Predicting Student’s Academic Performance Using Deep Learning

Abstract: Learning Platforms generate huge data and play a vital role in the field of education as a nation's future is dependent upon the progress of the students. These platforms generate lots of data and, offer valuable opportunities to predict and categorize student performance. Machine Learning (ML) has become a prominent method for analyzing this data, providing meaningful insights into academic outcomes. This research proposes a deep learning-based approach for processing and classifying student performance using ML algorithms. ML classifiers like Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), and Naïve Bayes are applied to preprocessed data. The models' effectiveness is measured using parameters like accuracy, precision, recall, F-score, and the time taken to train each model.

Educational Data, Hold-out Method, 10-Fold Cross Validation, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Naïve Bayes (NB)

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