Predicting students' academic performance using e-learning logs
IAES International Journal of Artificial Intelligence
; 12(2):831-839, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2227009
ABSTRACT
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.
Computers--Artificial Intelligence; Coronavirus disease 2019; Correlation; E-learning; Jordan University of Science and Technology; Machine learning; Online instruction; Algorithms; Students; Viral diseases; Performance prediction; Coronaviruses; Academic achievement; Distance learning; Impact prediction; Colleges & universities; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
IAES International Journal of Artificial Intelligence
Year:
2023
Document Type:
Article
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