Mostra i principali dati dell'item

dc.contributor.authorSáiz Manzanares, María Consuelo 
dc.contributor.authorMarticorena Sánchez, Raúl 
dc.contributor.authorOchoa Orihuel, Javier
dc.date.accessioned2023-01-31T08:30:28Z
dc.date.available2023-01-31T08:30:28Z
dc.date.issued2021-10
dc.identifier.urihttp://hdl.handle.net/10259/7345
dc.description.abstractThe 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.en
dc.description.sponsorshipThis research was funded by the MINISTERIO DE CIENCIA E INNOVACIÓN, grant number PID2020-117111RB-I00.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofElectronics. 2021, V. 10, n. 21, 2620es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAdvanced learning technologiesen
dc.subjectLMSen
dc.subjectMachine learningen
dc.subjectSelf-regulated learningen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherPsicologíaes
dc.subject.otherPsychologyen
dc.titleUsing Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniquesen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/electronics10212620es
dc.identifier.doi10.3390/electronics10212620
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117111RB-I00/ES/ASISTENTES DE VOZ E INTELIGENCIA ARTIFICIAL EN MOODLE: UN CAMINO HACIA UNA UNIVERSIDAD INTELIGENTE/es
dc.identifier.essn2079-9292
dc.journal.titleElectronicses
dc.volume.number10es
dc.issue.number21es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


Files in questo item

Thumbnail

Questo item appare nelle seguenti collezioni

Mostra i principali dati dell'item