Enhancing skill prediction through generalising Bayesian knowledge tracing

Learning Analytics (LA) have been widely investigated and applied to understand and optimise the learning process and environment. Among a number of LA tools, Bayesian Knowledge Tracing (BKT) was developed aiming at predicting the probability that a skill has been successfully acquired by a learner. While current development has proved BKT to be sufficiently accurate in prediction and useful, the state-of-the-art BKT methods suffer from a number of shortcomings such as the incapability to predict multiple skills learnt by a student. In this paper, we extend the ordinary BKT model to predict unlimited number of skills learned by a learner based on a non-parametric Dirichlet Process (DP). Another characteristic of our approach is that it can easily incorporate prior knowledge to our model resulting in a more accurate prediction. The extended model is more generic and able to handle border applications. We have developed two efficient approximate inference methods based on Gibbs sampling and variational methods. Copyright © 2021 Inderscience Enterprises Ltd.