Enhancing academic performance prediction through machine learning in cloud environments

Mirko Talajić (), Robert Matijević () and Zlatan Morić ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 6, 370-395

Abstract: This study aims to enhance the accuracy and applicability of academic performance prediction by integrating machine learning techniques within cloud-based environments. It seeks to address critical gaps in leveraging predictive analytics to support at-risk students and optimize educational outcomes through scalable solutions. The research utilizes a dataset from Portuguese secondary schools, applying advanced machine learning models, including ensemble techniques and cloud-based frameworks such as Azure Machine Learning. Exploratory data analysis, preprocessing techniques like SMOTE for class balancing, and automated machine learning pipelines are employed to develop and evaluate predictive models. The Voting Ensemble model emerged as the most effective, achieving an F1 score of 0.836 and an AUC of 0.973. Historical academic performance, attendance, and parental education were identified as the most influential predictors of student success. The study emphasizes the potential of cloud-integrated machine learning to deliver scalable and interpretable predictive analytics, enabling proactive interventions and promoting equal access to educational opportunities. This study contributes to the field by integrating automated machine learning pipelines with cloud-based solutions, offering a replicable framework for educational institutions. By addressing class imbalance and enhancing model interpretability through feature importance analysis, the research bridges critical gaps in the practical deployment of predictive analytics for academic performance. The findings provide a foundation for future advancements in adaptive, data-driven education systems.

Keywords: Academic performance prediction; Class imbalance handling; Machine learning in education; Smote technique. Voting ensemble model. (search for similar items in EconPapers)
Date: 2025
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