Abstract
In this paper we analyse a real e-learning dataset derived from the e-learning platform of the University of Pavia. The dataset concerns an online learning environment with in-depth teaching materials. The main focus of this paper is to supply a measure of the relative importance of the exercises (test) at the end of each training unit; to build predictive models of student’s performance and finally to personalize the e-learning platform. The methodology employed is based on nonparametric statistical methods for kernel density estimation and generalized linear models and generalized additive models for predictive purposes.
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Figini, S., Giudici, P. Statistical models for e-learning data. Stat Methods Appl 18, 293–304 (2009). https://doi.org/10.1007/s10260-008-0098-4
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DOI: https://doi.org/10.1007/s10260-008-0098-4