Automatic tutoring system to support cross-disciplinary training in Big Data

During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—r...

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Detalles Bibliográficos
Autores: Solé-Beteta, Xavier, Navarro, Joan, Vernet, David, Zaballos, Agustin, Torres Kompen, Ricardo, Fonseca, David, Briones, Alan
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:20.500.14342/3274
Acceso en línea:http://hdl.handle.net/20.500.14342/3274
https://doi.org/10.1007/s11227-020-03330-x
Access Level:acceso abierto
Palabra clave:Ensenyament universitari
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spelling Automatic tutoring system to support cross-disciplinary training in Big DataSolé-Beteta, XavierNavarro, JoanVernet, DavidZaballos, AgustinTorres Kompen, RicardoFonseca, DavidBriones, AlanEnsenyament universitariDades massives00437862During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which complicate the conception and deployment of academic programs and methodologies able to effectively train students in this discipline. The purpose of this paper is to propose a learning and teaching framework committed to train masters’ students in Big Data by conceiving an intelligent tutoring system aimed to (1) automatically tracking students’ progress, (2) effectively exploiting the diversity of their backgrounds, and (3) assisting the teaching staff on the course operation. Obtained results endorse the feasibility of this proposal and encourage practitioners to use this approach in other domains.SpringerUniversitat Ramon Llull. La Salle2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion24 p.http://hdl.handle.net/20.500.14342/3274https://doi.org/10.1007/s11227-020-03330-xreponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésThe Journal of Supercomputing, 2020, 1818-1852info:eu-repo/grantAgreement/SUR del DEC/SGR/2017-SGR-934info:eu-repo/grantAgreement/SUR del DEC/SGR/2017-SGR-977© L'autor/a. Tots el drets reservatsinfo:eu-repo/semantics/openAccessoai:recercat.cat:20.500.14342/32742026-05-29T05:05:01Z
dc.title.none.fl_str_mv Automatic tutoring system to support cross-disciplinary training in Big Data
title Automatic tutoring system to support cross-disciplinary training in Big Data
spellingShingle Automatic tutoring system to support cross-disciplinary training in Big Data
Solé-Beteta, Xavier
Ensenyament universitari
Dades massives
004
378
62
title_short Automatic tutoring system to support cross-disciplinary training in Big Data
title_full Automatic tutoring system to support cross-disciplinary training in Big Data
title_fullStr Automatic tutoring system to support cross-disciplinary training in Big Data
title_full_unstemmed Automatic tutoring system to support cross-disciplinary training in Big Data
title_sort Automatic tutoring system to support cross-disciplinary training in Big Data
dc.creator.none.fl_str_mv Solé-Beteta, Xavier
Navarro, Joan
Vernet, David
Zaballos, Agustin
Torres Kompen, Ricardo
Fonseca, David
Briones, Alan
author Solé-Beteta, Xavier
author_facet Solé-Beteta, Xavier
Navarro, Joan
Vernet, David
Zaballos, Agustin
Torres Kompen, Ricardo
Fonseca, David
Briones, Alan
author_role author
author2 Navarro, Joan
Vernet, David
Zaballos, Agustin
Torres Kompen, Ricardo
Fonseca, David
Briones, Alan
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. La Salle
dc.subject.none.fl_str_mv Ensenyament universitari
Dades massives
004
378
62
topic Ensenyament universitari
Dades massives
004
378
62
description During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which complicate the conception and deployment of academic programs and methodologies able to effectively train students in this discipline. The purpose of this paper is to propose a learning and teaching framework committed to train masters’ students in Big Data by conceiving an intelligent tutoring system aimed to (1) automatically tracking students’ progress, (2) effectively exploiting the diversity of their backgrounds, and (3) assisting the teaching staff on the course operation. Obtained results endorse the feasibility of this proposal and encourage practitioners to use this approach in other domains.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/3274
https://doi.org/10.1007/s11227-020-03330-x
url http://hdl.handle.net/20.500.14342/3274
https://doi.org/10.1007/s11227-020-03330-x
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The Journal of Supercomputing, 2020, 1818-1852
info:eu-repo/grantAgreement/SUR del DEC/SGR/2017-SGR-934
info:eu-repo/grantAgreement/SUR del DEC/SGR/2017-SGR-977
dc.rights.none.fl_str_mv © L'autor/a. Tots el drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © L'autor/a. Tots el drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24 p.
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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