Student Dropout Risk Detection: A Data-Driven Approach with Deep Neural Networks

Abstract

High psychological stress and ineffective study habits induce academic pressure and low learning efficiency, respectively. When these psycho-behavioral dimensions compound poor performance, the student profile exhibits high vulnerability to dropout. In conventional frameworks, data limitations and unobserved behavioral patterns severely degrade detection accuracy. Therefore, scientific interest has migrated toward data-driven learning analytics frameworks oriented to automated risk-profile generation using multi-source educational indicators. In this paper, an interpretable-by-design automated dropout risk classification framework based on explicitly engineered academic and behavioral variables is presented. By using a Principal Component Analysis (PCA)-driven automatic labeling strategy, direct conversion of continuous indicators into discrete risk profiles is achieved. This methodology is implemented through a Deep Neural Network (DNN) that transforms the student dropout prediction task into a psycho-academic risk classification task. The proposed approach avoids information leakage and reliance on complex institutional records, exploiting latent nonlinear relationships in a manner fully compatible with standard administrative data. Compared to advanced ensemble-based architectures, this framework offers high robustness against overfitting and a compact design, achieving an overall accuracy of 99% in student risk classification.