Learning Analytics to Enhance Dermatopathology Education Among Dermatology Residents

BACKGROUND: With the advent of digital microscopy, learning analytics can be leveraged to advance teaching of dermatopathology in dermatology residency. OBJECTIVE: To analyze the acquisition and decay of dermatopathology visual recognition skills and areas of diagnostic confusion amongst residents using learning metrics generated by a web-based learning tool. METHODS: This was a prospective, longitudinal study of dermatology residents who studied digital photomicrographs of 18 routine diagnostic entities using an online software platform. Residents at different years of training were given 60 minutes to complete assessments on three occasions (initial test with follow-ups at one and three months). RESULTS: 4,938 responses were analyzed. Accuracy and time to diagnosis improved within each assessment and between the first and second assessments. First year residents showed knowledge decay when tested at three months (67% vs 64%; P=0.002) while third year residents retained knowledge and continued to improve upon their accuracy after three months (83% to 91%, <0.001). Learning analytics highlighted diagnostically challenging cases for residents that contradicted experts’ predictions (R=0.48). CONCLUSIONS: The use of learning analytics and interactive digital platforms enhances dermatopathology curriculum development by identifying challenging diagnostic entities, assessing mastery of subject material, and optimizing review schedules. J Drugs Dermatol. 2019;18(12):1231-1236.