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Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era
In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by...
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/PMC8731199/ https://www.ncbi.nlm.nih.gov/pubmed/35013647 http://dx.doi.org/10.1007/s10489-021-02916-z |
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author | Yan, Fei Wu, Nan Iliyasu, Abdullah M. Kawamoto, Kazuhiko Hirota, Kaoru |
author_facet | Yan, Fei Wu, Nan Iliyasu, Abdullah M. Kawamoto, Kazuhiko Hirota, Kaoru |
author_sort | Yan, Fei |
collection | PubMed |
description | In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., face-to-face (F2F)) communication. Consequently, schools, colleges, and universities worldwide have been forced to transition to different forms of online and virtual learning. Unlike F2F classes where the instructors could monitor and adjust lessons and content in tandem with the learners’ perceived emotions and engagement, in online learning environments (OLE), such tasks are daunting to undertake. In our modest contribution to ameliorate disruptions to education caused by the pandemic, this study presents an intuitive model to monitor the concentration, understanding, and engagement expected of a productive classroom environment. The proposed apposite OLE (i.e., AOLE) provides an intelligent 3D visualisation of the classroom atmosphere (CA), which could assist instructors adjust and tailor both content and instruction for maximum delivery. Furthermore, individual learner status could be tracked via visualisation of his/her emotion curve at any stage of the lesson or learning cycle. Considering the enormous emotional and psychological toll caused by COVID and the attendant shift to OLE, the emotion curves could be progressively compared through the duration of the learning cycle and the semester to track learners’ performance through to the final examinations. In terms of learning within the CA, our proposed AOLE is assessed within a class of 15 students and three instructors. Correlation of the outcomes reported with those from administered questionnaires validate the potential of our proposed model as a support for learning and counselling during these unprecedentedtimes that we find ourselves. |
format | Online Article Text |
id | pubmed-8731199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87311992022-01-06 Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era Yan, Fei Wu, Nan Iliyasu, Abdullah M. Kawamoto, Kazuhiko Hirota, Kaoru Appl Intell (Dordr) Article In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., face-to-face (F2F)) communication. Consequently, schools, colleges, and universities worldwide have been forced to transition to different forms of online and virtual learning. Unlike F2F classes where the instructors could monitor and adjust lessons and content in tandem with the learners’ perceived emotions and engagement, in online learning environments (OLE), such tasks are daunting to undertake. In our modest contribution to ameliorate disruptions to education caused by the pandemic, this study presents an intuitive model to monitor the concentration, understanding, and engagement expected of a productive classroom environment. The proposed apposite OLE (i.e., AOLE) provides an intelligent 3D visualisation of the classroom atmosphere (CA), which could assist instructors adjust and tailor both content and instruction for maximum delivery. Furthermore, individual learner status could be tracked via visualisation of his/her emotion curve at any stage of the lesson or learning cycle. Considering the enormous emotional and psychological toll caused by COVID and the attendant shift to OLE, the emotion curves could be progressively compared through the duration of the learning cycle and the semester to track learners’ performance through to the final examinations. In terms of learning within the CA, our proposed AOLE is assessed within a class of 15 students and three instructors. Correlation of the outcomes reported with those from administered questionnaires validate the potential of our proposed model as a support for learning and counselling during these unprecedentedtimes that we find ourselves. Springer US 2022-01-06 2022 /pmc/articles/PMC8731199/ /pubmed/35013647 http://dx.doi.org/10.1007/s10489-021-02916-z Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 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 Yan, Fei Wu, Nan Iliyasu, Abdullah M. Kawamoto, Kazuhiko Hirota, Kaoru Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era |
title | Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era |
title_full | Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era |
title_fullStr | Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era |
title_full_unstemmed | Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era |
title_short | Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era |
title_sort | framework for identifying and visualising emotional atmosphere in online learning environments in the covid-19 era |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8731199/ https://www.ncbi.nlm.nih.gov/pubmed/35013647 http://dx.doi.org/10.1007/s10489-021-02916-z |
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