A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system

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ObjectivesToday a great many medical schools have turned to a problem-based learning (PBL) approach to teaching. While PBL has many strengths, effective PBL requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of our work is the development of representational techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. The system combines concepts from computer-supported collaborative learning with those from intelligent tutoring systems.

论文关键词:Intelligent tutoring systems,Computer-supported collaborative learning,Bayesian networks,Medicine,Problem-based learning

论文评审过程:Received 26 July 2004, Revised 31 March 2005, Accepted 20 April 2005, Available online 23 September 2005.

论文官网地址:https://doi.org/10.1016/j.artmed.2005.04.003