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