.png){kind=icon}
Return
Predicting Student Performance Through Innovative Machine Learning Techniques
.png){kind=icon}
PRE
AI
DOI：10.1177/24056456261448332.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The digital transformation in education, driven by platforms such as massive open online courses and virtual learning environments, has significantly broadened access to education globally. This study explores how machine learning (ML) can enhance and predict learning outcomes by identifying and supporting at-risk students through predictive models. The goal is to improve academic achievement by analyzing student profiles and addressing multiple contributing factors. In this research, ML classification models, specifically support vector classification (SVC), were employed along with two optimizers: the Ebola optimization algorithm and the attack-leave optimizer. The aim was to enhance the model's performance and prediction accuracy. The results indicate that in the training phase, the support vector-based ebola optimization (SVEO) model achieved an accuracy of 0.900, demonstrating moderate performance compared to the SVC model, which achieved an accuracy of 0.887. The support vector-based attack-leave optimization (SVAL) model, however, outperformed the others with the highest accuracy of 0.936.
Keywords:
massive open online courses
virtual learning environments
academic achievement
metaheuristic algorithms
Journal
W
IF:
0.3
Papers:
13
Citations:
96
Organization
No organization information available
Cited Papers
No cited papers available