Despite evidence that learners vary greatly in their learning needs, practical constraints tend to favor “one-size-fits-all” educational approaches, in simulation-based education as elsewhere. Adaptive educational technologies - devices and/or software applications that capture and analyze relevant data about learners to select and present individually tailored learning stimuli - are a promising aid in learners' and educators' efforts to provide learning experiences that meet individual needs. In this article, we summarize and build upon the 2017 Society for Simulation in Healthcare Research Summit panel discussion on adaptive learning. First, we consider the role of adaptivity in learning broadly. We then outline the basic functions that adaptive learning technologies must implement and the unique affordances and challenges of technology-based approaches for those functions, sharing an illustrative example from healthcare simulation. Finally, we consider future directions for accelerating research, development, and deployment of effective adaptive educational technology and techniques in healthcare simulation.
From the Director of Simulation Research, Assessment, and Outcomes & Assistant Professor of Health Policy and Management, Zamierowski Institute for Experiential Learning & Department of Health Policy and Management (M.L.); SimTabs LLC (P.D.), Los Altos Hills, CA; Department of Educational Psychology & Illinois Informatics Institute (H.C.L.), University of Illinois, Urbana-Champaign, Champaign, IL; and USC Institute for Creative Technologies(T.B.T.), Keck School of Medicine, University of Southern California, Los Angeles, CA.
Reprints: Matthew Lineberry, PhD, University of Kansas Medical Center and Health System, 3901 Rainbow Blvd, Sudler Hall G005, Kansas City, KS 66160 (e-mail: firstname.lastname@example.org).
The authors declare no conflict of interest.
P.D. is a co-owner in SimTabs and in Innovation in Learning Inc, developers of custom educational technologies for academic and corporate clients. V-CAEST was developed for the University of Memphis. The article does not endorse that application or make any claims to its effectiveness; it is rather used only as an illustrative example.
M.L. receives grants from the US Army Medical Research Materiel Command, National Science Foundation, and National Board of Medical Examiners. H.C.L. receives grants from the National Science Foundation. T.T. receives grants from the Defense Medical Research Development Program, US Army Medical Research Material Command, Army Research Laboratory, and Uniformed Services University. He consults for Emory University School of Medicine, Charles River Analytics, University of Washington, and the National Board of Medical Examiners.