Optimizing Educational Content With Q-Learning: A Reinforcement Learning Approach To Enhance Student Engagement And Outcomes

DOI

10.2991/978-2-38476-408-2_15How to use a DOI?

Keywords

Q-Learning; Reinforcement learning; Adaptive learning

Abstract

In the dynamic realm of education, adapting and tailoring educational materials is becoming increasingly crucial to cater to the diverse requirements of learners. This presentation delves into the innovative application of Q-Learning, a Reinforcement Learning technique, to optimize educational content. We introduce an approach in which a machine learning agent models and customizes educational content based on learner interactions and performance.

Our approach revolves around establishing a learning environment where the agent’s choices are represented by various types of educational content, and the rewards are determined by the success metrics of the learners. Through Q-Learning, the agent progressively acquires the optimal strategy for delivering content that maximizes learner engagement and comprehension.

This research explores the integration of Q-Learning, a Reinforcement Learning method, into educational systems such as Moodle to cater to the individual needs of students. The methodology employs student interactions and academic performance data to construct a Q-Learning model. This model evaluates the effectiveness of pedagogical actions across different educational contexts with the ultimate goal of enhancing student engagement and improving learning outcomes.

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  - Amal Douara
AU  - Adil Enaanai
AU  - Youssef Zaz
PY  - 2025
DA  - 2025/06/20
TI  - Optimizing Educational Content With Q-Learning: A Reinforcement Learning Approach To Enhance Student Engagement And Outcomes
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 193
EP  - 206
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_15
DO  - 10.2991/978-2-38476-408-2_15
ID  - Douara2025
ER  -