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All Journal Narra J Heca Journal of Applied Sciences Journal of Educational Management and Learning Grimsa Journal of Science Engineering and Technology
Kemala, Pati
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Journal : Journal of Educational Management and Learning

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Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.
Co-Authors - Fakhrurrazi Ahmad, Khairunnas Asep Rusyana Binawati Ginting Boy M. Bachtiar Destiana, Khaerunisa Diana Setya Ningsih, Diana Hakim, Rachmi F. Hanafiah, Olivia A. Harera, Cheariva Firsa Hizir Sofyan Idroes, Ghazi M. Idroes, Ghazi Mauer Khairan Khairan Lubis, Vanizra F. Mauer Idroes, Ghazi Maulana, Aga Maulydia, Nur B. Maulydia, Nur Balqis Maysarah, Hilda Mirda, Erisna Muliadi Ramli Muslem Muslem, Muslem Ningsih, Diana S. Noviandy, Teuku R. Patwekar, Mohsina Putra, Noviandi I. Rinaldi Idroes Romadhoni, Yenni Sasmita, Novi Reandy Shafira, Ghina A. Trina E. Tallei, Trina E. TRINA EKAWATI TALLEI Zuchra Helwani, Zuchra