Beyond knowledge tracing: Modeling skill topologies with bayesian networks

Tanja Käser, Severin Klingler, Alexander Gerhard Schwing, Markus Gross

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing (BKT). BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies. To ensure model interpretability, we constrain the parameter space. We evaluate the performance of our models on five large-scale data sets of different learning domains such as mathematics, spelling learning and physics, and demonstrate that our approach outperforms BKT in prediction accuracy on unseen data across all learning domains.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings
Number of pages11
ISBN (Print)9783319072203
StatePublished - Jan 1 2014
Externally publishedYes
Event12th International Conference on Intelligent Tutoring Systems, ITS 2014 - Honolulu, HI, United States
Duration: Jun 5 2014Jun 9 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8474 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th International Conference on Intelligent Tutoring Systems, ITS 2014
Country/TerritoryUnited States
CityHonolulu, HI


  • Bayesian networks
  • Knowledge Tracing
  • constrained optimization
  • parameter learning
  • prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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