Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do Mackenzie |
Texto Completo: | https://dspace.mackenzie.br/handle/10899/28617 |
Resumo: | Scientific production is one of the components considered by CAPES in the four-year evaluation of Stricto Sensu Graduate Programs (Masters and Doctoral Programs) in Brazil and directly influences the grade awarded to these Programs. Higher grades imply greater visibility and, consequently, the attraction of financial resources in the form of scholarships and funding for research. Thus, this paper aims to analyze the scientific production represented by the theses, dissertations, projects and book chapters generated in the period from 2013 to 2016, that is, the period coinciding with the 2017 evaluation for the four-year preceding period, in order to understand its relationship with the performance of the original Programs. This work not only consists of the quantitative analysis of the production of Graduate Programs, but also seeks, through techniques of artificial neural networks and text mining, to generate groups of Programs based on the similarity of their productions. The results obtained allow the identification of predominant patterns and characteristics of the Programs considered to be of excellence, which can be used as a reference by other Programs that wish to achieve the same performance. |
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2021-12-18T21:44:29Z2021-12-18T21:44:29Z2020-03-24CASARA, Mary Adriana. Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos. 2020. 70 f. Dissertação (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2020https://dspace.mackenzie.br/handle/10899/28617Scientific production is one of the components considered by CAPES in the four-year evaluation of Stricto Sensu Graduate Programs (Masters and Doctoral Programs) in Brazil and directly influences the grade awarded to these Programs. Higher grades imply greater visibility and, consequently, the attraction of financial resources in the form of scholarships and funding for research. Thus, this paper aims to analyze the scientific production represented by the theses, dissertations, projects and book chapters generated in the period from 2013 to 2016, that is, the period coinciding with the 2017 evaluation for the four-year preceding period, in order to understand its relationship with the performance of the original Programs. This work not only consists of the quantitative analysis of the production of Graduate Programs, but also seeks, through techniques of artificial neural networks and text mining, to generate groups of Programs based on the similarity of their productions. The results obtained allow the identification of predominant patterns and characteristics of the Programs considered to be of excellence, which can be used as a reference by other Programs that wish to achieve the same performance.A produção científica é um dos componentes considerados pela CAPES na avaliação quadrienal dos Programas de Pós-Graduação Stricto Sensu (Mestrado e Doutorado) do Brasil e influencia diretamente a nota atribuída a esses Programas. Maiores notas implicam maior visibilidade e, consequentemente, na atração de recursos financeiros na forma de bolsas de estudo e verbas de fomento à pesquisa. Sendo assim, este trabalho tem como objetivo analisar a produção científica representada pelas teses, dissertações, projetos, artigos e capítulos de livros publicados no período de 2013 a 2016, ou seja, o período que coincide com a avaliação quadrienal de 2017, para entender sua relação com o desempenho dos Programas de origem. Este trabalho não se limita `a análise quantitativa da produção dos Programas de Pós-Graduação, mas procura, por meio de técnicas de redes neurais artificiais e mineração de textos, gerar agrupamentos de Programas com base na similaridade de suas produções. Os resultados obtidos permitem que se identifiquem padrões e características predominantes dos Programas considerados de excelência, que podem ser utilizados como referência por outros Programas que desejam obter o mesmo desempenho.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorInstituto Presbiteriano Mackenzieapplication/pdfporUniversidade Presbiteriana MackenzieEngenharia ElétricaUPMBrasilEscola de Engenharia Mackenzie (EE)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessindicadores bibliométricosmineração de textosmineração de dadosmapas auto-organizáveisredes neurais artificiaismodelagem de tópicosCNPQ::ENGENHARIASAnálise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisSilva, Leandro Augusto dahttp://lattes.cnpq.br/1396385111251741 / https://orcid.org/0000-0002-8671-3102Notargiacomo, Pollyana Coelho da Silvahttp://lattes.cnpq.br/5131975026612008 / https://orcid.org/0000-0001-8292-1644Lopes, Paulo Batistahttp://lattes.cnpq.br/1678715490240349 / https://orcid.org/0000-0002-8070-1688Colugnati, Fernando Antonio Basilehttp://lattes.cnpq.br/1622643885752324 / https://orcid.org/0000-0002-8288-203Xhttp://lattes.cnpq.br/0782937908073737Casara, Mary Adrianabibliometric indicatorstext miningdata miningself-organizing mapsartificial neural networkstopic modelingreponame:Biblioteca Digital de Teses e Dissertações do Mackenzieinstname:Universidade Presbiteriana Mackenzie (MACKENZIE)instacron:MACKENZIEORIGINALMARY ADRIANA CASARA - protegido.pdfMary Adriana