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Predicting students’ academic performance using e-learning logs
Copyright policy ) Malak Abdullah; Mahmoud Al-Ayyoub; Farah Shatnawi; Saif Rawashdeh; Rob Abbott;
Malak Abdullah; Mahmoud Al-Ayyoub; Farah Shatnawi; Saif Rawashdeh; Rob Abbott;
Predicting students’ academic performance using e-learning logs
The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students’ academic performances using e-learning data. Consequently, we collected students’ datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student’s academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students’ performances during the semester.
- University of North Carolina at Chapel Hill United States
- University of North Carolina at Charlotte United States
- Jordan University of Science and Technology Jordan
Information Systems and Management, Coronavirus disease 2019, Artificial Intelligence, Control and Systems Engineering, Machine learning, Electrical and Electronic Engineering, E-learning, Jordan University of Science and Technology, Correlation
Information Systems and Management, Coronavirus disease 2019, Artificial Intelligence, Control and Systems Engineering, Machine learning, Electrical and Electronic Engineering, E-learning, Jordan University of Science and Technology, Correlation
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[6] M. Abdullah, M. Al-Ayyoub, S. AlRawashdeh, and F. Shatnawi, “E-learningDJUST: e-learning dataset from Jordan University of science and technology toward investigating the impact of COVID-19 pandemic on education,” Neural Computing and Applications, pp. 1-15, 2021, doi: https://doi.org/10.1007/s00521-021-06712-1. [OpenAIRE]
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[10] S. B. Kotsiantis, “Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades,” Artificial Intelligence Review, vol. 37, no. 4, pp. 331-344, 2012, doi: https://doi.org/10.1007/s10462-011-9234-x.
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Related to Research communities
- University of North Carolina at Chapel Hill United States
- University of North Carolina at Charlotte United States
- Jordan University of Science and Technology Jordan
The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students’ academic performances using e-learning data. Consequently, we collected students’ datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student’s academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students’ performances during the semester.