Spectral Bayesian Knowledge Tracing.

Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student’s latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is also treated as a binary variable. While the BKT served the ITS community well, representing both the latent state and the observed performance as binary variables is, nevertheless, a simplification. In addition, BKT as a two-state and two-observation first-order HMM is prone to noise in the data. In this paper, we present work that uses feature compensation and model compensation paradigms in an attempt to conceptualize a more flexible and robust BKT model. Validation of this approach on the KDD Cup 2010 data shows a tangible boost in model accuracy well over the improvements reported in the literature.