Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/10071/20405 |
Resumo: | Many university programs include Microsoft Excel courses given their value as a scientific and technical tool. However, evaluating what is effectively learned by students is a challenging task. Considering multiple-choice written exams are a standard evaluation format, this study aimed to uncover the features influencing students’ success in answering these types of questions. The empirical experiments were based on Excel evaluation exams containing questions answered by 526 students between 2012 and 2016, with a total of 3,340 answers characterized by 17 features. Through data mining, a neural network was developed that accurately modeled students’ choices. A sensitivity analysis was applied to the model to assess the most relevant features. Findings identified four highly relevant features for students’ success: number of words of the question, topic, difficulty degree, and number of similar choices. This study helps to guide the design of future exams by quantifying the individual influence of each feature. |
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Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft ExcelData miningExcelFeature relevanceMultiple-choice questionsStudents’ performanceMany university programs include Microsoft Excel courses given their value as a scientific and technical tool. However, evaluating what is effectively learned by students is a challenging task. Considering multiple-choice written exams are a standard evaluation format, this study aimed to uncover the features influencing students’ success in answering these types of questions. The empirical experiments were based on Excel evaluation exams containing questions answered by 526 students between 2012 and 2016, with a total of 3,340 answers characterized by 17 features. Through data mining, a neural network was developed that accurately modeled students’ choices. A sensitivity analysis was applied to the model to assess the most relevant features. Findings identified four highly relevant features for students’ success: number of words of the question, topic, difficulty degree, and number of similar choices. This study helps to guide the design of future exams by quantifying the individual influence of each feature.Taylor and Francis2021-10-10T00:00:00Z2020-01-01T00:00:00Z20202020-11-26T13:43:19Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10071/20405eng1528-70330738-056910.1080/07380569.2020.1749127Moro, S.Martins, A.Ramos, P.Esmerado, J.Costa, J. M.Almeida, D.info: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:RCAAP2023-11-09T18:01:52Zoai:repositorio.iscte-iul.pt:10071/20405Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:33:12.942538Repositório 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 |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
title |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
spellingShingle |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel Moro, S. Data mining Excel Feature relevance Multiple-choice questions Students’ performance |
title_short |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
title_full |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
title_fullStr |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
title_full_unstemmed |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
title_sort |
Unfolding the drivers of students’ success in answering multiple-choice questions about Microsoft Excel |
author |
Moro, S. |
author_facet |
Moro, S. Martins, A. Ramos, P. Esmerado, J. Costa, J. M. Almeida, D. |
author_role |
author |
author2 |
Martins, A. Ramos, P. Esmerado, J. Costa, J. M. Almeida, D. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Moro, S. Martins, A. Ramos, P. Esmerado, J. Costa, J. M. Almeida, D. |
dc.subject.por.fl_str_mv |
Data mining Excel Feature relevance Multiple-choice questions Students’ performance |
topic |
Data mining Excel Feature relevance Multiple-choice questions Students’ performance |
description |
Many university programs include Microsoft Excel courses given their value as a scientific and technical tool. However, evaluating what is effectively learned by students is a challenging task. Considering multiple-choice written exams are a standard evaluation format, this study aimed to uncover the features influencing students’ success in answering these types of questions. The empirical experiments were based on Excel evaluation exams containing questions answered by 526 students between 2012 and 2016, with a total of 3,340 answers characterized by 17 features. Through data mining, a neural network was developed that accurately modeled students’ choices. A sensitivity analysis was applied to the model to assess the most relevant features. Findings identified four highly relevant features for students’ success: number of words of the question, topic, difficulty degree, and number of similar choices. This study helps to guide the design of future exams by quantifying the individual influence of each feature. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01T00:00:00Z 2020 2020-11-26T13:43:19Z 2021-10-10T00:00:00Z |
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/10071/20405 |
url |
http://hdl.handle.net/10071/20405 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1528-7033 0738-0569 10.1080/07380569.2020.1749127 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Taylor and Francis |
publisher.none.fl_str_mv |
Taylor and Francis |
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 |
institution |
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) |
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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1799134893754548225 |