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dc.contributor.authorSáiz Manzanares, María Consuelo 
dc.contributor.authorRodríguez Diez, Juan José 
dc.contributor.authorDiez Pastor, José Francisco 
dc.contributor.authorRodríguez Arribas, Sandra
dc.contributor.authorMarticorena Sánchez, Raúl 
dc.contributor.authorJi, Yi Peng
dc.date.accessioned2021-11-29T08:55:12Z
dc.date.available2021-11-29T08:55:12Z
dc.date.issued2021-03
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10259/6241
dc.description.abstractIn this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5.en
dc.description.sponsorshipCONSEJERÍA DE EDUCACIÓN DE LA JUNTA DE CASTILLA Y LEÓN (Spain), grant number BU032G19.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofApplied Sciences. 2021, V. 11, n. 6, 2677en
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAt-risk studenten
dc.subjectClusteringen
dc.subjectVisualisationen
dc.subjectSelf-regulated learningen
dc.subjectMoodleen
dc.subjectLearning analyticsen
dc.subject.otherEnseñanza superiores
dc.subject.otherEducation, Higheren
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherPsicologíaes
dc.subject.otherPsychologyen
dc.titleMonitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniquesen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app11062677es
dc.identifier.doi10.3390/app11062677
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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