# 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 […]

**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:

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*Source: [Wikipedia](https://en.wikipedia.org/wiki/Bayesian_knowledge_tracing)*

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- **URL:** https://wpsearchai.com/bayesian-knowledge-tracing/
- **Published:** 2026-01-28T18:55:48+00:00
- **Modified:** 2026-01-28T18:55:48+00:00
- **Author:** admin
- **Categories:** Educational technology