Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus

In recent years, the use of Learning Management Systems (LMS) has grown considerably. This has had a strong effect on the learning process, particularly in higher education. Most universities incorporate LMS as a complement to face-to-face classes in order to improve the student learning process. Ho...

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Autores: Regueras Santos, Luisa María, Verdú Pérez, María Jesús, Castro Fernández, Juan Pablo de, Verdú Pérez, Elena
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/65502
Acceso en línea:https://doi.org/10.1109/ACCESS.2019.2943212
https://uvadoc.uva.es/handle/10324/65502
Access Level:acceso abierto
Palabra clave:Data mining
Education
Tools
Clustering methods
Feature extraction
Learning systems
Machine learning
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spelling Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual CampusRegueras Santos, Luisa MaríaVerdú Pérez, María JesúsCastro Fernández, Juan Pablo deVerdú Pérez, ElenaData miningEducationToolsClustering methodsFeature extractionClustering methodsData miningLearning systemsMachine learningIn recent years, the use of Learning Management Systems (LMS) has grown considerably. This has had a strong effect on the learning process, particularly in higher education. Most universities incorporate LMS as a complement to face-to-face classes in order to improve the student learning process. However, not all teachers use LMS in the same way and universities lack the tools to measure and quantify their use effectively. This study proposes a method to automatically classify and certify teacher competence in LMS from the LMS data. Objective knowledge of actual LMS use will help the university and its faculty to make strategic decisions. The information produced will be used to support teachers and institutions in the classification and design of courses by showing the different LMS usage patterns of teachers and students. In this study, we processed the structure of 3,303 courses and two million interactive events to obtain a classification model based on LMS usage patterns in blended learning. Three clustering methods were compared to find which one was best suited to our problem. The resulting model is clearly related to different course archetypes that can be used to describe the actual use of LMS. We also performed analyses of prediction accuracy and of course typologies across course attributes (academic disciplines and level and academic performance indicators). The results of this study will be used as the basis for an automatic expert system that automatically certifies teacher competence in LMS as evidenced in each course.IEEE2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1109/ACCESS.2019.2943212https://uvadoc.uva.es/handle/10324/65502reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidInglésinfo:eu-repo/semantics/openAccessoai:uvadoc.uva.es:10324/655022026-06-13T12:44:47Z
dc.title.none.fl_str_mv Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
title Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
spellingShingle Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
Regueras Santos, Luisa María
Data mining
Education
Tools
Clustering methods
Feature extraction
Clustering methods
Data mining
Learning systems
Machine learning
title_short Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
title_full Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
title_fullStr Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
title_full_unstemmed Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
title_sort Clustering Analysis for Automatic Certification of LMS Strategies in a University Virtual Campus
dc.creator.none.fl_str_mv Regueras Santos, Luisa María
Verdú Pérez, María Jesús
Castro Fernández, Juan Pablo de
Verdú Pérez, Elena
author Regueras Santos, Luisa María
author_facet Regueras Santos, Luisa María
Verdú Pérez, María Jesús
Castro Fernández, Juan Pablo de
Verdú Pérez, Elena
author_role author
author2 Verdú Pérez, María Jesús
Castro Fernández, Juan Pablo de
Verdú Pérez, Elena
author2_role author
author
author
dc.subject.none.fl_str_mv Data mining
Education
Tools
Clustering methods
Feature extraction
Clustering methods
Data mining
Learning systems
Machine learning
topic Data mining
Education
Tools
Clustering methods
Feature extraction
Clustering methods
Data mining
Learning systems
Machine learning
description In recent years, the use of Learning Management Systems (LMS) has grown considerably. This has had a strong effect on the learning process, particularly in higher education. Most universities incorporate LMS as a complement to face-to-face classes in order to improve the student learning process. However, not all teachers use LMS in the same way and universities lack the tools to measure and quantify their use effectively. This study proposes a method to automatically classify and certify teacher competence in LMS from the LMS data. Objective knowledge of actual LMS use will help the university and its faculty to make strategic decisions. The information produced will be used to support teachers and institutions in the classification and design of courses by showing the different LMS usage patterns of teachers and students. In this study, we processed the structure of 3,303 courses and two million interactive events to obtain a classification model based on LMS usage patterns in blended learning. Three clustering methods were compared to find which one was best suited to our problem. The resulting model is clearly related to different course archetypes that can be used to describe the actual use of LMS. We also performed analyses of prediction accuracy and of course typologies across course attributes (academic disciplines and level and academic performance indicators). The results of this study will be used as the basis for an automatic expert system that automatically certifies teacher competence in LMS as evidenced in each course.
publishDate 2019
dc.date.none.fl_str_mv 2019
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 https://doi.org/10.1109/ACCESS.2019.2943212
https://uvadoc.uva.es/handle/10324/65502
url https://doi.org/10.1109/ACCESS.2019.2943212
https://uvadoc.uva.es/handle/10324/65502
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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