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 four model parameters used in BKT:

• ${\displaystyle p(L_{}}$

…

Source: Wikipedia