Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics

Detalhes bibliográficos
Autor(a) principal: Salas-Rueda , Ricardo-Adán
Data de Publicação: 2023
Outros Autores: Ramírez-Ortega, Jesús, Martínez-Ramírez, Selene-Marisol, Alvarado-Zamorano, Clara
Tipo de documento: Artigo
Idioma: spa
Título da fonte: Texto livre
Texto Completo: https://periodicos.ufmg.br/index.php/textolivre/article/view/41293
Resumo: The aim of this mixed study is to analyze the students' perceptions on the use of Moodle and smartphones in the educational process about Physics through Data Science. The algorithms of Machine Learning used are linear regression, decision tree and deep learning. In this research, the incorporation of Moodle facilitated the delivery of tasks, consultation of contents, communication and review of multimedia resources. Likewise, smartphones allowed the access to virtual learning platforms, use of mobile applications and communication from anywhere. The results of the linear regression and deep learning algorithms establish that the use of Moodle and smartphones positively influence the motivation of the students, assimilation of knowledge and satisfaction in the Physics course. On the other hand, the decision tree algorithm determines six predictive models. The limitations are the Machine Learning techniques used and the analysis of technological tools for the assimilation of knowledge, motivation and satisfaction. Future studies may look at the use of Moodle and smartphones for active role and skill development in various high schools and universities. Similarly, Machine Learning algorithms on random forests and logistic regression can be used to examine the impact of these technological tools considering academic performance. Finally, the incorporation of Moodle and smartphones allows updating the courses and designing creative distance activities.
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spelling Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of PhysicsUso de los algoritmos Machine Learning para analizar Moodle y los teléfonos inteligentes en el proceso educativo de la FísicaUso dos algoritmos Machine Learning para analisar o Moodle e os telefones inteligentes no processo educativo da FísicaMoodleTeléfonos inteligentes Aprendizaje máquinaAprendizaje profundoEducaciónMoodleSmartphonesAprendizado de máquinaAprendizagem profundaEducaçãoMoodleSmartphonesMachine learningDeep learningEducationThe aim of this mixed study is to analyze the students' perceptions on the use of Moodle and smartphones in the educational process about Physics through Data Science. The algorithms of Machine Learning used are linear regression, decision tree and deep learning. In this research, the incorporation of Moodle facilitated the delivery of tasks, consultation of contents, communication and review of multimedia resources. Likewise, smartphones allowed the access to virtual learning platforms, use of mobile applications and communication from anywhere. The results of the linear regression and deep learning algorithms establish that the use of Moodle and smartphones positively influence the motivation of the students, assimilation of knowledge and satisfaction in the Physics course. On the other hand, the decision tree algorithm determines six predictive models. The limitations are the Machine Learning techniques used and the analysis of technological tools for the assimilation of knowledge, motivation and satisfaction. Future studies may look at the use of Moodle and smartphones for active role and skill development in various high schools and universities. Similarly, Machine Learning algorithms on random forests and logistic regression can be used to examine the impact of these technological tools considering academic performance. Finally, the incorporation of Moodle and smartphones allows updating the courses and designing creative distance activities.El objetivo de este estudio mixto es analizar las percepciones de los alumnos sobre el uso de Moodle y los teléfonos inteligentes en el proceso educativo de la Física a través de la Ciencia de Datos. Los algoritmos Machine Learning utilizados son regresión lineal, árbol de decisión y deep learning. En este estudio, la incorporación de Moodle facilitó la entrega de tareas, la consulta de los contenidos, la comunicación y la revisión de los recursos multimedia. Incluso, los teléfonos inteligentes permitieron el acceso a las plataformas virtuales de aprendizaje, el uso de las aplicaciones móviles y la comunicación desde cualquier lugar. Los resultados de los algoritmos regresión lineal y deep learning indican que el uso de Moodle y los teléfonos inteligentes influye positivamente la motivación de los alumnos, la asimilación del conocimiento y la satisfacción en el curso Física. Por otro lado, el algoritmo árbol de decisión determina seis modelos predictivos. Las limitaciones son las técnicas de Machine Learning utilizadas y el análisis de las herramientas tecnológicas para la asimilación del conocimiento, la motivación y la satisfacción. Los futuros estudios pueden analizar el uso de Moodle y los teléfonos inteligentes para el rol activo y el desarrollo de las habilidades en diversas preparatorias y universidades. Asimismo, los algoritmos Machine Learning sobre los bosques aleatorios y la regresión logística pueden ser empleados para analizar el impacto de estas herramientas tecnológicas considerando el rendimiento académico. Por último, la incorporación de Moodle y los teléfonos inteligentes permite actualizar los cursos y diseñar creativas actividades a distancia.O objetivo deste estudo misto é analisar as percepções dos alunos sobre o uso do Moodle e smartphones no processo educacional de Física por meio da Ciência de Dados. Os algoritmos de Machine Learning utilizados são regressão linear, árvore de decisão e deep learning. Neste estudo, a incorporação do Moodle facilitou a entrega de tarefas, a consulta dos conteúdos, a comunicação e a revisão dos recursos multimédia. Os smartphones permitiram ainda o acesso a plataformas virtuais de aprendizagem, a utilização de aplicações móveis e a comunicação a partir de qualquer lugar. Os resultados dos algoritmos de regressão linear e deep learning indicam que o uso do Moodle e smartphones influencia positivamente a motivação dos alunos, a assimilação do conhecimento e a satisfação no curso de Física. Por outro lado, o algoritmo da árvore de decisão determina seis modelos preditivos. As limitações são as técnicas de Machine Learning utilizadas e a análise de ferramentas tecnológicas para a assimilação do conhecimento, motivação e satisfação. Estudos futuros podem analisar o uso do Moodle e smartphones para papel ativo e desenvolvimento de habilidades em várias escolas de ensino médio e universidades. Da mesma forma, algoritmos de Machine Learning sobre florestas aleatórias e regressão logística podem ser usados para analisar o impacto dessas ferramentas tecnológicas no desempenho acadêmico. Por último, a incorporação do Moodle e dos smartphones permite a atualização dos cursos e o desenho de atividades criativas a distância.Universidade Federal de Minas Gerais2023-01-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtigo avaliado pelos paresapplication/pdfhttps://periodicos.ufmg.br/index.php/textolivre/article/view/4129310.1590/1983-3652.2023.41293Texto Livre; Vol. