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

Abstract

Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching as an alternative to traditional didactic medical education to teach clinical-reasoning skills at the early stages of medical education. While PBL has many strengths, effective PBL tutoring is time-intensive and 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 the student modeling approach used in the COMET intelligent tutoring system for collaborative medical PBL. To generate appropriate tutorial actions, COMET uses a model of each student's clinical reasoning for the problem domain. In addition, since problem solving in group PBL is a collaborative process, COMET uses a group model that enables it to do things like focus the group discussion, promote collaboration, and suggest peer helpers. Bayesian networks are used to model individual student knowledge and activity, as well as that of the group. The validity of the modeling approach has been tested with student models in the areas 212 User Model User-Adap Inter (2006) 16:211-248 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 the focus of 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).

Psychometric properties of a clinical reasoning assessment rubric for nursing education

“…This rubric had moderate accuracy according to Greiner et al’s [ 38 ] standard. Similarly, Suebnukarn & Haddawy [ 39 ] developed a problem-based learning model for medical students that included clinical reasoning processes such as problem identification, problem analysis, hypothesis reporting and that had an AUC of 0.878, which was consistent with this study. The cut-off score is a significant indicator for evaluating scale accuracy in screening measurements.…”

Unveiling emotion dynamics in problem-solving: a comprehensive analysis with an intelligent tutoring system using facial expressions and electrodermal activities

“…Intelligent tutoring systems offer medical students extensive guided practice at a cost-effective rate while motivating them through diverse media and materials (Dharmathilaka et al, 2023). For example, Suebnukarn and Haddawy (2006) developed an intelligent tutoring system named COMET to stimulate group discussion and recommend peer assistance. Kazi et al (2013) enhanced the capabilities of intelligent tutoring systems for clinical problems by incorporating a comprehensive medical knowledge source.…”