Learning Analytics in Medical Education Assessment: The Past, the Present, and the Future

Abstract

With the implementation of competency-based medical education (CBME) in emergency medicine, residency programs will amass substantial amounts of qualitative and quantitative data about trainees' performances. This increased volume of data will challenge traditional processes for assessing trainees and remediating training deficiencies. At the intersection of trainee performance data and statistical modeling lies the field of medical learning analytics. At a local training program level, learning analytics has the potential to assist program directors and competency committees with interpreting assessment data to inform decision making. On a broader level, learning analytics can be used to explore system questions and identify problems that may impact our educational programs. Scholars outside of health professions education have been exploring the use of learning analytics for years and their theories and applications have the potential to inform our implementation of CBME. The purpose of this review is to characterize the methodologies of learning analytics and explore their potential to guide new forms of assessment within medical education.

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