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The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach
Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students’ marks from e...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Milan
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887420/ https://www.ncbi.nlm.nih.gov/pubmed/33612860 http://dx.doi.org/10.1007/s40300-021-00200-1 |
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author | Tomal, Jabed Rahmati, Saeed Boroushaki, Shirin Jin, Lingling Ahmed, Ehsan |
author_facet | Tomal, Jabed Rahmati, Saeed Boroushaki, Shirin Jin, Lingling Ahmed, Ehsan |
author_sort | Tomal, Jabed |
collection | PubMed |
description | Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed effects model fitted to longitudinal data. The Bayesian linear mixed effects model is designed for this application which allows student-specific error variances to vary. The novel Bayesian missing value imputation method is flexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom’s Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support. |
format | Online Article Text |
id | pubmed-7887420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Milan |
record_format | MEDLINE/PubMed |
spelling | pubmed-78874202021-02-17 The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach Tomal, Jabed Rahmati, Saeed Boroushaki, Shirin Jin, Lingling Ahmed, Ehsan Metron Article Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed effects model fitted to longitudinal data. The Bayesian linear mixed effects model is designed for this application which allows student-specific error variances to vary. The novel Bayesian missing value imputation method is flexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom’s Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support. Springer Milan 2021-02-17 2021 /pmc/articles/PMC7887420/ /pubmed/33612860 http://dx.doi.org/10.1007/s40300-021-00200-1 Text en © Sapienza Università di Roma 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 Tomal, Jabed Rahmati, Saeed Boroushaki, Shirin Jin, Lingling Ahmed, Ehsan The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach |
title | The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach |
title_full | The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach |
title_fullStr | The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach |
title_full_unstemmed | The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach |
title_short | The Impact of COVID-19 on Students’ Marks: A Bayesian Hierarchical Modeling Approach |
title_sort | impact of covid-19 on students’ marks: a bayesian hierarchical modeling approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887420/ https://www.ncbi.nlm.nih.gov/pubmed/33612860 http://dx.doi.org/10.1007/s40300-021-00200-1 |
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