Monitoring Students at the University: Design and Application of a Moodle Plugin

Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish un...

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Detalles Bibliográficos
Autores: Sáiz Manzanares, María Consuelo, Marticorena Sánchez, Raúl, García Osorio, César
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:10259/6244
Acceso en línea:http://hdl.handle.net/10259/6244
Access Level:acceso abierto
Palabra clave:Student guidance
Personalized learning
Machine Learning
Moodle
Plugin
Enseñanza superior
Psicología
Informática
Education, Higher
Psychology
Computer science
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spelling Monitoring Students at the University: Design and Application of a Moodle PluginSáiz Manzanares, María ConsueloMarticorena Sánchez, RaúlGarcía Osorio, CésarStudent guidancePersonalized learningMachine LearningMoodlePluginEnseñanza superiorPsicologíaInformáticaEducation, HigherPsychologyComputer scienceEarly detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.Consejería de Educación de la Junta de Castilla y León (Spain) (Department of Education of the Junta de Castilla y León), grant number BU032G19, and grants from the University of Burgos for the dissemination and the improvement of teaching innovation experiences of the Vice-Rectorate of Teaching and Research Staff, the Vice-Rectorate for Research and Knowledge Transfer, 2020, and the Departamento de Ciencias de la Salud the University of Burgos (Spain).MDPI202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/6244reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésApplied Sciences. 2020, V. 10, n. 10, 3469https://doi.org/10.3390/app10103469info: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:10259/62442026-05-28T07:56:11Z
dc.title.none.fl_str_mv Monitoring Students at the University: Design and Application of a Moodle Plugin
title Monitoring Students at the University: Design and Application of a Moodle Plugin
spellingShingle Monitoring Students at the University: Design and Application of a Moodle Plugin
Sáiz Manzanares, María Consuelo
Student guidance
Personalized learning
Machine Learning
Moodle
Plugin
Enseñanza superior
Psicología
Informática
Education, Higher
Psychology
Computer science
title_short Monitoring Students at the University: Design and Application of a Moodle Plugin
title_full Monitoring Students at the University: Design and Application of a Moodle Plugin
title_fullStr Monitoring Students at the University: Design and Application of a Moodle Plugin
title_full_unstemmed Monitoring Students at the University: Design and Application of a Moodle Plugin
title_sort Monitoring Students at the University: Design and Application of a Moodle Plugin
dc.creator.none.fl_str_mv Sáiz Manzanares, María Consuelo
Marticorena Sánchez, Raúl
García Osorio, César
author Sáiz Manzanares, María Consuelo
author_facet Sáiz Manzanares, María Consuelo
Marticorena Sánchez, Raúl
García Osorio, César
author_role author
author2 Marticorena Sánchez, Raúl
García Osorio, César
author2_role author
author
dc.subject.none.fl_str_mv Student guidance
Personalized learning
Machine Learning
Moodle
Plugin
Enseñanza superior
Psicología
Informática
Education, Higher
Psychology
Computer science
topic Student guidance
Personalized learning
Machine Learning
Moodle
Plugin
Enseñanza superior
Psicología
Informática
Education, Higher
Psychology
Computer science
description Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.
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/10259/6244
url http://hdl.handle.net/10259/6244
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Sciences. 2020, V. 10, n. 10, 3469
https://doi.org/10.3390/app10103469
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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