Evaluating Student Performance in E-learning Systems: A Two-step Robust Bayesian Multiclass Procedure

Antonio Pacifico, Luca Giraldi and Elena Cedrola

MPRA Paper from University Library of Munich, Germany

Abstract: This paper addresses a computational method to evaluate student performance through convolutional neural network. Image recognition and processing are fundamentals and current trends in deep learning systems, mainly with the outbreak in coronavirus infection. A two-step system is developed combining a first-step robust Bayesian model averaging for selecting potential candidate predictors in multiple model classes with a frequentist second-step procedure for estimating the parameters of a multinomial logistic regression. Methodologically, parametric conjugate informative priors are used to deal with model uncertainty and overfitting, and Markov Chains algorithms are designed to construct exact posterior distributions. An empirical example to the use of e-learning systems on student performance analysis describes the model's functioning and estimation performance. Potential prevention policies and strategies to address key technology factors affecting e-learning tools are also discussed.

Keywords: E-learning systems; Student Performance; Bayesian Inference; Policy Issues; Logistic Regression; Variable Selection Procedure. (search for similar items in EconPapers)
JEL-codes: C1 C5 O1 (search for similar items in EconPapers)
Date: 2023-02
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https://mpra.ub.uni-muenchen.de/117394/1/Manuscript_AP.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/117397/1/Manuscript_AP.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:117394

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