Sep 2026· Innovations in Education and Teaching International· Vol 63, pp. 1553 - 1572· 14 citations· ⚡ 1 influential· 47 references
TL;DR
This study employs machine learning approaches to predict the final exam scores of vocational undergraduate students and analyse critical factors influencing their academic performance, indicating monthly exam scores, admission scores and self-study time as the most influential predictors, whereas demographic features were comparatively less significant.
Predicting student academic performance is of significant importance for enabling timely educational interventions and improving educational outcomes in secondary education. However, existing predictive models in educational data mining often face the dual challenges of limited interpretability and suboptimal performan...
Identifying which behavioral and demographic factors most strongly predict student academic performance is a long-standing goal of learning analytics. We apply linear regression, ridge regression, random forest, and gradient boosting to a 1,000-student behavioral and demographic dataset. Five-fold cross-validated R² sh...
Thuy Thi Bich Vo, T. Vu· Journal of Systems Engineeri...· 0 citations
The application of machine learning models in digital education has become increasingly important for predicting student academic performance and supporting early academic interventions. However, predictive accuracy alone is insufficient if models introduce bias against certain demographic groups. This study presents a...
Hamdatul Mabruroh, Rayhan Rizky Widi Ananta, Vinna Rahmayanti Setyaning Nastiti· Jurnal Teknik Informatika (J...· 0 citations
The findings indicate that machine learning methods can effectively support early dropout prediction and decision-support systems in higher education institutions.
Arūnas Mincevičius· New Trends in Computer Scien...· 0 citations
Accurate prediction of learner performance is essential for adaptive educational systems and personalized learning. However, the integration of advanced gradient-boosting models with established educational frameworks remains underexplored, particularly when predictive performance and interpretability are considered jo...
Zakaria Alj, A. Bouayad· Frontiers in Artificial Inte...· 0 citations
The early identification of students who may be at risk requires predictive models that can integrate behavioral and institutional data while maintaining reliable performance across academic terms. This study examines the relative predictive information carried by xAPI behavioral traces and Student Information System (...
Emrah Emirtekin, Urfat Nuriyev, T. Kışla· IEEE Access· 0 citations
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