Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

[EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to f...

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Detalles Bibliográficos
Autores: Moltó, Germán|||0000-0002-8049-253X, Segrelles Quilis, José Damián|||0000-0001-5698-7965, Naranjo-Delgado, Diana María
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/161978
Acceso en línea:https://riunet.upv.es/handle/10251/161978
Access Level:acceso abierto
Palabra clave:Learning analytics
Cloud computing
CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL
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spelling Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWSMoltó, Germán|||0000-0002-8049-253XSegrelles Quilis, José Damián|||0000-0001-5698-7965Naranjo-Delgado, Diana MaríaLearning analyticsCloud computingCIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL[EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.This research was funded by the Spanish "Ministerio de Economia, Industria y Competitividad through grant number TIN2016-79951-R (BigCLOE)", the "Vicerrectorado de Estudios, Calidad y Acreditacion" of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29 and PIME/19-20/166, and by the Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" with reference number AICO/2019/313.MDPI AGDepartamento de Sistemas Informáticos y ComputaciónEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialEscuela Técnica Superior de Ingeniería InformáticaInstituto de Instrumentación para Imagen MolecularGeneralitat ValencianaUniversitat Politècnica de ValènciaMinisterio de Economía y CompetitividadRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/161978reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PIME 2018-2019 B29 Comunidades de Aprendizaje como servicios en la nube para el desarrollo y evaluación automática de Competencias Transversales y Objetivos Formativos específicosUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PIME 2019-2020 B-19-20%2F166Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TIN2016-79951-R COMPUTACION BIG DATA Y DE ALTAS PRESTACIONES SOBRE MULTI-CLOUDS ELASTICOSGeneralitat Valenciana https://doi.org/10.13039/501100003359 AICO%2F2019%2F313open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1619782026-06-13T07:49:27Z
dc.title.none.fl_str_mv Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
title Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
spellingShingle Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
Moltó, Germán|||0000-0002-8049-253X
Learning analytics
Cloud computing
CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL
title_short Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
title_full Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
title_fullStr Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
title_full_unstemmed Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
title_sort Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
dc.creator.none.fl_str_mv Moltó, Germán|||0000-0002-8049-253X
Segrelles Quilis, José Damián|||0000-0001-5698-7965
Naranjo-Delgado, Diana María
author Moltó, Germán|||0000-0002-8049-253X
author_facet Moltó, Germán|||0000-0002-8049-253X
Segrelles Quilis, José Damián|||0000-0001-5698-7965
Naranjo-Delgado, Diana María
author_role author
author2 Segrelles Quilis, José Damián|||0000-0001-5698-7965
Naranjo-Delgado, Diana María
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Sistemas Informáticos y Computación
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Escuela Técnica Superior de Ingeniería Informática
Instituto de Instrumentación para Imagen Molecular
Generalitat Valenciana
Universitat Politècnica de València
Ministerio de Economía y Competitividad
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Learning analytics
Cloud computing
CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL
topic Learning analytics
Cloud computing
CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL
description [EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/161978
url https://riunet.upv.es/handle/10251/161978
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universitat Politècnica de València https://doi.org/10.13039/501100004233 PIME 2018-2019 B29 Comunidades de Aprendizaje como servicios en la nube para el desarrollo y evaluación automática de Competencias Transversales y Objetivos Formativos específicos
Universitat Politècnica de València https://doi.org/10.13039/501100004233 PIME 2019-2020 B-19-20%2F166
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TIN2016-79951-R COMPUTACION BIG DATA Y DE ALTAS PRESTACIONES SOBRE MULTI-CLOUDS ELASTICOS
Generalitat Valenciana https://doi.org/10.13039/501100003359 AICO%2F2019%2F313
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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