A Platform for Early Class Dropout Prediction of University Students

• Published in: IEEE access Vol. 13; pp. 109116 - 109133
• Main Authors: Borges, Guilherme Antonio, Pedro, Carolina Filipa Dos Santos, Anjos, Julio Cesar Santos Dos, Rodrigues, Andre, Boavida, Fernando, Sa Silva, Jorge
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Colleges & universities; Computer aided instruction; Data models; early intervention; Education; educational data mining; Electronic learning; higher education; Machine learning; Measurement; Prediction algorithms; Prediction models; Predictive models; Radio frequency; real world-data; State-of-the-art reviews; Student dropout; Student retention; Students; support platform; Support systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3581751

Higher education faces significant challenges, with student dropout rates remaining a critical issue. While much research has focused on course-level dropout, less attention has been given to the early identification of at-risk students within individual courses. In this context, this study reviews the state-of-the-art and applies Machine Learning (ML) techniques to predict early dropout of students from real-world data. Key demographic, academic, and behavioral factors were analyzed to identify students at risk of dropping subjects, enabling timely intervention and the implementation of preventive strategies. The analysis revealed that student performance, re-enrollment behavior, and attendance patterns are strongly correlated with the risk of early dropout. Models trained on mid-term and final attendance data before final exams showed the best metrics, highlighting the importance of tracking student engagement throughout the semester. Based on these findings, we developed and implemented the Best Assisting Tutor and INteractive Advisor (BATINA) OnBoard Platform, an advanced platform that integrates predictive models in a practical and user-friendly way. This platform provides comprehensive support to students and faculty, enhancing academic performance and enabling early intervention to improve student retention. This study demonstrates how data-driven solutions can enhance educational support systems and help mitigate student dropout.