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
Modeling individual and collaborative problem-solving in medical problem-based learning
User Modeling and User-Adapted Interaction
Artificial Intelligence in Medicine
An intelligent tutoring system for visual classification problem solving
Artificial Intelligence in Medicine
A collaborative medical case authoring environment based on the UMLS
Journal of Biomedical Informatics
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
EpiList II: closing the loop in the development of generic cognitive skills
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expanding the Space of Plausible Solutions in a Medical Tutoring System for Problem-Based Learning
International Journal of Artificial Intelligence in Education
METEOR: medical tutor employing ontology for robustness
Proceedings of the 16th international conference on Intelligent user interfaces
Clinical reasoning gains in medical PBL: an UMLS based tutoring system
Journal of Intelligent Information Systems
Training for crisis decision making - An approach based on plan adaptation
Knowledge-Based Systems
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The Collaborative Medical Tutor (COMET) is an intelligent tutoring system for medical problem-based learning (PBL). COMET emulates live human-tutored medical PBL sessions as much as possible while also letting students participate collaboratively from disparate locations. COMET uses Bayesian networks to model both individual and group student knowledge and activity. Generic domain-independent tutoring algorithms use these student and group models to generate tutoring hints. COMET incorporates a multimodal interface that integrates text and graphics in a rich communication channel between the students and the system and among students in the group. A comparison of learning outcomes shows that students using the COMET system achieved significantly higher clinical-reasoning gains than students in human-tutored sessions. This article is part of a special issue on intelligent educational systems.