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

<Previous Article In Volume
Next Article In Volume>

Towards Personalized Education: Choosing Machine Learning Algorithms To Predict Learner Engagement And Performance

Authors
Admeur Smail1, *, Mohamed Ahmed Moqbel Saleh1, Haddani Outman1, Alaoui Souad2, Attariuas Hicham1
1Emerging Computer Technologies (ECT), University of Abdelmalek Essaadi, Faculty of Science, Tetouan, Morocco
2Sidi Mohamed Ben Abdellah, Fès, Morocco
*Corresponding author. Email: s.admeur@uae.ac.ma
Corresponding Author
Admeur Smail
Available Online 20 June 2025.
DOI
10.2991/978-2-38476-408-2_25How to use a DOI?
Keywords
Machine learning; Behavior prediction; Algorithms; Predictive models; Artificial intelligence
Abstract

The article “Towards Personalized Education: Choosing Machine Learning Algorithms for Predicting Learning Activities” explores how machine learning techniques can transform education by providing tailored learning experiences, preferences, learning style of each learner. The authors analyze data from MOODLE learning platforms, highlighting the importance of collecting and processing data on learner engagement and performance. They examine various algorithms, such as neural networks, decision trees, random forests, in order to predict learning behaviors. The results of the study reveal that these models can not only identify at-risk and struggling students, but also personalize learning pathways based on the individual needs of learners. The article also addresses challenges faced, such as data quality and interpretability of results, while paving the way for future research on the integration of these tools into education systems. In short, this article offers an encouraging vision of more adapted and effective education thanks to artificial intelligence.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
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_25How 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  - Admeur Smail
AU  - Mohamed Ahmed Moqbel Saleh
AU  - Haddani Outman
AU  - Alaoui Souad
AU  - Attariuas Hicham
PY  - 2025
DA  - 2025/06/20
TI  - Towards Personalized Education: Choosing Machine Learning Algorithms To Predict Learner Engagement And Performance
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 339
EP  - 355
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_25
DO  - 10.2991/978-2-38476-408-2_25
ID  - Smail2025
ER  -