Vol.14, No.3, August 2025.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Using Deep Learning in Student Performance Prediction: A Systematic Review

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 3, Pages 2472-2482, ISSN 2217-8309, DOI: 10.18421/TEM143-51, August 2025.

Received: 04 December 2024.

Revised: 06 June 2025.
Accepted: 08 July 2025.
Published: 27 August 2025.

Abstract:

This article examines how Deep Learning (DL) techniques can predict student achievement (SA), following the methodology of Kitchenham and Charters.
The study focuses on identifying the most widely used models, assessing their accuracy, and determining key variables for SA prediction. From 1,096 studies reviewed, 27 were selected that met the inclusion criteria, such as focusing specifically on DL and providing details about the models and variables used. The most common models were Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN), with an accuracy of over 90%. It is highlighted that, although these models are effective, they face challenges such as the small amount of data and representativeness, the analysis of "black box" models, and the generalization of results to diverse educational contexts. In conclusion, DL has great potential to improve SA prediction and the early identification of at-risk students but requires overcoming important challenges to maximize its impact on education.

Keywords – Deep learning, student achievement, prediction, educational models, neural networks, Kitchenham and Charters.

-----------------------------------------------------------------------------------------------------------

Full text PDF >  

-----------------------------------------------------------------------------------------------------------