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A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed?
Automatic analysis of the myriad discussion messages in large online courses can support effective educator-learner interaction at scale. Robust classifiers are an essential foundation for the use of automatic analysis of cognitive presence in practice. This study reports on the application of a rev...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467662/ https://www.ncbi.nlm.nih.gov/pubmed/36118283 http://dx.doi.org/10.1186/s41239-022-00353-7 |
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author | Hu, Yuanyuan Donald, Claire Giacaman, Nasser |
author_facet | Hu, Yuanyuan Donald, Claire Giacaman, Nasser |
author_sort | Hu, Yuanyuan |
collection | PubMed |
description | Automatic analysis of the myriad discussion messages in large online courses can support effective educator-learner interaction at scale. Robust classifiers are an essential foundation for the use of automatic analysis of cognitive presence in practice. This study reports on the application of a revised machine learning approach, which was originally developed from traditional, small-scale, for-credit, online courses, to automatically identify the phases of cognitive presence in the discussions from a Philosophy Massive Open Online Course (MOOC). The classifier performed slightly better on the MOOC discussions than similar previous studies have found. A new set of indicators to identify cognitive presence was revealed in the MOOC discussions, unlike those in the traditional courses. This study also cross-validated the classifier using MOOC discussion data from three other disciplines: Medicine, Education, and Humanities. Our results suggest that the cognitive classifier trained using MOOC data in only one discipline cannot yet be applied to other disciplines with sufficient accuracy. |
format | Online Article Text |
id | pubmed-9467662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-94676622022-09-13 A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? Hu, Yuanyuan Donald, Claire Giacaman, Nasser Int J Educ Technol High Educ Research Article Automatic analysis of the myriad discussion messages in large online courses can support effective educator-learner interaction at scale. Robust classifiers are an essential foundation for the use of automatic analysis of cognitive presence in practice. This study reports on the application of a revised machine learning approach, which was originally developed from traditional, small-scale, for-credit, online courses, to automatically identify the phases of cognitive presence in the discussions from a Philosophy Massive Open Online Course (MOOC). The classifier performed slightly better on the MOOC discussions than similar previous studies have found. A new set of indicators to identify cognitive presence was revealed in the MOOC discussions, unlike those in the traditional courses. This study also cross-validated the classifier using MOOC discussion data from three other disciplines: Medicine, Education, and Humanities. Our results suggest that the cognitive classifier trained using MOOC data in only one discipline cannot yet be applied to other disciplines with sufficient accuracy. Springer International Publishing 2022-09-13 2022 /pmc/articles/PMC9467662/ /pubmed/36118283 http://dx.doi.org/10.1186/s41239-022-00353-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Hu, Yuanyuan Donald, Claire Giacaman, Nasser A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? |
title | A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? |
title_full | A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? |
title_fullStr | A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? |
title_full_unstemmed | A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? |
title_short | A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed? |
title_sort | revised application of cognitive presence automatic classifiers for moocs: a new set of indicators revealed? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467662/ https://www.ncbi.nlm.nih.gov/pubmed/36118283 http://dx.doi.org/10.1186/s41239-022-00353-7 |
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