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.

Experimental evaluation on 1,200 student records demonstrated strong predictive performance, achieving a testing R² value of 0.88 with low prediction errors (MAE = 3.78; RMSE = 4.89). Comparative analysis confirmed that the proposed ANN model outperformed traditional machine learning algorithms, including Decision Tree, K-Nearest Neighbors, and Support Vector Machine. Statistical validation further indicated that there was no significant difference between predicted and actual performance, confirming the reliability of the model.

The proposed framework provides a practical early warning system for identifying academically at-risk students and supports data-driven decision-making in higher education. The findings contribute to the development of intelligent academic monitoring systems that integrate predictive analytics into modern educational environments.

Artificial Neural Networks, Educational Data Mining, Student Performance Prediction, Deep Learning, Early Warning Systems

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