Chapter A quantitative study to measure the family impact of e-learning

The Covid emergency has forced universities around the world to transfer teaching activities online. Even if online teaching has made it possible to carry out the planned teaching activities, it is necessary, in retrospect, to evaluate the impact that this teaching method has had on the different ty...

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Hlavní autoři: Davino, Cristina, GHERGHI, Marco, VISTOCCO, Domenico
Médium: Online
Jazyk:angličtina
Vydáno: Firenze University Press 2022
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On-line přístup:ONIX_20220601_9788855183048_507
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author Davino, Cristina
GHERGHI, Marco
VISTOCCO, Domenico
author_browse Davino, Cristina
GHERGHI, Marco
VISTOCCO, Domenico
author_facet Davino, Cristina
GHERGHI, Marco
VISTOCCO, Domenico
author_sort Davino, Cristina
collection Directory of Open Access Books
description The Covid emergency has forced universities around the world to transfer teaching activities online. Even if online teaching has made it possible to carry out the planned teaching activities, it is necessary, in retrospect, to evaluate the impact that this teaching method has had on the different types of students, in terms of preparation, characteristics and social background. In this framework, the presents paper aims to evaluate if distance learning can be considered socially less useful because it increases the divide between the advantaged and disadvantaged students. The study is based on the analysis of data collected at the University of Naples Federico II in June 2020. More than 19 thousand students took part in the survey, carried out to monitor distance learning activities. The aim of this work is to analyse whether and how much the distance learning activities has had an impact on the students' families both in terms of the organisation of the spaces and daily rhythms and from an economic point of view, having required additional expenses. This objective will be achieved through the use of a factorial method that will provide a composite indicator measuring the family impact of distance learning. We will then try to explain if the family impact takes different forms and intensity depending on the students' characteristics, the availability of computer equipment and the type of teaching used. Quantile regression will allow to differentiate the study of effects for different levels of family impact. Finally, it will also be evaluated whether the experience lived in terms of the family impact of the distance learning, conditions the judgement on the preferred teaching method for the future, totally online, oriented towards a complete return to face-to-face teaching or a mixed solution that takes advantage of the experience lived.
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spelling doab-20.500.12854ir-838002022-06-02T04:38:16Z Chapter A quantitative study to measure the family impact of e-learning Davino, Cristina GHERGHI, Marco VISTOCCO, Domenico Online learning Quantile regression Composite indicators The Covid emergency has forced universities around the world to transfer teaching activities online. Even if online teaching has made it possible to carry out the planned teaching activities, it is necessary, in retrospect, to evaluate the impact that this teaching method has had on the different types of students, in terms of preparation, characteristics and social background. In this framework, the presents paper aims to evaluate if distance learning can be considered socially less useful because it increases the divide between the advantaged and disadvantaged students. The study is based on the analysis of data collected at the University of Naples Federico II in June 2020. More than 19 thousand students took part in the survey, carried out to monitor distance learning activities. The aim of this work is to analyse whether and how much the distance learning activities has had an impact on the students' families both in terms of the organisation of the spaces and daily rhythms and from an economic point of view, having required additional expenses. This objective will be achieved through the use of a factorial method that will provide a composite indicator measuring the family impact of distance learning. We will then try to explain if the family impact takes different forms and intensity depending on the students' characteristics, the availability of computer equipment and the type of teaching used. Quantile regression will allow to differentiate the study of effects for different levels of family impact. Finally, it will also be evaluated whether the experience lived in terms of the family impact of the distance learning, conditions the judgement on the preferred teaching method for the future, totally online, oriented towards a complete return to face-to-face teaching or a mixed solution that takes advantage of the experience lived. 2022-06-02T04:38:15Z 2022-06-02T04:38:15Z 2022-06-01T12:19:09Z 2021 chapter ONIX_20220601_9788855183048_507 2704-5846 https://library.oapen.org/handle/20.500.12657/56322 9788855183048 https://directory.doabooks.org/handle/20.500.12854/83800 eng Proceedings e report open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/56322/1/16990.pdf Firenze University Press 10.36253/978-88-5518-304-8.21 10.36253/978-88-5518-304-8.21 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855183048 5 Florence open access
spellingShingle Online learning
Quantile regression
Composite indicators
Davino, Cristina
GHERGHI, Marco
VISTOCCO, Domenico
Chapter A quantitative study to measure the family impact of e-learning
title Chapter A quantitative study to measure the family impact of e-learning
title_full Chapter A quantitative study to measure the family impact of e-learning
title_fullStr Chapter A quantitative study to measure the family impact of e-learning
title_full_unstemmed Chapter A quantitative study to measure the family impact of e-learning
title_short Chapter A quantitative study to measure the family impact of e-learning
title_sort chapter a quantitative study to measure the family impact of e learning
topic Online learning
Quantile regression
Composite indicators
topic_facet Online learning
Quantile regression
Composite indicators
url ONIX_20220601_9788855183048_507
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