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Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence
This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous lea...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970089/ https://www.ncbi.nlm.nih.gov/pubmed/36848374 http://dx.doi.org/10.1371/journal.pone.0282306 |
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author | Subirats, Laia Palacios Corral, Aina Pérez-Ruiz, Sof´ıa Fort, Santi Sacha, Go´mez-Mon˜ivas |
author_facet | Subirats, Laia Palacios Corral, Aina Pérez-Ruiz, Sof´ıa Fort, Santi Sacha, Go´mez-Mon˜ivas |
author_sort | Subirats, Laia |
collection | PubMed |
description | This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students’ marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students’ wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement. |
format | Online Article Text |
id | pubmed-9970089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99700892023-02-28 Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence Subirats, Laia Palacios Corral, Aina Pérez-Ruiz, Sof´ıa Fort, Santi Sacha, Go´mez-Mon˜ivas PLoS One Research Article This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students’ marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students’ wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement. Public Library of Science 2023-02-27 /pmc/articles/PMC9970089/ /pubmed/36848374 http://dx.doi.org/10.1371/journal.pone.0282306 Text en © 2023 Subirats et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Subirats, Laia Palacios Corral, Aina Pérez-Ruiz, Sof´ıa Fort, Santi Sacha, Go´mez-Mon˜ivas Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence |
title | Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence |
title_full | Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence |
title_fullStr | Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence |
title_full_unstemmed | Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence |
title_short | Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence |
title_sort | temporal analysis of academic performance in higher education before, during and after covid-19 confinement using artificial intelligence |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9970089/ https://www.ncbi.nlm.nih.gov/pubmed/36848374 http://dx.doi.org/10.1371/journal.pone.0282306 |
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