Improving Personalization in Education: An Approach to Automatically Tagging Resources with Tag Recommender Systems

DOI

10.2991/978-94-6463-360-3_23How to use a DOI?

Keywords

Intelligent Educational System (IES); Personalized learning; Tag Recommender Systems (TRS); Content-based filtering; Naive Bayes classifier

Abstract

This article discusses the use of Tag Recommender Systems (TRS) in Intelligent Educational Systems (IES) for providing personalized learning experiences to learners. The article explains how TRS works by analyzing tags associated with resources and suggesting similar content to users based on their tag preferences. The article also explains the Naive Bayes algorithm, which is used in building tag recommendation systems, and its application in LMSs. The goal is to recommend courses to learners on the LMS platform that align with their interests by recommending courses with tags that closely match their preferences.

Copyright

© 2023 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  - Zakaria Tagdimi
AU  - Souhaib Aammou
AU  - Ikram Amzil
AU  - Hicham Erradi
PY  - 2024
DA  - 2024/02/05
TI  - Improving Personalization in Education: An Approach to Automatically Tagging Resources with Tag Recommender Systems
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023)
PB  - Atlantis Press
SP  - 217
EP  - 223
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-360-3_23
DO  - 10.2991/978-94-6463-360-3_23
ID  - Tagdimi2024
ER  -