Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing

Conference paper

DOI: 10.1007/978-3-319-39583-8_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)
Cite this paper as:
Lin C., Chi M. (2016) Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing. In: Micarelli A., Stamper J., Panourgia K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science, vol 9684. Springer, Cham

Abstract

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.

Keywords

Knowledge tracing Hidden Markov Model Input Output Hidden Markov Model Student modeling Instructional intervention 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The Department of Computer ScienceNorth Carolina State UniversityRaleighUSA

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