Modeling individual and collaborative problem solving in medical problem-based learning

@inproceedings{citeulike:1074845, abstract = {Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. We have used Bayesian networks to model individual student knowledge and activity, as well as that of the group. The validity of the approach has been tested with student models in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823). {\copyright} Springer-Verlag Berlin Heidelberg 2005. Sponsors: Universita di Torino; University of Canterbury; University of Glasgow}, address = {Computer Science and Information Management Program, Asian Institute of Technology, Pathumthani, 12120, Thailand}, author = {Suebnukarn, S. and Haddawy, P. and Ardissono, L. and Brna, P. and Mitrovic, A. }, citeulike-article-id = {1074845}, journal = {Lect Notes Artif Intell}, keywords = {um2005}, pages = {377--386}, posted-at = {2007-01-29 19:24:16}, priority = {5}, series = {10th International Conference on User Modeling 2005, UM 2005}, title = {Modeling individual and collaborative problem solving in medical problem-based learning}, url = {http://www.scopus.com/scopus/record/display.url?view=extended\&origin=resultslist\&eid=2-s2.0-26944435518}, volume = {3538} }

TY - CONF ID - citeulike:1074845 L3 - citeulike-article-id:1074845 TI - Modeling individual and collaborative problem solving in medical problem-based learning SE - 10th International Conference on User Modeling 2005, UM 2005 JF - Lect Notes Artif Intell VL - 3538 SP - 377 EP - 386 AD - Computer Science and Information Management Program, Asian Institute of Technology, Pathumthani, 12120, Thailand N2 - Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. We have used Bayesian networks to model individual student knowledge and activity, as well as that of the group. The validity of the approach has been tested with student models in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823). © Springer-Verlag Berlin Heidelberg 2005. Sponsors: Universita di Torino; University of Canterbury; University of Glasgow KW - um2005 AU - Suebnukarn, S AU - Haddawy, P AU - Ardissono, L AU - Brna, P AU - Mitrovic, A UR - http://www.scopus.com/scopus/record/display.url?view=extended&origin=resultslist&eid=2-s2.0-26944435518 ER -