Casaraapplication/pdf6714769https://dspace.mackenzie.br/bitstream/10899/28617/1/MARY%20ADRIANA%20CASARA%20-%20protegido.pdf4e046cc89a059b1f32b3f7e2205dab90MD51CC-LICENSElicense_urlapplication/octet-stream49https://dspace.mackenzie.br/bitstream/10899/28617/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_textapplication/octet-stream0https://dspace.mackenzie.br/bitstream/10899/28617/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdfapplication/octet-stream0https://dspace.mackenzie.br/bitstream/10899/28617/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54LICENSElicense.txttext/plain2108https://dspace.mackenzie.br/bitstream/10899/28617/5/license.txt1ca4f25d161e955cf4b7a4aa65b8e96eMD55TEXTMARY ADRIANA CASARA - protegido.pdf.txtMARY ADRIANA CASARA - protegido.pdf.txtExtracted texttext/plain115669https://dspace.mackenzie.br/bitstream/10899/28617/6/MARY%20ADRIANA%20CASARA%20-%20protegido.pdf.txt8e15714125ca73eb7f4d001c2fc382d5MD56THUMBNAILMARY ADRIANA CASARA - 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dc.title.por.fl_str_mv |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
title |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
spellingShingle |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos Casara, Mary Adriana indicadores bibliométricos mineração de textos mineração de dados mapas auto-organizáveis redes neurais artificiais modelagem de tópicos CNPQ::ENGENHARIAS |
title_short |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
title_full |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
title_fullStr |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
title_full_unstemmed |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
title_sort |
Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos |
author |
Casara, Mary Adriana |
author_facet |
Casara, Mary Adriana |
author_role |
author |
dc.contributor.advisor-co1.fl_str_mv |
Silva, Leandro Augusto da |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/1396385111251741 / https://orcid.org/0000-0002-8671-3102 |
dc.contributor.advisor1.fl_str_mv |
Notargiacomo, Pollyana Coelho da Silva |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/5131975026612008 / https://orcid.org/0000-0001-8292-1644 |
dc.contributor.referee1.fl_str_mv |
Lopes, Paulo Batista |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/1678715490240349 / https://orcid.org/0000-0002-8070-1688 |
dc.contributor.referee2.fl_str_mv |
Colugnati, Fernando Antonio Basile |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/1622643885752324 / https://orcid.org/0000-0002-8288-203X |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0782937908073737 |
dc.contributor.author.fl_str_mv |
Casara, Mary Adriana |
contributor_str_mv |
Silva, Leandro Augusto da Notargiacomo, Pollyana Coelho da Silva Lopes, Paulo Batista Colugnati, Fernando Antonio Basile |
dc.subject.por.fl_str_mv |
indicadores bibliométricos mineração de textos mineração de dados mapas auto-organizáveis redes neurais artificiais modelagem de tópicos |
topic |
indicadores bibliométricos mineração de textos mineração de dados mapas auto-organizáveis redes neurais artificiais modelagem de tópicos CNPQ::ENGENHARIAS |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS |
description |
Scientific production is one of the components considered by CAPES in the four-year evaluation of Stricto Sensu Graduate Programs (Masters and Doctoral Programs) in Brazil and directly influences the grade awarded to these Programs. Higher grades imply greater visibility and, consequently, the attraction of financial resources in the form of scholarships and funding for research. Thus, this paper aims to analyze the scientific production represented by the theses, dissertations, projects and book chapters generated in the period from 2013 to 2016, that is, the period coinciding with the 2017 evaluation for the four-year preceding period, in order to understand its relationship with the performance of the original Programs. This work not only consists of the quantitative analysis of the production of Graduate Programs, but also seeks, through techniques of artificial neural networks and text mining, to generate groups of Programs based on the similarity of their productions. The results obtained allow the identification of predominant patterns and characteristics of the Programs considered to be of excellence, which can be used as a reference by other Programs that wish to achieve the same performance. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-03-24 |
dc.date.accessioned.fl_str_mv |
2021-12-18T21:44:29Z |
dc.date.available.fl_str_mv |
2021-12-18T21:44:29Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CASARA, Mary Adriana. Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos. 2020. 70 f. Dissertação (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2020 |
dc.identifier.uri.fl_str_mv |
https://dspace.mackenzie.br/handle/10899/28617 |
identifier_str_mv |
CASARA, Mary Adriana. Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos. 2020. 70 f. Dissertação (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2020 |
url |
https://dspace.mackenzie.br/handle/10899/28617 |
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por |
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por |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
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Universidade Presbiteriana Mackenzie |
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Engenharia Elétrica |
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UPM |
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Brasil |
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Escola de Engenharia Mackenzie (EE) |
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Universidade Presbiteriana Mackenzie |
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