Student Performance in E-learning Systems. An Empirical Study

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.