Clinical-Reasoning Skill Acquisition through Intelligent Group Tutoring

This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning.  COMET uses Bayesian networks to model individual student knowledge and activity, as well as that of the group.  Generic domain-independent 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).