Bayesian Knowledge Tracing

A predictive modeling framework that uses Hidden Markov Models to track a student's mastery of specific skills over time.

Corbett and Anderson introduced Bayesian Knowledge Tracing (BKT) in 1994 to power Intelligent Tutoring Systems like Cognitive Tutor. The model uses four parameters (p-init, p-learn, p-guess, and p-slip) to estimate the probability that a learner has mastered a latent skill based on their performance history. By continuously updating these probabilities as students interact with problem sets, BKT allows software to personalize instruction: it identifies exactly when a student has reached the 95% mastery threshold and can move to the next concept.

https://link.springer.com/article/10.1007/BF00118613