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
A collaborative intelligent tutoring system for medical problem-based learning
Proceedings of the 9th international conference on Intelligent user interfaces
Anatomical sketch understanding: Recognizing explicit and implicit structure
Artificial Intelligence in Medicine
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Automated interviews on clinical case reports to elicit directed acyclic graphs
Artificial Intelligence in Medicine
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This paper describes COMET, a collaborative intelligent tutoring system for medical problembased learning. COMET uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. Generic domainindependent tutoring algorithms use the models to generate tutoring hints. We present an overview of the system and then the results of two evaluation studies. The validity of the modeling approach is evaluated in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis indicates that, the models are accurate in predicting individual student actions. Comparison of learning outcomes shows that student clinical reasoning gains from our system are significantly higher than those obtained from human tutored sessions (Mann-Whitney, p = 0.011).