Measuring Emotions in Medical Education: Methodological and Technological Advances Within Authentic Medical Learning Environments

Abstract

Emotions serve an important role in learning and performance, yet their role in medical education has been largely overlooked. In this chapter, we examine how multiple research methodologies and measures can be used to detect and analyze emotions within authentic medical learning environments. Our goal is to highlight conceptual, methodological, and practical considerations that should be attended to by researchers, educators, and medical professionals interested in examining the role of emotions within medical education. Findings from our literature review and empirical work suggest that appraisal models that treat emotions as multi-componential (e.g., control-value theory) can provide a fruitful framework for examining links between emotions and learning. In terms of measuring emotions, self-report can be useful with respect to scalability and capturing subjective experience, whereas behavioral and physiological measures provide continuous data streams and are less susceptible to cognitive or memory biases. Other factors researchers and health sciences professionals should take into consideration when selecting a measure of emotion include: efficiency; level of granularity; and person-centered versus group-level analyses. Recent work suggests that multiple measures of emotions can be integrated into affect-aware learning technologies to aid instructional design by detecting, tracing, and modeling emotional processes during learning.