Abstract:

Student dropout is a critical issue with various consequences for student success, universities, and society at large. Understanding the factors that contribute to student dropout can help universities and policymakers to develop effective interventions based on scientific findings in order to reduce dropout rates. Therefore, this paper thoroughly explores the various factors influencing student dropout, the application of machine learning and deep learning in predicting dropout, and the emerging field of eXplainable Artificial Intelligence (XAI) in the context of student dropout prediction. Additionally, it identifies the limitations of existing studies and offers suggestions for future research directions.