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International Conference on Intelligent Tutoring Systems

ITS 2016: Intelligent Tutoring Systems pp 208–218Cite as

Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9684))

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.

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Acknowledgments

This research was supported by the NSF Grant 1432156 “Educational Data Mining for Individualized Instruction in STEM Learning Environments”.

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Authors and Affiliations

  1. The Department of Computer Science, North Carolina State University, Raleigh, USA

    Chen Lin & Min Chi

Authors
  1. Chen Lin
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  2. Min Chi
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Correspondence to Chen Lin or Min Chi .

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Editors and Affiliations

  1. Roma Tre University , Rome, Italy

    Alessandro Micarelli

  2. Carnegie Mellon University , Pittsburgh, Pennsylvania, USA

    John Stamper

  3. Neoanalysis Ltd , Athens, Greece

    Kitty Panourgia

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© 2016 Springer International Publishing Switzerland

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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. https://doi.org/10.1007/978-3-319-39583-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-39583-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39582-1

  • Online ISBN: 978-3-319-39583-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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