Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data min...

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Detalles Bibliográficos
Autores: Sáiz Manzanares, María Consuelo, Marticorena Sánchez, Raúl, Ochoa Orihuel, Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/7345
Acceso en línea:http://hdl.handle.net/10259/7345
Access Level:acceso abierto
Palabra clave:Advanced learning technologies
LMS
Machine learning
Self-regulated learning
Informática
Psicología
Computer science
Psychology
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spelling Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning TechniquesSáiz Manzanares, María ConsueloMarticorena Sánchez, RaúlOchoa Orihuel, JavierAdvanced learning technologiesLMSMachine learningSelf-regulated learningInformáticaPsicologíaComputer sciencePsychologyThe use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.This research was funded by the MINISTERIO DE CIENCIA E INNOVACIÓN, grant number PID2020-117111RB-I00.MDPI202320232021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/7345reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésElectronics. 2021, V. 10, n. 21, 2620https://doi.org/10.3390/electronics10212620info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117111RB-I00Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/73452026-05-28T07:56:11Z
dc.title.none.fl_str_mv Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
spellingShingle Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
Sáiz Manzanares, María Consuelo
Advanced learning technologies
LMS
Machine learning
Self-regulated learning
Informática
Psicología
Computer science
Psychology
title_short Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_full Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_fullStr Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_full_unstemmed Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_sort Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
dc.creator.none.fl_str_mv Sáiz Manzanares, María Consuelo
Marticorena Sánchez, Raúl
Ochoa Orihuel, Javier
author Sáiz Manzanares, María Consuelo
author_facet Sáiz Manzanares, María Consuelo
Marticorena Sánchez, Raúl
Ochoa Orihuel, Javier
author_role author
author2 Marticorena Sánchez, Raúl
Ochoa Orihuel, Javier
author2_role author
author
dc.subject.none.fl_str_mv Advanced learning technologies
LMS
Machine learning
Self-regulated learning
Informática
Psicología
Computer science
Psychology
topic Advanced learning technologies
LMS
Machine learning
Self-regulated learning
Informática
Psicología
Computer science
Psychology
description The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/7345
url http://hdl.handle.net/10259/7345
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Electronics. 2021, V. 10, n. 21, 2620
https://doi.org/10.3390/electronics10212620
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117111RB-I00
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
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
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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