A Network Security Course Teaching Resource Sharing Method based on Reinforcement Learning

DOI

10.2991/978-2-38476-346-7_8How to use a DOI?

Keywords

reinforcement learning; cybersecurity education; resource sharing; teaching quality improvement

Abstract

This study aims to optimize the sharing and allocation of teaching resources for cybersecurity courses by applying reinforcement learning technology, thereby improving resource utilization efficiency and teaching quality. The deep Q-network (DQN) algorithm was used to design and implement an intelligent resource evaluation model, which dynamically adjusts the allocation of teaching resources by analyzing user interaction data. The results show that under the guidance of reinforcement learning strategies, the average number of visits to video lectures, experimental guides, and interactive learning modules increased by at least 30%, and user satisfaction increased from 70% to 85%. The conclusion shows that the teaching resource sharing method based on reinforcement learning can significantly improve the utilization efficiency and learning quality of educational resources, bringing innovation and value to the field of educational technology.

Copyright

© 2024 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  - Qi He
PY  - 2024
DA  - 2024/12/27
TI  - A Network Security Course Teaching Resource Sharing Method based on Reinforcement Learning
BT  - Proceeding of the 2024 International Conference on Diversified Education and Social Development (DESD 2024)
PB  - Atlantis Press
SP  - 55
EP  - 62
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-346-7_8
DO  - 10.2991/978-2-38476-346-7_8
ID  - He2024
ER  -