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Towards intelligent E-learning systems
The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedago...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742041/ https://www.ncbi.nlm.nih.gov/pubmed/36532790 http://dx.doi.org/10.1007/s10639-022-11479-6 |
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author | Liu, Mengchi Yu, Dongmei |
author_facet | Liu, Mengchi Yu, Dongmei |
author_sort | Liu, Mengchi |
collection | PubMed |
description | The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning. |
format | Online Article Text |
id | pubmed-9742041 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-97420412022-12-12 Towards intelligent E-learning systems Liu, Mengchi Yu, Dongmei Educ Inf Technol (Dordr) Article The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning. Springer US 2022-12-12 /pmc/articles/PMC9742041/ /pubmed/36532790 http://dx.doi.org/10.1007/s10639-022-11479-6 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Liu, Mengchi Yu, Dongmei Towards intelligent E-learning systems |
title | Towards intelligent E-learning systems |
title_full | Towards intelligent E-learning systems |
title_fullStr | Towards intelligent E-learning systems |
title_full_unstemmed | Towards intelligent E-learning systems |
title_short | Towards intelligent E-learning systems |
title_sort | towards intelligent e-learning systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742041/ https://www.ncbi.nlm.nih.gov/pubmed/36532790 http://dx.doi.org/10.1007/s10639-022-11479-6 |
work_keys_str_mv | AT liumengchi towardsintelligentelearningsystems AT yudongmei towardsintelligentelearningsystems |