Abstract
Modeling cognitive processes in clinical learning environments is a necessary first step towards improving learning assessment and medical practice by using an alternative assessment model. Verbal protocol and cognitive content analyses are effective methods of exploring such cognitive processes. For the purpose of simplifying the discussion, we have labeled these processes as Identification of Information, Advanced Cognition, and Medical Cognitive Action. Exploring problem solving processes with Bayesian network techniques can characterize students' dynamic learning processes quantitatively, identify differences in cognitive components at different stages of learning and better represent clinical problem solving features.We develop a hierarchical cognitive model as a cognitive assessment tool to describe the complex cognitive network relations, which can be applied to various clinical cognitive situations. The study concludes that the cognitive model was useful in identifying students' learning trajectories by representing the different cognitive features.
Highlights
An examination of medical student clinical learning through verbal protocol analysis can contribute to our understanding of the diverse aspects of problem solving (Lajoie, Greer, Munsie, Wilkie, Guerrera, & Aleong, 1995; Lu, 2007; Rose, Bearman, Naweed, & Dorrian, 2019)
Dialectical pluralism as a paradigmatic framework is appropriate in the study to assemble both data from the cognitive task analysis and the one represented in a Bayesian network model
This study explores a cognitive process indicating how medical students recognize information, experience deep cognition, and take action in a simulated emergency medicine situation
Summary
An examination of medical student clinical learning through verbal protocol analysis can contribute to our understanding of the diverse aspects of problem solving (Lajoie, Greer, Munsie, Wilkie, Guerrera, & Aleong, 1995; Lu, 2007; Rose, Bearman, Naweed, & Dorrian, 2019). Learning trajectories have several signposts where learning transitions can take place These signposts can be represented as critical cognitive components applied in describing problem solving processes (Zhang & Frederiksen, 2007; Zhang & Lu, 2014a). Zhang and Frederiksen (2007) have explored the learning trajectory concept for statistics problems that require the use of ANOVA. The learning trajectory concept reflects the dynamic nature of learning and that there are transitional processes that lead to expertise. They developed a Bayesian network model based on the cognitive model of the domain. The procedures are applied to this investigation where we analyze clinical problem solving through a verbal protocol analysis
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