Educational data mining in a discipline offered in the distance learning modality
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/7428 |
Resumo: | Data mining seeks to identify relationships between data, to create information that can generate new knowledge for the development of science, as well as for decision-making by subsidizing new actions that transform the current reality. And the use of educational data mining techniques, which focuses on the development of methods to explore these data sets. In this study, we tried to analyze the accesses and student grades from a virtual learning environment, measuring the evolution according to the number of accesses. Three classes of the mathematical financial discipline offered in the second semester of 2019 were considered for the research, with the data extracted from all the user activities in the content areas, carrying out an analysis according to the CRISP-DM process with implementation in the software. R and RStudio. The results indicate that the longest period of access to the study environment is between Monday and Wednesday, and it has been shown that with a minimum of 55 times of interaction in the environment, students tend to obtain the minimum grade for approval, while that access over 100 times indicate notes close to the maximum value. |
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Educational data mining in a discipline offered in the distance learning modalityMinería de datos educativos en una disciplina ofrecida en la modalidad de educación a distanciaMineração de dados educacionais em uma disciplina ofertada na modalidade de ensino a distânciaDiscovery of knowledgeData miningDecision-making process.Descubrimiento del conocimientoProcesamiento de datosProceso de toma de decisiones.Descoberta do conhecimentoMineração de dadosProcesso decisório.Data mining seeks to identify relationships between data, to create information that can generate new knowledge for the development of science, as well as for decision-making by subsidizing new actions that transform the current reality. And the use of educational data mining techniques, which focuses on the development of methods to explore these data sets. In this study, we tried to analyze the accesses and student grades from a virtual learning environment, measuring the evolution according to the number of accesses. Three classes of the mathematical financial discipline offered in the second semester of 2019 were considered for the research, with the data extracted from all the user activities in the content areas, carrying out an analysis according to the CRISP-DM process with implementation in the software. R and RStudio. The results indicate that the longest period of access to the study environment is between Monday and Wednesday, and it has been shown that with a minimum of 55 times of interaction in the environment, students tend to obtain the minimum grade for approval, while that access over 100 times indicate notes close to the maximum value.La minería de datos busca identificar relaciones entre datos, generar información que pueda generar nuevos conocimientos para el desarrollo de la ciencia, así como para la toma de decisiones mediante el subsidio de nuevas acciones que transformen la realidad actual. Y el uso de técnicas de minería de datos educativos, que se centra en el desarrollo de métodos para explorar estos conjuntos de datos. En este estudio, se intentó analizar los accesos y las calificaciones de los alumnos desde un entorno de aprendizaje virtual, midiendo la evolución en función del número de accesos. Se consideraron para la investigación tres clases de la disciplina matemática financiera ofrecida en el segundo semestre de 2019, con los datos extraídos de todas las actividades del usuario en las áreas de contenido, realizando un análisis según el proceso CRISP-DM con implementación en el software. R y RStudio. Los resultados indican que el período más largo de acceso al ambiente de estudio es entre el lunes y el miércoles, y se ha demostrado que con un mínimo de 55 veces de interacción en el ambiente, los estudiantes tienden a obtener la nota mínima de aprobación, mientras que que acceden más de 100 veces indican notas cercanas al valor máximo.A mineração de dados procura identificar relações entre os dados, para se criar informações que podem gerar novos conhecimentos ao desenvolvimento da ciência, assim como para tomada de decisões subsidiando novas ações que transforme a realidade atual. E o uso de técnicas de mineração de dados educacionais, que tem como foco o desenvolvimento de métodos para se explorar estes conjuntos de dados. Procurou-se analisar neste estudo, os acessos e notas discente a partir de um ambiente virtual de aprendizagem, aferindo a evolução de acordo com o número de acesos. Foram consideradas para a pesquisa três turmas da disciplina matemática financeira ofertada no segundo semestre de 2019, com os dados extraídos de todas as atividades do usuário nas áreas de conteúdo, realizando-se uma análise de acordo com o processo CRISP-DM com implementação no