Examining the Relationship Between Performance Feedback and Emotions in Diagnostic Reasoning: Toward a Predictive Framework for Emotional Support

摘要

The purpose of this research is to understand achievement emotions resulting from performance feedback in a medical education context where 30 first and second year medical students learned to diagnose virtual patients in an intelligent tutoring system (ITS), BioWorld. We found that students could be organized into groups using cluster analyses based on the emotions they reported after receiving performance feedback: a positive emotion cluster, negative emotion cluster, and low overall emotion cluster. Medical students in the positive achievement emotion cluster had the highest performance on the diagnostic reasoning cases; those in the negative achievement emotion cluster had the lowest performance; and students categorized as belonging to the low overall achievement emotion cluster had mean performance levels that fell between the two. From the results we propose critical performance thresholds that can be used to predict emotions following performance feedback.