Chen Lin, Min Chi
Bayesian Knowledge Tracing (BKT) is one of the most widely adopted student modeling methods in Intelligent Tutoring Systems (ITSs). Conventional BKT mainly leverages sequences of observations (e.g. correct, incorrect) from student-system interaction log files to infer student latent knowledge states (e.g. unlearned, learned). However, the model does not take into account the instructional interventions that generate those observations. On the other hand, we hypothesized that various types of instructional interventions can impact student’s latent states differently. Therefore, we proposed a new student model called Intervention-Bayesian Knowledge Tracing (Intervention-BKT). Our results showed the new model outperforms conventional BKT and two factor analysis based alternatives: Additive Factor Model (AFM) and Instructional Factor Model (IFM); moreover, the learned parameters of Intervention-BKT can recommend adaptive pedagogical policies.
The final publication is available at Springer via https://doi.org/10.1007/978-3-319-39583-8_20.