Knowledge assessment: tapping human expertise by the QUERY routine
International Journal of Human-Computer Studies
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Knowledge Spaces
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Automata for the Assessment of Knowledge
IEEE Transactions on Knowledge and Data Engineering
Student Modeling from Conversational Test Data: A Bayesian Approach Without Priors
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Adaptive Bayesian Networks for Multilevel Student Modelling
ITS '00 Proceedings of the 5th 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
Learning Bayesian Networks
A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory
International Journal of Artificial Intelligence in Education
Bayes nets in educational assessment: Where the numbers come from
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An Efficient Student Model Based on Student Performance and Metadata
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
International Journal of Human-Computer Studies
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Predictive student model supported by fuzzy-causal knowledge and inference
Expert Systems with Applications: An International Journal
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Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We explore their use for modeling item-item relationships, in accordance with the theory of knowledge spaces. We compare two Bayesian frameworks for that purpose, a standard Bayesian network approach and a more constrained framework that relies on a local independence assumption. Their performance is compared over their respective ability to predict item outcome and through simulations over two data sets. The simulation results show that both approaches can effectively perform accurate predictions, but the constrained approach shows higher predictive power than a Bayesian Network. We discuss the applications of item to item structure for cognitive modeling within different contexts.