A multiple-indicator latent growth mixture model to track courses with low-quality teaching
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/10071/20032 |
Resumo: | This paper describes a multi-indicator latent growth mixture model built on the data collected by a large Italian university to track students’ satisfaction over time. The analysis of the data involves two steps: first, a pre-processing of data selects the items to be part of the synthetic indicator that measures students’ satisfaction; the second step then retrieves heterogeneity that allows the identification of a clustering structure with a group of university courses (outliers) which underperform in terms of students’ satisfaction over time. Regression components of the model identify courses in need of further improvement and that are prone to receiving low classifications from students. Results show that it is possible to identify a large group of didactic activities with a high satisfaction level that stays constant over time; there is also a small group of problematic didactic activities with low satisfaction that decreases over the period under analysis. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A multiple-indicator latent growth mixture model to track courses with low-quality teachingHigher educationQuality of didacticsLatent growth mixture modelsOutlier detectionSynthetic indicatorData scienceThis paper describes a multi-indicator latent growth mixture model built on the data collected by a large Italian university to track students’ satisfaction over time. The analysis of the data involves two steps: first, a pre-processing of data selects the items to be part of the synthetic indicator that measures students’ satisfaction; the second step then retrieves heterogeneity that allows the identification of a clustering structure with a group of university courses (outliers) which underperform in terms of students’ satisfaction over time. Regression components of the model identify courses in need of further improvement and that are prone to receiving low classifications from students. Results show that it is possible to identify a large group of didactic activities with a high satisfaction level that stays constant over time; there is also a small group of problematic didactic activities with low satisfaction that decreases over the period under analysis.Springer Netherlands2020-08-01T00:00:00Z2020-01-01T00:00:00Z20202020-03-05T11:14:10Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/20032eng0303-830010.1007/s11205-019-02169-xGuerra, M.Bassi, F.Dias, J. G.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:RCAAP2023-11-09T17:36:46Zoai:repositorio.iscte-iul.pt:10071/20032Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:16:45.196484Repositório 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 |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
title |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
spellingShingle |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching Guerra, M. Higher education Quality of didactics Latent growth mixture models Outlier detection Synthetic indicator Data science |
title_short |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
title_full |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
title_fullStr |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
title_full_unstemmed |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
title_sort |
A multiple-indicator latent growth mixture model to track courses with low-quality teaching |
author |
Guerra, M. |
author_facet |
Guerra, M. Bassi, F. Dias, J. G. |
author_role |
author |
author2 |
Bassi, F. Dias, J. G. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Guerra, M. Bassi, F. Dias, J. G. |
dc.subject.por.fl_str_mv |
Higher education Quality of didactics Latent growth mixture models Outlier detection Synthetic indicator Data science |
topic |
Higher education Quality of didactics Latent growth mixture models Outlier detection Synthetic indicator Data science |
description |
This paper describes a multi-indicator latent growth mixture model built on the data collected by a large Italian university to track students’ satisfaction over time. The analysis of the data involves two steps: first, a pre-processing of data selects the items to be part of the synthetic indicator that measures students’ satisfaction; the second step then retrieves heterogeneity that allows the identification of a clustering structure with a group of university courses (outliers) which underperform in terms of students’ satisfaction over time. Regression components of the model identify courses in need of further improvement and that are prone to receiving low classifications from students. Results show that it is possible to identify a large group of didactic activities with a high satisfaction level that stays constant over time; there is also a small group of problematic didactic activities with low satisfaction that decreases over the period under analysis. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-01T00:00:00Z 2020-01-01T00:00:00Z 2020 2020-03-05T11:14:10Z |
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/10071/20032 |
url |
http://hdl.handle.net/10071/20032 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0303-8300 10.1007/s11205-019-02169-x |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer Netherlands |
publisher.none.fl_str_mv |
Springer Netherlands |
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 |
institution |
RCAAP |
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 |
repository.mail.fl_str_mv |
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1799134726227755008 |