Frequency detection of experimental errors through Learning Analytics techniques

Detalhes bibliográficos
Autor(a) principal: Costa, Heverton Marcos
Data de Publicação: 2022
Outros Autores: Alves, Gustavo R., Silva, Juarez Bento Da, Mota Alves, Joao Bosco Da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/21484
Resumo: The process that systematically collects and analyzes large volumes of data in order to improve the teaching-learning process is called Learning Analytics. Based on data processing, educational data mining and visualization, it is possible to monitor academic progress, enhancing actions on how the teacher should conduct the discipline. The objective of this research is to mine data from experiments carried out in the remote laboratory called “Virtual Instrument Systems in Reality”. In order to create classification groups according to theoretical analysis studied in circuit analysis. The k-NN classification model was used for this research. The algorithm presented a very satisfactory result, its accuracy resulted in samples with values greater than 0.9. Considering an excellent form of classification analysis for circuits with 1: 1, 0: 1, 1:0 model.
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spelling Frequency detection of experimental errors through Learning Analytics techniquesLearning AnalyticsClassification AlgorithmRemote labsEngineering educationVISIRRemote LaboratorySimple Electrical CircuitsThe process that systematically collects and analyzes large volumes of data in order to improve the teaching-learning process is called Learning Analytics. Based on data processing, educational data mining and visualization, it is possible to monitor academic progress, enhancing actions on how the teacher should conduct the discipline. The objective of this research is to mine data from experiments carried out in the remote laboratory called “Virtual Instrument Systems in Reality”. In order to create classification groups according to theoretical analysis studied in circuit analysis. The k-NN classification model was used for this research. The algorithm presented a very satisfactory result, its accuracy resulted in samples with values greater than 0.9. Considering an excellent form of classification analysis for circuits with 1: 1, 0: 1, 1:0 model.IEEERepositório Científico do Instituto Politécnico do PortoCosta, Heverton MarcosAlves, Gustavo R.Silva, Juarez Bento DaMota Alves, Joao Bosco Da20222033-01-01T00:00:00Z2022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/21484eng10.1109/TAEE54169.2022.9840595metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-12T01:47:05Zoai:recipp.ipp.pt:10400.22/21484Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:41:32.582470Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Frequency detection of experimental errors through Learning Analytics techniques
title Frequency detection of experimental errors through Learning Analytics techniques
spellingShingle Frequency detection of experimental errors through Learning Analytics techniques
Costa, Heverton Marcos
Learning Analytics
Classification Algorithm
Remote labs
Engineering education
VISIR
Remote Laboratory
Simple Electrical Circuits
title_short Frequency detection of experimental errors through Learning Analytics techniques
title_full Frequency detection of experimental errors through Learning Analytics techniques
title_fullStr Frequency detection of experimental errors through Learning Analytics techniques
title_full_unstemmed Frequency detection of experimental errors through Learning Analytics techniques
title_sort Frequency detection of experimental errors through Learning Analytics techniques
author Costa, Heverton Marcos
author_facet Costa, Heverton Marcos
Alves, Gustavo R.
Silva, Juarez Bento Da
Mota Alves, Joao Bosco Da
author_role author
author2 Alves, Gustavo R.
Silva, Juarez Bento Da
Mota Alves, Joao Bosco Da
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Costa, Heverton Marcos
Alves, Gustavo R.
Silva, Juarez Bento Da
Mota Alves, Joao Bosco Da
dc.subject.por.fl_str_mv Learning Analytics
Classification Algorithm
Remote labs
Engineering education
VISIR
Remote Laboratory
Simple Electrical Circuits
topic Learning Analytics
Classification Algorithm
Remote labs
Engineering education
VISIR
Remote Laboratory
Simple Electrical Circuits
description The process that systematically collects and analyzes large volumes of data in order to improve the teaching-learning process is called Learning Analytics. Based on data processing, educational data mining and visualization, it is possible to monitor academic progress, enhancing actions on how the teacher should conduct the discipline. The objective of this research is to mine data from experiments carried out in the remote laboratory called “Virtual Instrument Systems in Reality”. In order to create classification groups according to theoretical analysis studied in circuit analysis. The k-NN classification model was used for this research. The algorithm presented a very satisfactory result, its accuracy resulted in samples with values greater than 0.9. Considering an excellent form of classification analysis for circuits with 1: 1, 0: 1, 1:0 model.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
2033-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/21484
url http://hdl.handle.net/10400.22/21484
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1109/TAEE54169.2022.9840595
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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