Clinical-Reasoning Skill Acquisition through Intelligent Group Tutoring
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1425-1431.
Abstract
This paper describes COMET, a collaborative intelligent tutoring system for medical problembased learning. COMET uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. Generic domainindependent 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).
@inproceedings{ijcai2005p38,
title = {Clinical-Reasoning Skill Acquisition through Intelligent Group Tutoring},
author = {Suebnukarn, Siriwan and Haddawy, Peter},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1425--1431},
year = {2005},
url = {https://ijcai.org/proceedings/2005/38},
}
TY - CPAPER TI - Clinical-Reasoning Skill Acquisition through Intelligent Group Tutoring AU - Suebnukarn, Siriwan AU - Haddawy, Peter T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1425 EP - 1431 UR - https://ijcai.org/proceedings/2005/38 ER -Copied
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