Using "big data" to guide implementation of a web and mobile adaptive learning platform for medical students

Adaptive learning platforms (ALPs) can revolutionize medical education by making learning more efficient, but their potential has not been realized because students do not use them persistently.

We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( www.osmosis.org ) from 1 August 2014 to 31 July 2015. Multivariate logistic regressions modeled persistence on Osmosis as the dependent variable and Osmosis-collected variables as predictors.

The 6787 students included in our analysis responded to a total of 887,193 items, with 2138 (31.5%) using Osmosis persistently. Number of items per student, mobile device use, subscription payment, and group membership were independently associated with persisting (p < 0.001 in all models). Persistent users rated quality more favorably (p < 0.01) but were not more confident in answer selections (p = 0.80). While persisters were more accurate than non-persisters (55% (SD 18%) vs 52% (SD 22%), p < 0.001), after adjusting for number of items, lower accuracy was associated with persistent use (OR 0.93 [95% CI 0.90-0.97], p < 0.01).

Our study of a large sample of U.S. medical students illustrates big data medical education research and provides guidance for improving implementation of ALPs and further investigation.