Enhancing skill prediction through generalising Bayesian knowledge tracing

Tak-Lam WONG, Di ZOU, Kwok Shing CHENG, Jeff Kai Tai TANG, Yi CAI, Fu Lee WANG

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)358-373
JournalInternational Journal of Mobile Learning and Organisation
Volume15
Issue number4
Early online dateOct 2021
DOIs
Publication statusPublished - 2021

Citation

Wong, T.-L., Zou, D., Cheng, G., Tang, J. K. T., Cai, Y., & Wang, F. L. (2021). Enhancing skill prediction through generalising Bayesian knowledge tracing. International Journal of Mobile Learning and Organisation, 15(4), 358-373. doi: 10.1504/IJMLO.2021.118433

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