Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

In 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...

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Autores: Sáiz Manzanares, María Consuelo, Rodríguez Diez, Juan José, Diez Pastor, José Francisco, Rodríguez Arribas, Sandra, Marticorena Sánchez, Raúl, Ji, Yi Peng
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:10259/6241
Acceso en línea:http://hdl.handle.net/10259/6241
Access Level:acceso abierto
Palabra clave:At-risk student
Clustering
Visualisation
Self-regulated learning
Moodle
Learning analytics
Enseñanza superior
Informática
Psicología
Education, Higher
Computer science
Psychology
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spelling Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining TechniquesSáiz Manzanares, María ConsueloRodríguez Diez, Juan JoséDiez Pastor, José FranciscoRodríguez Arribas, SandraMarticorena Sánchez, RaúlJi, Yi PengAt-risk studentClusteringVisualisationSelf-regulated learningMoodleLearning analyticsEnseñanza superiorInformáticaPsicologíaEducation, HigherComputer sciencePsychologyIn 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.CONSEJERÍA DE EDUCACIÓN DE LA JUNTA DE CASTILLA Y LEÓN (Spain), grant number BU032G19.MDPI202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/6241reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésApplied Sciences. 2021, V. 11, n. 6, 2677https://doi.org/10.3390/app11062677info:eu-repo/grantAgreement/Junta de Castilla y León//BU032G19Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/62412026-05-28T07:56:11Z
dc.title.none.fl_str_mv Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
title Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
spellingShingle Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
Sáiz Manzanares, María Consuelo
At-risk student
Clustering
Visualisation
Self-regulated learning
Moodle
Learning analytics
Enseñanza superior
Informática
Psicología
Education, Higher
Computer science
Psychology
title_short Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
title_full Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
title_fullStr Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
title_full_unstemmed Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
title_sort Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
dc.creator.none.fl_str_mv Sáiz Manzanares, María Consuelo
Rodríguez Diez, Juan José
Diez Pastor, José Francisco
Rodríguez Arribas, Sandra
Marticorena Sánchez, Raúl
Ji, Yi Peng
author Sáiz Manzanares, María Consuelo
author_facet Sáiz Manzanares, María Consuelo
Rodríguez Diez, Juan José
Diez Pastor, José Francisco
Rodríguez Arribas, Sandra
Marticorena Sánchez, Raúl
Ji, Yi Peng
author_role author
author2 Rodríguez Diez, Juan José
Diez Pastor, José Francisco
Rodríguez Arribas, Sandra
Marticorena Sánchez, Raúl
Ji, Yi Peng
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv At-risk student
Clustering
Visualisation
Self-regulated learning
Moodle
Learning analytics
Enseñanza superior
Informática
Psicología
Education, Higher
Computer science
Psychology
topic At-risk student
Clustering
Visualisation
Self-regulated learning
Moodle
Learning analytics
Enseñanza superior
Informática
Psicología
Education, Higher
Computer science
Psychology
description In 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
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/10259/6241
url http://hdl.handle.net/10259/6241
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Sciences. 2021, V. 11, n. 6, 2677
https://doi.org/10.3390/app11062677
info:eu-repo/grantAgreement/Junta de Castilla y León//BU032G19
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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