Machine Learning Techniques for Understanding and Enhancing Student Adaptation in the Post-Pandemic Online Learning Environment

DOI

10.2991/978-94-6463-700-7_39How to use a DOI?

Keywords

Student Adaptability; Online Education; Machine Learning; Personalized Learning; Digital Divide; Educational Technology

Abstract

This research paper presents a complete assessment of current improvements in using machine learning techniques to assess and improve scholar revision in post-pandemic online learning environments. Nowadays, digital education continues to develop, and educators and representatives must escalate how scholars relate, get involved, and adjust to online education. Machine learning provides vigorous tools for analyzing various data sources, including scholars’ interaction records, presentation metrics, and commitment designs, providing valued visions into discrete and group learning methods. The analysis examines machine learning platforms such as cataloging, clustering, and analytical demonstration to monitor and calculate student alteration. To expand educational results and student assignation, these methods cover the way for building modified learning pathways, real-time monitoring systems, and modified intrusions. This paper also discusses the task of realizing these replicas, such as scalability, data privacy, and certifying model correctness across unlike enlightening backgrounds. Generally, the result emphasizes the transformative approach of machine learning in building more adaptive, responsive, and operative online learning surroundings.

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  - Paras Parashar
AU  - Randeep Singh
AU  - Deepak Chandra Uprety
PY  - 2025
DA  - 2025/04/19
TI  - Machine Learning Techniques for Understanding and Enhancing Student Adaptation in the Post-Pandemic Online Learning Environment
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 499
EP  - 506
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_39
DO  - 10.2991/978-94-6463-700-7_39
ID  - Parashar2025
ER  -