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.

Student Performance in E-learning Systems. An Empirical Study

Pacifico A.;Giraldi L.;Cedrola E.
2023-01-01

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.
2023
978-606-95516-1-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/324730
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