Suebnukarn Siriwan, Haddawy Peter
Computer Science and Information Management Program, Asian Institute of Technology, Pathumthani 12120, Thailand.
Artif Intell Med. 2006 Sep;38(1):5-24. doi: 10.1016/j.artmed.2005.04.003. Epub 2005 Sep 23.
Today 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.
The system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. The prototype system incorporates substantial domain knowledge in the areas of head injury, stroke and heart attack. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. In order to evaluate the appropriateness and quality of the hints generated by our system, we compared the tutoring hints generated by COMET with those of experienced human tutors. We also compared the focus of group activity chosen by COMET with that chosen by human tutors.
On average, 74.17% of the human tutors used the same hint as COMET. The most similar human tutor agreed with COMET 83% of the time and the least similar tutor agreed 62% of the time. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.652, kappa=0.773). The focus of group activity chosen by COMET agrees with that chosen by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.774, kappa=0.823).
Bayesian network clinical reasoning models can be combined with generic tutoring strategies to successfully emulate human tutor hints in group medical PBL.
如今，许多医学院校已转向基于问题的学习（PBL）教学法。虽然PBL有诸多优点，但有效的PBL要求导师高度关注学生，而在当前对教师时间要求日益增加的学术环境中，这很难做到。本文介绍了一种用于PBL的协作式医学导师中的智能辅导。我们工作的主要贡献在于开发了用于在PBL小组问题解决中生成辅导提示的表示技术和算法，并将这些技术应用于协作式智能辅导系统COMET中。该系统将计算机支持的协作学习概念与智能辅导系统的概念相结合。
该系统使用贝叶斯网络对个体学生以及小组的临床推理进行建模。原型系统在头部损伤、中风和心脏病发作等领域纳入了大量领域知识。PBL中的辅导极具挑战性，因为导师应尽可能少地提供指导，同时又不能让学生迷失方向。通过对当地一所医学院PBL课程的研究，我们确定并实施了八种常用的提示策略。为了评估我们系统生成的提示的适当性和质量，我们将COMET生成的辅导提示与经验丰富的人类导师生成的提示进行了比较。我们还比较了COMET选择的小组活动重点与人类导师选择的重点。
平均而言，74.17%的人类导师使用了与COMET相同的提示。最相似的人类导师在83%的时间里与COMET意见一致，最不相似的导师在62%的时间里意见一致。我们的结果表明，COMET的提示与大多数人类导师的提示在统计上高度一致（麦克尼马尔检验，p = 0.652，kappa = 0.773）。COMET选择的小组活动重点与大多数人类导师选择的重点在统计上高度一致（麦克尼马尔检验，p = 0.774，kappa = 0.823）。
贝叶斯网络临床推理模型可与通用辅导策略相结合，在小组医学PBL中成功模拟人类导师的提示。
