Improving E-Learning Platforms Multimedia Analysis And Personalization With CNN And RNN Neural Networks

DOI

10.2991/978-2-38476-408-2_46How to use a DOI?

Keywords

Adaptive Learning; Multimedia Analysis; Convolutional Neural Networks (CNN); Recurrent Neural Networks (RNN); Artificial Intelligence in Education

Abstract

The following article discusses the integration of neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), into e-learning platforms to enhance the analysis of multimedia resources and customize learning paths. CNNs are adept at extracting intricate visual features, making them well-suited for analyzing images and educational videos, allowing for content segmentation and classification based on pedagogical requirements. RNNs and their variations, such as LSTMs, model learners’ sequential and behavioral data, enabling the dynamic adjustment of teaching methods in real time.

This approach aims to customize learning paths, foresee learners’ challenges, adapt content based on their progress, and optimize academic outcomes. By tailoring the learning experience, neural networks enhance user engagement, foster improved knowledge retention, and decrease dropout rates.

In addition to outlining the advantages of these technologies, this abstract also delves into the associated implementation challenges, such as the necessity for substantial data, algorithm complexity, and the ethical implications linked to the growing reliance on automation in the educational sphere. Finally, it explores concrete examples of applications and the potential for these technologies to permanently revolutionize the e-learning landscape.

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  - Abdennour Omar
AU  - Kemouss Hassane
AU  - Khaldi Mohamed
PY  - 2025
DA  - 2025/06/20
TI  - Improving E-Learning Platforms Multimedia Analysis And Personalization With CNN And RNN Neural Networks
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 620
EP  - 629
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_46
DO  - 10.2991/978-2-38476-408-2_46
ID  - Omar2025
ER  -