Knowledge assessment: tapping human expertise by the QUERY routine
International Journal of Human-Computer Studies
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
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
Bayesian networks in educational testing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Computer adaptive testing: comparison of a probabilistic network approach with item response theory
UM'05 Proceedings of the 10th international conference on User Modeling
International Journal of Human-Computer Studies
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The problem of modeling and assessing an individual's ability level is central to learning environments. Numerous approaches exists to this end. Computer Adaptive Testing (CAT) techniques, such as IRT and Bayesian posterior updating, are amongst the early approaches. Bayesian networks and graphs models are more recent approaches to this problem. These frameworks differ on their expressiveness and on their ability to automate model building and calibration with empirical data. We discuss the implication of expressiveness and data-driven properties of different frameworks, and analyze how it affects the applicability and accuracy of the knowledge assessment process. We conjecture that although expressive models such as Bayesian networks provide better cognitive diagnostic ability, their applicability, reliability, and accuracy is strongly affected by the knowledge engineering effort they require. We conclude with a comparative analysis of data driven approaches and provide empirical estimates of their respective performance for two data sets.