Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/11328/3862 https://doi.org/10.3390/bdcc5040081 |
Resumo: | Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment |
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Clustering Algorithm to Measure Student Assessment Accuracy: A Double StudySelf-assessmentSelf-evaluationHigher educationClusteringAccuracySelf-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation momentMDPI - Multidisciplinary Digital Publishing Institute2021-12-23T17:09:31Z2021-12-232021-12-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSobral, S. R., & Oliveira, C. F. (2021). Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study. Big Data and Cognitive Computing, 5(4), 81. doi: https://doi.org/10.3390/bdcc5040081. Disponível no Repositório UPT, http://hdl.handle.net/11328/3862http://hdl.handle.net/11328/3862Sobral, S. R., & Oliveira, C. F. (2021). Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study. Big Data and Cognitive Computing, 5(4), 81. doi: https://doi.org/10.3390/bdcc5040081. Disponível no Repositório UPT, http://hdl.handle.net/11328/3862http://hdl.handle.net/11328/3862https://doi.org/10.3390/bdcc5040081eng2504-2289http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSobral, Sónia RollandOliveira, Catarina Félix dereponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-16T02:10:18Zoai:repositorio.upt.pt:11328/3862Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:40:43.902332Repositório 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 |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
title |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
spellingShingle |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study Sobral, Sónia Rolland Self-assessment Self-evaluation Higher education Clustering Accuracy |
title_short |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
title_full |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
title_fullStr |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
title_full_unstemmed |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
title_sort |
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study |
author |
Sobral, Sónia Rolland |
author_facet |
Sobral, Sónia Rolland Oliveira, Catarina Félix de |
author_role |
author |
author2 |
Oliveira, Catarina Félix de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Sobral, Sónia Rolland Oliveira, Catarina Félix de |
dc.subject.por.fl_str_mv |
Self-assessment Self-evaluation Higher education Clustering Accuracy |
topic |
Self-assessment Self-evaluation Higher education Clustering Accuracy |
description |
Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-23T17:09:31Z 2021-12-23 2021-12-18T00:00:00Z |
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 |
Sobral, S. R., & Oliveira, C. F. (2021). Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study. Big Data and Cognitive Computing, 5(4), 81. doi: https://doi.org/10.3390/bdcc5040081. Disponível no Repositório UPT, http://hdl.handle.net/11328/3862 http://hdl.handle.net/11328/3862 Sobral, S. R., & Oliveira, C. F. (2021). Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study. Big Data and Cognitive Computing, 5(4), 81. doi: https://doi.org/10.3390/bdcc5040081. Disponível no Repositório UPT, http://hdl.handle.net/11328/3862 http://hdl.handle.net/11328/3862 https://doi.org/10.3390/bdcc5040081 |
identifier_str_mv |
Sobral, S. R., & Oliveira, C. F. (2021). Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study. Big Data and Cognitive Computing, 5(4), 81. doi: https://doi.org/10.3390/bdcc5040081. Disponível no Repositório UPT, http://hdl.handle.net/11328/3862 |
url |
http://hdl.handle.net/11328/3862 https://doi.org/10.3390/bdcc5040081 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2504-2289 |
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 |
MDPI - Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
MDPI - Multidisciplinary Digital Publishing Institute |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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