Advancing Education: Hybrid Recommendation Systems for Best-Fit Student Domain Matching

Sarra Aouadi; Toufik Marir; Mohammed Lamine Kherfi

doi:10.2991/978-94-6463-496-9_6

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Advancing Education: Hybrid Recommendation Systems for Best-Fit Student Domain Matching

Authors

Sarra Aouadi¹^{, *}, Toufik Marir¹, Mohammed Lamine Kherfi²^{, 3}

¹ReLa(CS)2 Laboratory, University of Oum El Bouaghi, Oum El Bouaghi, Algeria

²LAMIA Laboratory, University of Quebec in Trois-Rivières, Trois-Rivières, Canada

³Department of Computer Science, University of Kasdi Merbah, Ouargla, 30000, Algeria

^*Corresponding author. Email: aouadi.sara@univ-oeb.dz

Corresponding Author

Sarra Aouadi

Available Online 31 August 2024.

DOI: 10.2991/978-94-6463-496-9_6 How to use a DOI?
Keywords: Dropout; Academic domain; Machine learning model; Recommendation system
Abstract: Universities around the world are concerned with the student dropout phenomenon, which is particularly prevalent in the early years. Research indicates that the main reason for early dropout is the wrong choice of academic study domain. In this work, we have tried to provide decision-making support to the new students to help them choose the path that best suits their abilities and skills. From a conceptual perspective, we propose a hybrid recommendation system that integrates machine learning algorithms and collaborative filtering techniques to address real-world educational big data. From a practical standpoint, this system utilizes the machine learning model to identify the academic domain in which a student is most likely to succeed. Subsequently, collaborative filtering is applied to utilize the top 20% of similar students to estimate potential success rates within the predicted domain. Our approach introduces several significant innovations compared to existing methods, demonstrating improved prediction accuracy and offering the potential to positively impact academic success rates.
Copyright: © 2024 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 International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
Series: Advances in Intelligent Systems Research
Publication Date: 31 August 2024
ISBN: 978-94-6463-496-9
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-496-9_6 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - Sarra Aouadi
AU  - Toufik Marir
AU  - Mohammed Lamine Kherfi
PY  - 2024
DA  - 2024/08/31
TI  - Advancing Education: Hybrid Recommendation Systems for Best-Fit Student Domain Matching
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 63
EP  - 72
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_6
DO  - 10.2991/978-94-6463-496-9_6
ID  - Aouadi2024
ER  -

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