Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista de Engenharia e Pesquisa Aplicada |
Texto Completo: | http://revistas.poli.br/index.php/repa/article/view/2224 |
Resumo: | The growing demand for STEM professionals was accelerated by the COVID-19 pandemic. In Brazil, the low investment and incentive for this particular area generates a deficit of professionals, meaning that there are not as many professionals as the market demands. In the state of Pernambuco, this scenario is even worse, so the government of the state has launched a technological extension program (PET) aiming to decrease this deficit. This program has generated a great amount of data that needed to be studied, analyzed and processed in order to lead to a better understanding of the program and also make it better in the next rounds. Therefore, some clustering techniques were used, like Agglomerative Algorithm, k-Modes, SOM maps with the objective of analyzing and absorbing some insights from the data. The metrics utilized to evaluate those clusters were the silhouette coefficient, purity and elbow method. The clusters resulting from those techniques showed important features of the project, in addition it showed that the project is being well evaluated by the beneficiaries and, so, achieving its goal. |
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Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering TechniquesAnálise do Programa de Extensão Tecnológica de Pernambuco usando Técnicas de Aglomeração de DadosThe growing demand for STEM professionals was accelerated by the COVID-19 pandemic. In Brazil, the low investment and incentive for this particular area generates a deficit of professionals, meaning that there are not as many professionals as the market demands. In the state of Pernambuco, this scenario is even worse, so the government of the state has launched a technological extension program (PET) aiming to decrease this deficit. This program has generated a great amount of data that needed to be studied, analyzed and processed in order to lead to a better understanding of the program and also make it better in the next rounds. Therefore, some clustering techniques were used, like Agglomerative Algorithm, k-Modes, SOM maps with the objective of analyzing and absorbing some insights from the data. The metrics utilized to evaluate those clusters were the silhouette coefficient, purity and elbow method. The clusters resulting from those techniques showed important features of the project, in addition it showed that the project is being well evaluated by the beneficiaries and, so, achieving its goal.A demanda por profissionais relacionados à área de STEM é crescente e foi ainda mais acelerado pela pandemia do COVID-19. No Brasil, o baixo investimento e incentivo à área faz com que se forme menos profissionais em STEM do que a demanda. Em Pernambuco o cenário é ainda mais agravado e, pensando nisso, o governo do estado lançou um programa de extensão tecnológica (PET) com o intuito de incentivar a formação de profissionais na área. Tal programa gerou uma quantidade de dados e, com isso, uma demanda de processamento e estudo destes para que seja possível tanto um entendimento de como o programa está funcionando, como para gerar um melhoramento deste para as próximas rodadas. Assim, foram utilizados algoritmos de aglomeração de dados, como Algoritmo Aglomerativo, k-Modes e Mapas SOM para analisar e gerar resultados a partir dos dados coletados. As métricas utilizadas para avaliar os agrupamentos gerados foram coeficientes de silhueta, pureza e método do cotovelo. Os agrupamentos gerados por estas técnicas mostraram características importantes do programa, além de evidenciar que este está sendo bem avaliado pelos seus beneficiários e, então, cumprindo com seu propósito.Escola Politécnica de Pernambuco2022-07-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado pelos paresapplication/pdftext/htmlhttp://revistas.poli.br/index.php/repa/article/view/222410.25286/repa.v7i2.2224Journal of Engineering and Applied Research; Vol 7 No 2 (2022): Edição Especial em Inteligência Artificial; 118-128Revista de Engenharia e Pesquisa Aplicada; v. 7 n. 2 (2022): Edição Especial em Inteligência Artificial; 118-1282525-425110.25286/repa.v7i2reponame:Revista de Engenharia e Pesquisa Aplicadainstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPEporhttp://revistas.poli.br/index.php/repa/article/view/2224/835http://revistas.poli.br/index.php/repa/article/view/2224/836-Copyright (c) 2022 Thaise dos Santos Tenório, Victor Hugo Wanderley Freire, Carmelo José Albanez Bastos Filho, Emilia Rahnemay Kohlman Rabbanihttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessFreire, Victor Hugo WanderleyBastos Filho, Carmelo José AlbanezRabbani, Emilia Rahnemay Kohlman2022-07-17T20:06:52Zoai:ojs.poli.br:article/2224Revistahttp://revistas.poli.br/index.php/repaONGhttp://revistas.poli.br/index.php/repa/oai||repa@poli.br2525-42512525-4251opendoar:2022-07-17T20:06:52Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE)false |
dc.title.none.fl_str_mv |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques Análise do Programa de Extensão Tecnológica de Pernambuco usando Técnicas de Aglomeração de Dados |
title |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques |
spellingShingle |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques Freire, Victor Hugo Wanderley |
title_short |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques |
title_full |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques |
title_fullStr |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques |
title_full_unstemmed |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques |
title_sort |
Analysis of the Technology Extension Program from the Government of the state of Pernambuco Using Data Clustering Techniques |
author |
Freire, Victor Hugo Wanderley |
author_facet |
Freire, Victor Hugo Wanderley Bastos Filho, Carmelo José Albanez Rabbani, Emilia Rahnemay Kohlman |
author_role |
author |
author2 |
Bastos Filho, Carmelo José Albanez Rabbani, Emilia Rahnemay Kohlman |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Freire, Victor Hugo Wanderley Bastos Filho, Carmelo José Albanez Rabbani, Emilia Rahnemay Kohlman |
description |
The growing demand for STEM professionals was accelerated by the COVID-19 pandemic. In Brazil, the low investment and incentive for this particular area generates a deficit of professionals, meaning that there are not as many professionals as the market demands. In the state of Pernambuco, this scenario is even worse, so the government of the state has launched a technological extension program (PET) aiming to decrease this deficit. This program has generated a great amount of data that needed to be studied, analyzed and processed in order to lead to a better understanding of the program and also make it better in the next rounds. Therefore, some clustering techniques were used, like Agglomerative Algorithm, k-Modes, SOM maps with the objective of analyzing and absorbing some insights from the data. The metrics utilized to evaluate those clusters were the silhouette coefficient, purity and elbow method. The clusters resulting from those techniques showed important features of the project, in addition it showed that the project is being well evaluated by the beneficiaries and, so, achieving its goal. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-17 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Avaliado pelos pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2224 10.25286/repa.v7i2.2224 |
url |
http://revistas.poli.br/index.php/repa/article/view/2224 |
identifier_str_mv |
10.25286/repa.v7i2.2224 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
http://revistas.poli.br/index.php/repa/article/view/2224/835 http://revistas.poli.br/index.php/repa/article/view/2224/836 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.coverage.none.fl_str_mv |
- |
dc.publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
publisher.none.fl_str_mv |
Escola Politécnica de Pernambuco |
dc.source.none.fl_str_mv |
Journal of Engineering and Applied Research; Vol 7 No 2 (2022): Edição Especial em Inteligência Artificial; 118-128 Revista de Engenharia e Pesquisa Aplicada; v. 7 n. 2 (2022): Edição Especial em Inteligência Artificial; 118-128 2525-4251 10.25286/repa.v7i2 reponame:Revista de Engenharia e Pesquisa Aplicada instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
instname_str |
Universidade Federal de Pernambuco (UFPE) |
instacron_str |
UFPE |
institution |
UFPE |
reponame_str |
Revista de Engenharia e Pesquisa Aplicada |
collection |
Revista de Engenharia e Pesquisa Aplicada |
repository.name.fl_str_mv |
Revista de Engenharia e Pesquisa Aplicada - Universidade Federal de Pernambuco (UFPE) |
repository.mail.fl_str_mv |
||repa@poli.br |
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1798036000463650816 |