In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better.…

Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles…

We present an intelligent tutoring system that lets students of Chinese learn words and grammatical constructions. It relies on a Bayesian, linguistically motivated cognitive model that represents the learner's knowledge. This model is dynamically updated given observations about the learner's behaviour in the exercises, and employed at runtime to…