Twenty-Five Years of Bayesian knowledge tracing: a systematic review

The quality of an artificial intelligence-based tutoring system is its ability to observe and interpret student behaviour to infer the preferences and needs of an individual student. The student model enables a comprehensive representation of student knowledge and affects the quality of the other intelligent tutoring system’s (ITS) components. The Bayesian knowledge tracing model (BKT) is one of the first machine learning-based and widely investigated student models due to its interpretability and ability to infer student knowledge. The past Twenty-five Years have seen increasingly rapid advances in the field, so this systematic review deals with the BKT model enhancements by using the PRISMA guidelines and a unique set of criteria, including 13 aspects of enhancements and computational methods. Also, the study reveals two types of evaluation approaches found in the literature, including the prediction of student answers and the ability to estimate knowledge mastery. Overall, the most frequently investigated enhancements extended the vanilla BKT model by including student characteristics and tutor interventions. The educational context-based enhancements of domain knowledge properties, question difficulty and architectural prior knowledge were also frequently investigated enhancements. The expectation–maximization algorithm practically became the standard in estimating BKT parameters. While the enhanced BKT models generally overperformed the vanilla model in predicting the student answer by using the measures such as RMSE (root mean square error), AUC–ROC (area under curve, receiver operating characteristics curve) and accuracy, only a few studies further investigated the systems’ estimations of knowledge mastery by correlating it to knowledge on post-tests. The most frequently used educational platforms included ITSs, Massive Open Online Courses (MOOCs) and simulated environments.