Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment

Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function of their performance and we d...

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Autores principales:	Laia Subirats, Santi Fort, Santiago Atrio, Gomez-Monivas Sacha
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	supervised learning Applied Computing intelligent tutoring system COVID-19 Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d937329876574734bfe5df6034fa47a0
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spelling	oai:doaj.org-article:d937329876574734bfe5df6034fa47a02021-11-11T15:01:08ZArtificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment10.3390/app112199232076-3417https://doaj.org/article/d937329876574734bfe5df6034fa47a02021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9923https://doaj.org/toc/2076-3417Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function of their performance and we describe the best self-learning methodologies in distance learning, which will be useful both in confinement or for people with difficult access to schools. Due to the different learning scenarios provided by the different confinement conditions in the COVID-19 pandemic, we have performed the classification considering data before, during, and after COVID-19 confinement. Using a field experiment of 396 students, we have described the temporal evolution of students during all courses from 2016/2017 to 2020/2021. We have found that data obtained in the last month before the final exam of the subject include the most relevant information for a correct detection of students at risk of failure. On the other hand, students who obtain high scores are much easier to identify. Finally, we have concluded that the distance learning applied in COVID-19 confinement changed not only teaching strategies but also students’ strategies when learning autonomously.Laia SubiratsSanti FortSantiago AtrioGomez-Monivas SachaMDPI AGarticlesupervised learningApplied Computingintelligent tutoring systemCOVID-19TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9923, p 9923 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	supervised learning Applied Computing intelligent tutoring system COVID-19 Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	supervised learning Applied Computing intelligent tutoring system COVID-19 Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Laia Subirats Santi Fort Santiago Atrio Gomez-Monivas Sacha Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
description	Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function of their performance and we describe the best self-learning methodologies in distance learning, which will be useful both in confinement or for people with difficult access to schools. Due to the different learning scenarios provided by the different confinement conditions in the COVID-19 pandemic, we have performed the classification considering data before, during, and after COVID-19 confinement. Using a field experiment of 396 students, we have described the temporal evolution of students during all courses from 2016/2017 to 2020/2021. We have found that data obtained in the last month before the final exam of the subject include the most relevant information for a correct detection of students at risk of failure. On the other hand, students who obtain high scores are much easier to identify. Finally, we have concluded that the distance learning applied in COVID-19 confinement changed not only teaching strategies but also students’ strategies when learning autonomously.
format	article
author	Laia Subirats Santi Fort Santiago Atrio Gomez-Monivas Sacha
author_facet	Laia Subirats Santi Fort Santiago Atrio Gomez-Monivas Sacha
author_sort	Laia Subirats
title	Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
title_short	Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
title_full	Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
title_fullStr	Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
title_full_unstemmed	Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
title_sort	artificial intelligence to counterweight the effect of covid-19 on learning in a sustainable environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d937329876574734bfe5df6034fa47a0
work_keys_str_mv	AT laiasubirats artificialintelligencetocounterweighttheeffectofcovid19onlearninginasustainableenvironment AT santifort artificialintelligencetocounterweighttheeffectofcovid19onlearninginasustainableenvironment AT santiagoatrio artificialintelligencetocounterweighttheeffectofcovid19onlearninginasustainableenvironment AT gomezmonivassacha artificialintelligencetocounterweighttheeffectofcovid19onlearninginasustainableenvironment
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Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment

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