Expert Systems
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Bayesian networks in educational testing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Tradeoff analysis between knowledge assessment approaches
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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Bayesian and probabilistic networks are claimed to offer powerful approaches to inferring an individual's knowledge state from evidence of mastery of concepts or skills. A typical application where such tools can be useful is Computer Adaptive Testing (CAT). Bayesian networks have been proposed as an alternative to the traditional Item Response Theory (IRT), which has been the prevalent CAT approach for the last three decades. We compare the performance of one probabilistic network approach, named POKS, to the IRT two parameter logistic model. Experimental results over a 34 items UNIX test and a 160 items French language test show that both approaches can classify examinees as master or non master effectively and efficiently. Implications of these results for adaptive testing and student modeling are discussed.