Vol.14, No.4, November 2025.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Development of a Student Performance Prediction System with Deep Learning

Alexander EJ Villegas-Espinoza, Jorge Isaac Necochea-Chamorro

© 2025 Jorge Isaac Necochea Chamorro, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 14, Issue 4, Pages 3591-3598, ISSN 2217-8309, DOI: 10.18421/TEM144-64, November 2025.

Received: 04 December 2024.
Revised: 08 June 2025.
Accepted: 02 September 2025.
Published: 27 November 2025.

Abstract:

The present study focuses on the development of deep learning (DL) model to predict student performance and address the issue of low academic achievement among secondary school students. Utilizing a quasi-experimental design, the research process involved the enhancement of data with generative adversarial networks (GAN) and synthetic minority over-sampling techniques (SMOTE), followed by the training of a deep neural network (DNN). The model demonstrates high accuracy, achieving 96.81% and a Cohen's Kappa index of 0.866, indicating strong reliability. A comprehensive investigation is conducted to identify the key variables influencing student performance. These variables include self-concept, attitude towards subjects, parental satisfaction, and the use of additional learning resources. These factors are critical in building a robust predictive model capable of detecting students at risk of poor academic outcomes. The findings underscore the efficacy of the model in not only identifying at-risk students but also in facilitating the implementation of personalized intervention strategies. The objective of these strategies is twofold: to reduce the rate of school failure and to enhance the overall quality of education.

Keywords – Deep learning, student performance, prediction, deep neural networks, education.

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