A Deep Learning Approach for Predicting Student Academic Performance Using Artificial Neural Networks and Educational Data Mining

Early prediction of student academic performance is essential for improving learning outcomes and enabling timely educational intervention. Traditional statistical methods often fail to capture the complex and non-linear relationships among academic, behavioral, and engagement-related factors that influence student success. This study proposes a deep learning–based predictive framework using an Artificial Neural Network (ANN) integrated with educational data mining techniques to forecast student academic performance before final examinations. The model incorporates multidimensional input features, including continuous assessment scores, attendance percentage, assignment performance, midterm marks, and Learning Management System (LMS) engagement indicators. Data preprocessing techniques such as cleaning, normalization, and feature encoding were applied to ensure data quality and model stability. A multilayer feedforward neural network was trained using supervised learning with adaptive optimization to learn hidden relationships within the dataset.