The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Experiences in Implementing Constraint-Based Modeling in SQL-Tutor
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
A collaborative intelligent tutoring system for medical problem-based learning
Proceedings of the 9th international conference on Intelligent user interfaces
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
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Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. We have used Bayesian networks to model individual student knowledge and activity, as well as that of the group. The validity of the approach has been tested with student models in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823).