill equation (a). Depending on whether the student formula_13 learned and applies skill formula_14 correctly or incorrectly, the conditional probability is computed by using equation (b) for correct application, or by using equation  (c) for incorrect application. The conditional probability is used to update the probability of skill mastery calculated by equation (d). To figure out the probability of the student correctly applying the skill on a future practice is calculated with equation (e). Equation (a): formula_17 Equation (b): formula_18 Equation (c): formula_19 Equation (d): formula_20 Equation (e): formula_21