Personalizing Learning Experiences with Q-Learning in Adaptive Educational Systems

DOI

10.2991/978-94-6463-360-3_9How to use a DOI?

Keywords

Adaptive educational systems (AES); Reinforcement learning (RL); Q-learning

Abstract

Adaptive Educational Systems (AES) are computer-based systems that personalize the content and teaching methods based on individual students’ learning progress. AES aim to provide tailored learning experiences and improve learning outcomes. This research paper explores how Q-learning, a type of Reinforcement Learning (RL) algorithm, can be used to model the interactions between students and AES. The paper discusses how Q-learning works, how it can be applied to AES, and how it can improve the personalization of learning experiences for students. The benefits and potential drawbacks of using Q-learning in AES are highlighted, and future research directions are discussed. The theoretical framework includes an overview of AES and Reinforcement Learning, with a focus on Q-learning as an algorithm for optimizing decision-making in complex environments. The paper emphasizes the importance of tracking and measuring learning progress in AES and how Q-learning can be used to create personalized recommendations for students based on their learning progress. This research paper provides insights into the potential of Q-learning as a tool for enhancing the personalization of learning experiences in AES, and identifies areas for further research in this field.

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  - Ikram Amzil
AU  - Souhaib Aammou
AU  - Zakaria Tagdimi
AU  - Hicham Erradi
PY  - 2024
DA  - 2024/02/05
TI  - Personalizing Learning Experiences with Q-Learning in Adaptive Educational Systems
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023)
PB  - Atlantis Press
SP  - 70
EP  - 77
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-360-3_9
DO  - 10.2991/978-94-6463-360-3_9
ID  - Amzil2024
ER  -