filename     : Kae14b.pdf
entry        : article
journal	  : 
pages        : 188-198
year         : 2014
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title        : Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks
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author       : Tanja K\"aser, Severin Klingler, Alexander G. Schwing, and Markus Gross
booktitle    : Proceedings of ITS
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language     : English
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abstract     : 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.