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- W4387482393 abstract "During the COVID-19 pandemic, educational institutions worldwide were affected and forced to switch to online education. This paper presents an open-source dataset that examines the influence of the pandemic on students’ e-learning activities and academic performance. The dataset, collected from the C Programming Language course at Chongqing University of Arts and Sciences from 2019 to 2022, includes e-learning activities and final exam grades. Correlation analysis reveals a strong association between final exam scores, online assignments, and experiment results. Regression models are then built using random forest, linear regression, and Bayesian algorithms to investigate the impact of COVID-19. These models exhibit consistent trends, showing improved performance each year after a decline in 2020 and outperforming the results from 2019. This indicates that online learning gradually became the primary factor influencing students’ academic performance after the initial chaotic adaptation period in 2020. The findings of this study provide valuable insights for educators and institutions, enhancing their understanding of the effects of online education and the pandemic on students’ academic performance." @default.
- W4387482393 created "2023-10-11" @default.
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- W4387482393 date "2023-01-01" @default.
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- W4387482393 title "Unveiling the Pandemic’s Impact: A Dataset for Probing COVID-19’s Effects on E-Learning Activities and Academic Performance" @default.
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- W4387482393 doi "https://doi.org/10.1007/978-981-99-7108-4_13" @default.
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