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Statistical methods to estimate the impact of remote teaching on university students’ performance
The COVID-19 pandemic manifested around the World since February 2020, leading to disruptive effects on many aspects of people social life. The suspension of face-to-face teaching activities in schools and universities was the first containment measure adopted by the Governments to deal with the spr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885921/ https://www.ncbi.nlm.nih.gov/pubmed/36743855 http://dx.doi.org/10.1007/s11135-023-01612-z |
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author | Bacci, Silvia Bertaccini, Bruno Del Sarto, Simone Grilli, Leonardo Rampichini, Carla |
author_facet | Bacci, Silvia Bertaccini, Bruno Del Sarto, Simone Grilli, Leonardo Rampichini, Carla |
author_sort | Bacci, Silvia |
collection | PubMed |
description | The COVID-19 pandemic manifested around the World since February 2020, leading to disruptive effects on many aspects of people social life. The suspension of face-to-face teaching activities in schools and universities was the first containment measure adopted by the Governments to deal with the spread of the virus. Remote teaching has been the emergency solution implemented by schools and universities to limit the damages of schools and universities closure to students’ learning. In this contribution we intend to suggest to policy makers and researchers how to assess the impact of emergency policies on remote learning in academia by analysing students’ careers. In particular, we exploit the quasi-experimental setting arising from the sudden implementation of remote teaching in the second semester of academic year 2019/2020: we compare the performance of the cohort 2019/2020, which represents the treatment group, with the performance of the cohort 2018/2019, which represents the control group. We distinguish the impact of remote teaching at two levels: degree program and single courses within a degree program. We suggest to use Difference-In-Differences approach in the former case and multilevel modeling in the latter one. The proposal is illustrated analysing administrative data referred to freshmen of cohorts 2018/2019 and 2019/2020 for a sample of degree programs of the University of Florence (Italy). |
format | Online Article Text |
id | pubmed-9885921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-98859212023-01-30 Statistical methods to estimate the impact of remote teaching on university students’ performance Bacci, Silvia Bertaccini, Bruno Del Sarto, Simone Grilli, Leonardo Rampichini, Carla Qual Quant Article The COVID-19 pandemic manifested around the World since February 2020, leading to disruptive effects on many aspects of people social life. The suspension of face-to-face teaching activities in schools and universities was the first containment measure adopted by the Governments to deal with the spread of the virus. Remote teaching has been the emergency solution implemented by schools and universities to limit the damages of schools and universities closure to students’ learning. In this contribution we intend to suggest to policy makers and researchers how to assess the impact of emergency policies on remote learning in academia by analysing students’ careers. In particular, we exploit the quasi-experimental setting arising from the sudden implementation of remote teaching in the second semester of academic year 2019/2020: we compare the performance of the cohort 2019/2020, which represents the treatment group, with the performance of the cohort 2018/2019, which represents the control group. We distinguish the impact of remote teaching at two levels: degree program and single courses within a degree program. We suggest to use Difference-In-Differences approach in the former case and multilevel modeling in the latter one. The proposal is illustrated analysing administrative data referred to freshmen of cohorts 2018/2019 and 2019/2020 for a sample of degree programs of the University of Florence (Italy). Springer Netherlands 2023-01-30 /pmc/articles/PMC9885921/ /pubmed/36743855 http://dx.doi.org/10.1007/s11135-023-01612-z Text en © The Author(s) 2023 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 | Article Bacci, Silvia Bertaccini, Bruno Del Sarto, Simone Grilli, Leonardo Rampichini, Carla Statistical methods to estimate the impact of remote teaching on university students’ performance |
title | Statistical methods to estimate the impact of remote teaching on university students’ performance |
title_full | Statistical methods to estimate the impact of remote teaching on university students’ performance |
title_fullStr | Statistical methods to estimate the impact of remote teaching on university students’ performance |
title_full_unstemmed | Statistical methods to estimate the impact of remote teaching on university students’ performance |
title_short | Statistical methods to estimate the impact of remote teaching on university students’ performance |
title_sort | statistical methods to estimate the impact of remote teaching on university students’ performance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885921/ https://www.ncbi.nlm.nih.gov/pubmed/36743855 http://dx.doi.org/10.1007/s11135-023-01612-z |
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