Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)

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Enhancing Student Classification Using Transformer Neural Networks

Authors
Abderrafik Laakel Hemdanou1, *, Youssef Achtoun1, Sara Mouali1, Mohammed Lamarti Sefian1
1AMCS Team, Normal Higher School, Abdelmalek Essaadi University, Tétouan, Morocco
*Corresponding author. Email: abderrafik.laakelhemdanou@etu.uae.ac.ma
Corresponding Author
Abderrafik Laakel Hemdanou
Available Online 20 June 2025.
DOI
10.2991/978-2-38476-408-2_47How to use a DOI?
Keywords
Transformer models; Student classification; Selection features
Abstract

Classification represents a challenge for researchers and a very important task for educational systems in general to help make good decisions to improve student engagement in intelligent tutoring systems. In this study, we propose an approach to classifying students on the basis of various characteristics using the latest Transformer model, which has shown its effectiveness in handling the nature of student data. Our methodology concerns the feature of the most important factors influencing academic performance to facilitate student classification, followed by the encoding of various student information, such as academic results, socio-economic background and extra-curricular activities, into a complete input sequence. Then the KNN, SVC, DNN and Transformer (Encoder-Decoder) models are used to classify students on the full data and on sub-data from LightGBM, Elastic Net and PCA. To assess the effectiveness of the proposed method, multiple metrics were used to perform a comprehensive evaluation. The results indicate that the Transformer model exceeds the performance benchmarks set by other models, demonstrating its effectiveness.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
20 June 2025
ISBN
978-2-38476-408-2
ISSN
2667-128X
DOI
10.2991/978-2-38476-408-2_47How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Abderrafik Laakel Hemdanou
AU  - Youssef Achtoun
AU  - Sara Mouali
AU  - Mohammed Lamarti Sefian
PY  - 2025
DA  - 2025/06/20
TI  - Enhancing Student Classification Using Transformer Neural Networks
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 630
EP  - 645
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_47
DO  - 10.2991/978-2-38476-408-2_47
ID  - Hemdanou2025
ER  -