The Role of Metacognition and Self-regulation on Clinical Reasoning: Leveraging Multimodal Learning Analytics to Transform Medical Education

Abstract

Medical errors are defined as preventable adverse effects of health care that often result from faulty clinical reasoning processes. Moreover, studies find that medical errors have been linked to failures in clinical-reasoning strategies and medical education, suggesting a need to transform curricula that prioritizes the knowledge, skills, and abilities [KSBs] needed for effective clinical-reasoning practices. In this chapter, first we discuss the need to incorporate metacognition, the process of monitoring and evaluating one’s own clinical reasoning, and self-regulation, the ability to adapt one’s clinical-reasoning strategies to address these significant issues within medical education. However, challenges exist within medical education because most educational programs rely on ‘snapshots’ of students’ performance (e.g., pre/post) to define competency using standardized assessments and self-report methodologies, missing information on [KSBs] during training activities. Next, we introduce multimodal learning analytics as a novel research approach for studying the role of metacognition and self-regulation on clinical-reasoning processes as they unfold using multiple streams of time series data, such as eye movements, facial expressions, concurrent verbalizations, physiological data, and many others, during medical education and training with emerging technologies with a specific focus on the socio-cognitive cyclic model of self-regulated learning by Zimmerman and Moylan (Self-regulation: Where metacognition and motivation intersect. In Handbook of metacognition in education. Routledge, London, pp. 311–328, 2009). Finally, we discuss implications for utilizing multimodal learning analytics to transform the KSAs needed for the next generation of medical professionals.