Student Performance in E-learning Systems: An Empirical Study
Student Performance in E-learning Systems: An Empirical Study

Author(s): Antonio PACIFICO, Luca GIRALDI, Elena CEDROLA
Subject(s): Social Sciences, Education, Higher Education , Distance learning / e-learning
Published by: RITHA Publishing
Keywords: machine learning; student performance; Bayesian inference; e-learning platforms; logistic regression; variable selection procedure;
Summary/Abstract: This research paper focuses on using a convolutional neural network to assess student performance and addresses the impact of the COVID-19 pandemic on education. It introduces a two-step system that combines robust Bayesian model averaging with a frequentist approach for estimating parameters in a multinomial logistic regression model. The authors provide an empirical example illustrating the application of this system in analysing student performance. They also explore strategies to improve e-learning tools by addressing technological factors. The paper contributes to educational evaluation and policy analysis by incorporating deep learning systems and addressing the challenges posed by the pandemic.

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