A machine learning-based approach for the early prediction of dropout risk, based on academic, socioeconomic, and demographic variables is proposed, which found the best-performing models were Random Forest and its Balanced Random Forest Classifier variant.
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
Student dropout remains a persistent challenge in higher education institutions, affecting academic continuity, institutional performance, and long-term socioeconomic outcomes. Early identification of at-risk students enables timely intervention and improved retention strategies. This study proposes an explainable mach...
Predicting how well a student will perform before, rather than after, a semester goes wrong is one of the more practical promises that machine learning has brought to higher education. This study reports a case study conducted at a public university, in which academic records, attendance logs, and a small set of socio-...
Habibrahman Habibi, Abdul Wajid Fazil, Kefayatullah Khairkhah· Frontiers in Sustainable Sci...· 0 citations
Student dropout rates in higher education have become a significant problem for universities worldwide, impacting both student academic achievement and institutional stability. To address this problem, this research proposes the Student Dropout Risk Prediction and Early Warning System (SDRP-EWS), a machine learning fra...
Kritanat Chungnoy, Tanatorn Tanantong, Naphat Jakkraphatcharakul et al.· Asian Health, Science and Te...· 0 citations
Student dropout in higher education is a persistent problem with real consequences for students, institutions, and the broader economy. This paper presents a machine learning pipeline for early dropout prediction built on the UCI Student Dropout and Academic Success dataset, which contains 4,424 student records across...
A. Febrian, Dwiki Yulian, Muhammad Arifin Ilham et al.· International Conferences on...· 0 citations
Student dropout requires accurate and explainable early detection so that model outputs can support academic decisions responsibly within an early warning system. This study compares logistic regression, Random Forest, and extreme gradient boosting for student dropout prediction and examines how explainable artificial...
Jepri Banjarnahor, Very Clevery· Jurnal Minfo Polgan· 0 citations
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