Predicting Student’s Academic Performance Using Deep Learning

Ms.Gazala Begum, Ms.Bhavana, Dr.Jabeen Sultana, Mohammed Ehtesham Ul Baqui and Nouman Ajmal Khan
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Ms.Gazala Begum: Department of Computer Science Lords Institute of Engineering & Technology Hyderabad,
Ms.Bhavana: Department of Computer Science Lords Institute of Engineering & Technology Hyderabad
Dr.Jabeen Sultana: Department of Computer Science Imam Muhammad Ibn Saud Islamic University (IMSIU) Kingdom of Saudi Arabia
Mohammed Ehtesham Ul Baqui: Student, Department of Computer Science Lords Institute of Engineering & Technology Hyderabad
Nouman Ajmal Khan: Student, Department of Computer Science Lords Institute of Engineering & Technology Hyderabad

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 5, 62-67

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.

Date: 2025
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