Abstract
Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and group basis with the resulting estimates being useful to both learners and educators for feedback and educational quality improvement. In this primer, we review various descriptive and modelling techniques appropriate to learning curves including smoothing, regression modelling and application of the Thurstone model. Using an example dataset we demonstrate each technique as it specifically applies to learning curves and point out limitations.
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Electronic Supplementary File 1
Spreadsheet with Raw Data and Excel Code for generating smoothed descriptive learning curves (Figures 5, 6) (XLSX 66 kb)
Electronic Supplementary File 2
Worked Examples including R statistical code for generating each of the analyses in the manuscript (Figures 7–12). Statistical code for Stata and for IBM SPSS is available at: http://short.med.nyu.edu/LearningCurvesPrimer (PDF 481 kb)
Electronic Supplementary File 3
Raw data for the 38 learners making up the radiology dataset (CSV 177 kb)
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Pusic, M.V., Boutis, K., Pecaric, M.R. et al. A primer on the statistical modelling of learning curves in health professions education. Adv in Health Sci Educ 22, 741–759 (2017). https://doi.org/10.1007/s10459-016-9709-2
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DOI: https://doi.org/10.1007/s10459-016-9709-2