Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Elements of information theory
Elements of information theory
Attribute-mastery patterns from rule space as the basis for student models in algebra
International Journal of Human-Computer Studies
Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using Mutual Information for Adaptive Student Assessments
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Bayes nets in educational assessment: Where the numbers come from
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
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This paper presents recently discovered properties of mutual information between concepts and dichotomous test items. The properties generalize some common intuitions for comparing test items, and provide principled foundations for designing item-selection heuristics for student assessments in computer-assisted educational systems. We compare performance profiles achieved by systems that adopt mutual information and the Mahalanobis distance in the assessment task. Experimental results reveal that, all else being equal, the mutual information based methods offer better performance profiles. In addition, experimental results suggest that, when computing mutual information online is considered computationally costly, heuristics that are designed based on our theoretical findings serve as a good delegate for exact mutual information.