Predicting Student Academic Performance Using Deep Learning: A PyTorch-Based Approach

Predicting student academic performance is essential for early intervention and improved educational outcomes. This study presents a deep learning approach utilizing a feedforward neural network built with PyTorch to predict students' final grades based on comprehensive demographic, behavioral, and academic features. Using the UCI Student Performance dataset, the data underwent categorical encoding and feature scaling for optimal model performance. Training the model over 50 epochs with Mean Squared Error loss and the Adam optimizer achieved a Root Mean Squared Error (RMSE) of approximately 2.5 on a 0–20 grade scale. Visualization of predicted versus actual grades demonstrated the model’s capability to accurately reflect underlying performance patterns. Security enhancements were integrated into the prediction process, including secure data handling, encryption of sensitive student data, and secure deployment practices. Potential model improvements, including enhanced feature engineering, architectural tuning, security measures, and alternative machine learning methods, are discussed. The findings underscore the potential of deep learning techniques in educational analytics to support proactive student success strategies.