Malak Abdullah, Mahmoud Al-Ayyoub, Saif AlRawashdeh, Farah Shatnawi
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To the best of our knowledge, it is the first collected dataset that reflects the students' study progress within a Jordanian institute using e-learning system records. One of this work's key findings is observing a high correlation between e-learning events and the final grades out of 100. Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. However, the proportion of students with high marks remained similar to that of pre-pandemic courses.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590139/pdf/","citationCount":"0","resultStr":"{\"title\":\"E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.\",\"authors\":\"Malak Abdullah, Mahmoud Al-Ayyoub, Saif AlRawashdeh, Farah Shatnawi\",\"doi\":\"10.1007/s00521-021-06712-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Recently, the COVID-19 pandemic has triggered different behaviors in education, especially during the lockdown, to contain the virus outbreak in the world. 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Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. 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E-learningDJUST: E-learning dataset from Jordan university of science and technology toward investigating the impact of COVID-19 pandemic on education.
Recently, the COVID-19 pandemic has triggered different behaviors in education, especially during the lockdown, to contain the virus outbreak in the world. As a result, educational institutions worldwide are currently using online learning platforms to maintain their education presence. This research paper introduces and examines a dataset, E-LearningDJUST, that represents a sample of the student's study progress during the pandemic at Jordan University of Science and Technology (JUST). The dataset depicts a sample of the university's students as it includes 9,246 students from 11 faculties taking four courses in spring 2020, summer 2020, and fall 2021 semesters. To the best of our knowledge, it is the first collected dataset that reflects the students' study progress within a Jordanian institute using e-learning system records. One of this work's key findings is observing a high correlation between e-learning events and the final grades out of 100. Thus, the E-LearningDJUST dataset has been experimented with two robust machine learning models (Random Forest and XGBoost) and one simple deep learning model (Feed Forward Neural Network) to predict students' performances. Using RMSE as the primary evaluation criteria, the RMSE values range between 7 and 17. Among the other main findings, the application of feature selection with the random forest leads to better prediction results for all courses as the RMSE difference ranges between (0-0.20). Finally, a comparison study examined students' grades before and after the Coronavirus pandemic to understand how it impacted their grades. A high success rate has been observed during the pandemic compared to what it was before, and this is expected because the exams were online. However, the proportion of students with high marks remained similar to that of pre-pandemic courses.
期刊介绍:
Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems.
All items relevant to building practical systems are within its scope, including but not limited to:
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applicable neural networks theory-
applied statistics-
architectures-
artificial intelligence-
benchmarks-
case histories of innovative applications-
fuzzy logic-
genetic algorithms-
hardware implementations-
hybrid intelligent systems-
intelligent agents-
intelligent control systems-
intelligent diagnostics-
intelligent forecasting-
machine learning-
neural networks-
neuro-fuzzy systems-
pattern recognition-
performance measures-
self-learning systems-
software simulations-
supervised and unsupervised learning methods-
system engineering and integration.
Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.