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

Ashwin Menon, Shiv Gaglani, M. Ryan Haynes, Sean Tackett

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Background: 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. Methods: We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( 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. Results: 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). Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)975-980
Number of pages6
JournalMedical teacher
Issue number9
StatePublished - Sep 2 2017

ASJC Scopus subject areas

  • Education


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