software R e RStudio. Os resultados indicam que o maior período acessos ao ambiente de estudo, estão entre segunda e quarta-feira, e comprovou-se que com o mínimo de 55 vezes de interação no ambiente, os discentes tendem em obter a nota mínima para aprovação, ao passo que acesso acima de 100 vezes indicam notas próximas ao valor máximo.Research, Society and Development2020-08-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/742810.33448/rsd-v9i9.7428Research, Society and Development; Vol. 9 No. 9; e347997428Research, Society and Development; Vol. 9 Núm. 9; e347997428Research, Society and Development; v. 9 n. 9; e3479974282525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/7428/6504Copyright (c) 2020 Enir da Silva Fonseca; Carlos Fernando de Araújo Jr; Frederico Kauffmann Barbosa; Luiz Henrique Amaralhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessFonseca, Enir da Silva Araújo Jr, Carlos Fernando deBarbosa, Frederico Kauffmann Amaral, Luiz Henrique 2020-09-18T01:42:11Zoai:ojs.pkp.sfu.ca:article/7428Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:30:09.310038Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Educational data mining in a discipline offered in the distance learning modality Minería de datos educativos en una disciplina ofrecida en la modalidad de educación a distancia Mineração de dados educacionais em uma disciplina ofertada na modalidade de ensino a distância |
title |
Educational data mining in a discipline offered in the distance learning modality |
spellingShingle |
Educational data mining in a discipline offered in the distance learning modality Fonseca, Enir da Silva Discovery of knowledge Data mining Decision-making process. Descubrimiento del conocimiento Procesamiento de datos Proceso de toma de decisiones. Descoberta do conhecimento Mineração de dados Processo decisório. |
title_short |
Educational data mining in a discipline offered in the distance learning modality |
title_full |
Educational data mining in a discipline offered in the distance learning modality |
title_fullStr |
Educational data mining in a discipline offered in the distance learning modality |
title_full_unstemmed |
Educational data mining in a discipline offered in the distance learning modality |
title_sort |
Educational data mining in a discipline offered in the distance learning modality |
author |
Fonseca, Enir da Silva |
author_facet |
Fonseca, Enir da Silva Araújo Jr, Carlos Fernando de Barbosa, Frederico Kauffmann Amaral, Luiz Henrique |
author_role |
author |
author2 |
Araújo Jr, Carlos Fernando de Barbosa, Frederico Kauffmann Amaral, Luiz Henrique |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Fonseca, Enir da Silva Araújo Jr, Carlos Fernando de Barbosa, Frederico Kauffmann Amaral, Luiz Henrique |
dc.subject.por.fl_str_mv |
Discovery of knowledge Data mining Decision-making process. Descubrimiento del conocimiento Procesamiento de datos Proceso de toma de decisiones. Descoberta do conhecimento Mineração de dados Processo decisório. |
topic |
Discovery of knowledge Data mining Decision-making process. Descubrimiento del conocimiento Procesamiento de datos Proceso de toma de decisiones. Descoberta do conhecimento Mineração de dados Processo decisório. |
description |
Data mining seeks to identify relationships between data, to create information that can generate new knowledge for the development of science, as well as for decision-making by subsidizing new actions that transform the current reality. And the use of educational data mining techniques, which focuses on the development of methods to explore these data sets. In this study, we tried to analyze the accesses and student grades from a virtual learning environment, measuring the evolution according to the number of accesses. Three classes of the mathematical financial discipline offered in the second semester of 2019 were considered for the research, with the data extracted from all the user activities in the content areas, carrying out an analysis according to the CRISP-DM process with implementation in the software. R and RStudio. The results indicate that the longest period of access to the study environment is between Monday and Wednesday, and it has been shown that with a minimum of 55 times of interaction in the environment, students tend to obtain the minimum grade for approval, while that access over 100 times indicate notes close to the maximum value. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-20 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/7428 10.33448/rsd-v9i9.7428 |
url |
https://rsdjournal.org/index.php/rsd/article/view/7428 |
identifier_str_mv |
10.33448/rsd-v9i9.7428 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/7428/6504 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
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 |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 9 No. 9; e347997428 Research, Society and Development; Vol. 9 Núm. 9; e347997428 Research, Society and Development; v. 9 n. 9; e347997428 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052830995972096 |