Recommendation System for Adapting Learning Objects: Using the K-means Clustering Algorithm

DOI

10.2991/978-2-38476-408-2_35How to use a DOI?

Keywords

Recommendation Systems; Adaptive Learning; K-means Clustering; learning object; learning Style

Abstract

This article examines the use of recommendation systems to personalize educational content according to learners’ preferences and learning styles. Utilizing K-means clustering algorithms, the study aims to improve the effectiveness and engagement of e-learning environments. By integrating learning style models and adaptive evaluation methods, it addresses the diversity of learners’ needs. The research highlights the benefits of personalized learning, enhanced engagement, and support for diverse learning styles through recommendation systems. It also discusses the application of K-means clustering for more accurate recommendations. Despite progress, significant gaps remain in initializing learning styles and developing adaptive techniques.

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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TY  - CONF
AU  - Chelliq Ikram
AU  - Anoir Lamya
AU  - Erradi Mohamed
AU  - Khaldi Mohamed
PY  - 2025
DA  - 2025/06/20
TI  - Recommendation System for Adapting Learning Objects: Using the K-means Clustering Algorithm
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 496
EP  - 502
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_35
DO  - 10.2991/978-2-38476-408-2_35
ID  - Ikram2025
ER  -