Tracing Students' Learning Performance on Multiple Skills using Bayesian Methods

Knowledge tracing (KT) is a research field of growing importance in technology enhanced education. It models students' mastery levels of skills and predicts their performance in question answering through an Intelligent Tutoring System (ITS). Traditionally, Bayesian methods like Bayesian Knowledge Tracing (BKT) and Weakest Knowledge Tracing (WKT) apply Hidden Markov Model to model the latent learning process of individual skills. With the advent of recent advances in Deep Learning (DL), DL based KT methods achieve better prediction performance by exploiting the temporal dependencies between consecutive exercises on different skills. However, Bayesian methods are still valuable with a simpler, more intuitive and interpretable model of learning. This paper proposes a new Bayesian method called Corrigible Knowledge Tracing (CKT) which assumes students can learn from mistakes in answering multi-skill questions. In addition, the combinations of high occurrence skills are considered, so that integrated abilities are more fully recognised. In evaluation, the harmonic mean is suggested for combining the predicted probabilities, and accuracy is chosen to be the performance metric. The proposed method is compared with both Bayesian methods and DL approaches by using the conventional question-solving records from a modern large-scale educational data mining dataset called EdNet. Experiment results show that the performance of CKT is on par with state-of-the-art DL approaches. The overall improvement from WKT to the proposed method is substantial.