Development of a Web-Based Student Academic Performance Prediction Using Machine Learning for Higher Education Institutions
Development of a Web-Based Student Academic Performance Prediction Using Machine Learning for Higher Education Institutions |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Michael Marvin P. Cruz, Ramil G. Lumauag | ||
| DOI : 10.14445/22315381/IJETT-V73I7P133 | ||
How to Cite?
Michael Marvin P. Cruz, Ramil G. Lumauag, "Development of a Web-Based Student Academic Performance Prediction Using Machine Learning for Higher Education Institutions," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.443-457, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P133
Abstract
The rapid rise of predictive analytics and machine learning is changing higher education institutions' student academic performance monitoring. This study introduces HEAPS, a practical and deployable machine learning approach designed to predict at-risk students. This research adopts a comprehensive approach by developing, evaluating, and integrating the selected model into a real-time web application, in contrast to most studies that focus exclusively on identifying the best-performing model. A comparison was conducted between a singular model, Enhanced Random Forest (ERF), and a stacking ensemble model that integrates Random Forest and XGBoost as both base and meta-classifiers. Although the ensemble model exhibited superior prediction accuracy across key evaluation metrics, the ERF model achieved a significantly faster training and inference time. This difference emphasizes a critical trade-off between model complexity and computational efficiency. The results provide practical guidance for researchers and practitioners in model selection, emphasizing accuracy and real-world applicability, thereby ensuring that implemented systems are both efficient and responsive. Usability evaluation showed that HEAPS is an accessible and effective tool for academic intervention, connecting algorithmic research with educational implementation.
Keywords
Academic intervention, Early prediction system, Higher education, Machine Learning, Student academic performance.
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