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Improving effectiveness of online learning for higher education students during the COVID-19 pandemic
During the COVID-19 pandemic, online learning has become one of the important ways of higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has become the focus of education research, and the role of the “online learning community” cannot b...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884959/ https://www.ncbi.nlm.nih.gov/pubmed/36726501 http://dx.doi.org/10.3389/fpsyg.2022.1111028 |
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author | Li, Xuelan Pei, Zhiqiang |
author_facet | Li, Xuelan Pei, Zhiqiang |
author_sort | Li, Xuelan |
collection | PubMed |
description | During the COVID-19 pandemic, online learning has become one of the important ways of higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has become the focus of education research, and the role of the “online learning community” cannot be ignored. In the context of the Internet of Things (IoT), we try to build up a new online learning community model: (1) First, we introduce the Kolb learning style theory to identify different online learning styles; (2) Second, we use a clustering algorithm to identify the nature of different learning style groups; and (3) Third, we introduce the group dynamics theory to design the dimensions of the questionnaire and combine the Analytic Hierarchy Process (AHP) method to identify the key influencing factors of the online learning community. We take business administration majors and students in universities as an example. The results show that (1) as a machine learning method, the clustering algorithm method is superior to the random construction method in identifying different learning style groups, and (2) our method can well judge the importance of each factor based on hierarchical analysis and clarify the different roles of factors in the process of knowledge transfer. This study can provide a useful reference for the sustainable development of online learning in higher education. |
format | Online Article Text |
id | pubmed-9884959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98849592023-01-31 Improving effectiveness of online learning for higher education students during the COVID-19 pandemic Li, Xuelan Pei, Zhiqiang Front Psychol Psychology During the COVID-19 pandemic, online learning has become one of the important ways of higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has become the focus of education research, and the role of the “online learning community” cannot be ignored. In the context of the Internet of Things (IoT), we try to build up a new online learning community model: (1) First, we introduce the Kolb learning style theory to identify different online learning styles; (2) Second, we use a clustering algorithm to identify the nature of different learning style groups; and (3) Third, we introduce the group dynamics theory to design the dimensions of the questionnaire and combine the Analytic Hierarchy Process (AHP) method to identify the key influencing factors of the online learning community. We take business administration majors and students in universities as an example. The results show that (1) as a machine learning method, the clustering algorithm method is superior to the random construction method in identifying different learning style groups, and (2) our method can well judge the importance of each factor based on hierarchical analysis and clarify the different roles of factors in the process of knowledge transfer. This study can provide a useful reference for the sustainable development of online learning in higher education. Frontiers Media S.A. 2023-01-16 /pmc/articles/PMC9884959/ /pubmed/36726501 http://dx.doi.org/10.3389/fpsyg.2022.1111028 Text en Copyright © 2023 Li and Pei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Li, Xuelan Pei, Zhiqiang Improving effectiveness of online learning for higher education students during the COVID-19 pandemic |
title | Improving effectiveness of online learning for higher education students during the COVID-19 pandemic |
title_full | Improving effectiveness of online learning for higher education students during the COVID-19 pandemic |
title_fullStr | Improving effectiveness of online learning for higher education students during the COVID-19 pandemic |
title_full_unstemmed | Improving effectiveness of online learning for higher education students during the COVID-19 pandemic |
title_short | Improving effectiveness of online learning for higher education students during the COVID-19 pandemic |
title_sort | improving effectiveness of online learning for higher education students during the covid-19 pandemic |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884959/ https://www.ncbi.nlm.nih.gov/pubmed/36726501 http://dx.doi.org/10.3389/fpsyg.2022.1111028 |
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