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Statistical models for e-learning data
Authors:Silvia Figini  Paolo Giudici
Institution:(1) University of Pavia, Strada Nuova 65, Pavia, Italy
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.
Keywords:E-learning data  Predictive models  Nonparametric statistical methods
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