Student Dropout Forecasting with Machine Learning: A Review

The rapid evolution of machine learning (ML) technologies has significantly impacted various sectors, including education. This analysis reviews the advancements in machine learning-driven models within the educational system, highlighting their roles in enhancing teaching methods, supporting personalized learning, and predicting student performance. By employing a range of ML techniques from traditional algorithms to hybrid and deep learning approaches educators can better assess student engagement, identify at-risk learners, and tailor interventions to improve academic outcomes. The review also explores key applications such as early academic performance prediction, intelligent tutoring systems, and adaptive learning environments that respond dynamically to individual student needs. Despite the promising results, challenges such as data privacy concerns, ethical considerations, and the need for comprehensive, unbiased datasets persist. This review aims to provide a holistic view of how machine learning is reshaping the educational landscape, while discussing existing limitations and suggesting future directions to maximize the benefits of ML in education.