Enhancing Student Dropout Risk Prediction through Deep Learning Models: A Comprehensive Review

Student dropout is a critical challenge faced by higher education institutions worldwide, leading to academic, economic, and social consequences. The increasing availability of educational data through digital learning platforms has enabled the application of advanced analytics to proactively identify students at risk of dropping out. This review paper presents a comprehensive analysis of recent research on enhancing student dropout risk prediction using deep learning models. It examines the evolution from traditional statistical and machine learning approaches to advanced deep learning architectures capable of modeling complex, non-linear, and temporal learning behaviors. The study synthesizes findings from recent literature, highlighting the effectiveness of recurrent neural networks, convolutional neural networks, transformer-based models, and hybrid ensemble frameworks in improving predictive accuracy. Additionally, the paper discusses the role of multimodal data integration, including academic performance, behavioral logs, psychological indicators, and digital traces, in strengthening prediction reliability. Key research findings, persistent challenges such as data imbalance, interpretability, privacy concerns, and generalizability issues are critically analyzed. The review aims to provide researchers and practitioners with a structured understanding of current trends, limitations, and opportunities, thereby supporting the development of robust, ethical, and actionable student dropout prediction systems.