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293Texto Livre; Vol. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293Texto Livre; Vol. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293Texto Livre; v. 16 (2023): Texto Livre: Linguagem e Tecnologia; e412931983-3652reponame:Texto livreinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGspahttps://periodicos.ufmg.br/index.php/textolivre/article/view/41293/32395Copyright (c) 2023 Ricardo-Adán Salas-Rueda , Jesús Ramírez-Ortega, Selene-Marisol Martínez-Ramírez, Clara Alvarado-Zamoranohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSalas-Rueda , Ricardo-Adán Ramírez-Ortega, Jesús Martínez-Ramírez, Selene-MarisolAlvarado-Zamorano, Clara2024-01-08T13:17:28Zoai:periodicos.ufmg.br:article/41293Revistahttp://www.periodicos.letras.ufmg.br/index.php/textolivrePUBhttps://periodicos.ufmg.br/index.php/textolivre/oairevistatextolivre@letras.ufmg.br1983-36521983-3652opendoar:2024-01-08T13:17:28Texto livre - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
Uso de los algoritmos Machine Learning para analizar Moodle y los teléfonos inteligentes en el proceso educativo de la Física
Uso dos algoritmos Machine Learning para analisar o Moodle e os telefones inteligentes no processo educativo da Física
title Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
spellingShingle Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
Salas-Rueda , Ricardo-Adán
Moodle
Teléfonos inteligentes
Aprendizaje máquina
Aprendizaje profundo
Educación
Moodle
Smartphones
Aprendizado de máquina
Aprendizagem profunda
Educação
Moodle
Smartphones
Machine learning
Deep learning
Education
title_short Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
title_full Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
title_fullStr Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
title_full_unstemmed Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
title_sort Use of Machine Learning algorithms to analyze Moodle and smartphones in the educational process of Physics
author Salas-Rueda , Ricardo-Adán
author_facet Salas-Rueda , Ricardo-Adán
Ramírez-Ortega, Jesús
Martínez-Ramírez, Selene-Marisol
Alvarado-Zamorano, Clara
author_role author
author2 Ramírez-Ortega, Jesús
Martínez-Ramírez, Selene-Marisol
Alvarado-Zamorano, Clara
author2_role author
author
author
dc.contributor.author.fl_str_mv Salas-Rueda , Ricardo-Adán
Ramírez-Ortega, Jesús
Martínez-Ramírez, Selene-Marisol
Alvarado-Zamorano, Clara
dc.subject.por.fl_str_mv Moodle
Teléfonos inteligentes
Aprendizaje máquina
Aprendizaje profundo
Educación
Moodle
Smartphones
Aprendizado de máquina
Aprendizagem profunda
Educação
Moodle
Smartphones
Machine learning
Deep learning
Education
topic Moodle
Teléfonos inteligentes
Aprendizaje máquina
Aprendizaje profundo
Educación
Moodle
Smartphones
Aprendizado de máquina
Aprendizagem profunda
Educação
Moodle
Smartphones
Machine learning
Deep learning
Education
description The aim of this mixed study is to analyze the students' perceptions on the use of Moodle and smartphones in the educational process about Physics through Data Science. The algorithms of Machine Learning used are linear regression, decision tree and deep learning. In this research, the incorporation of Moodle facilitated the delivery of tasks, consultation of contents, communication and review of multimedia resources. Likewise, smartphones allowed the access to virtual learning platforms, use of mobile applications and communication from anywhere. The results of the linear regression and deep learning algorithms establish that the use of Moodle and smartphones positively influence the motivation of the students, assimilation of knowledge and satisfaction in the Physics course. On the other hand, the decision tree algorithm determines six predictive models. The limitations are the Machine Learning techniques used and the analysis of technological tools for the assimilation of knowledge, motivation and satisfaction. Future studies may look at the use of Moodle and smartphones for active role and skill development in various high schools and universities. Similarly, Machine Learning algorithms on random forests and logistic regression can be used to examine the impact of these technological tools considering academic performance. Finally, the incorporation of Moodle and smartphones allows updating the courses and designing creative distance activities.
publishDate 2023
dc.date.none.fl_str_mv 2023-01-18
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artigo avaliado pelos pares
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufmg.br/index.php/textolivre/article/view/41293
10.1590/1983-3652.2023.41293
url https://periodicos.ufmg.br/index.php/textolivre/article/view/41293
identifier_str_mv 10.1590/1983-3652.2023.41293
dc.language.iso.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://periodicos.ufmg.br/index.php/textolivre/article/view/41293/32395
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://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 Universidade Federal de Minas Gerais
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv Texto Livre; Vol. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293
Texto Livre; Vol. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293
Texto Livre; Vol. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293
Texto Livre; v. 16 (2023): Texto Livre: Linguagem e Tecnologia; e41293
1983-3652
reponame:Texto livre
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Texto livre
collection Texto livre
repository.name.fl_str_mv Texto livre - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv revistatextolivre@letras.ufmg.br
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