COMET: A Collaborative Tutoring System for Medical Problem-Based Learning

This paper discussed about the developed collaborative intelligent tutoring system for medical PBL called Comet (collaborative medical tutor). Comet uses Bayesian networks to model the knowledge and activity of individual students as well as small groups. It applies generic tutoring algorithms to these models and generates tutorial hints that guide problem solving. An early laboratory study shows a high degree of agreement between the hints generated by Comet and those of experienced human tutors. Evaluations of Comet's clinical-reasoning model and the group reasoning path provide encouraging support for the general framework.

Authors

• Siriwan Suebnukarn
• Peter Haddawy

Keywords

• Collaboration
• Medical diagnostic imaging
• Problem-solving
• Collaborative work
• Intelligent systems
• Laboratories
• Humans
• Dentistry
• Knowledge management
• Technology management
• Problem-based Learning
• Tutoring Systems
• Bayesian Model
• Medical Students
• Individual Students
• Degree Of Agreement
• Clinical Reasoning
• Knowledge Of Students
• High Degree Of Agreement
• Intelligent Tutoring Systems
• Myocardial Infarction
• Group Of Students
• Head Injury
• Learning Objectives
• Root Node
• Model Domain
• Medical Knowledge
• Stroke Onset
• Pre-test Scores
• Student Model
• Post-test Scores
• Multimodal Interaction
• Medical Concepts
• Conditional Probability Table
• Problem-solving Process
• Kappa Index
• Problem Scenario
• Specific Version
• Natural Language Understanding
• computer-supported collaborative learning
• Bayesian networks
• medicine
• empirical evaluation