dc.contributorCosta, José Alfredo Ferreira
dc.contributorhttp://lattes.cnpq.br/8884696501382310
dc.contributorhttp://lattes.cnpq.br/9745845064013172
dc.contributorSilva, Gutembergue Soares da
dc.contributorhttp://lattes.cnpq.br/1214925346969928
dc.contributorSilva, Leonardo Enzo Brito da
dc.contributorSouza, Ricardo Pires de
dc.contributorhttps://orcid.org/0000-0002-2922-0985
dc.contributorhttp://lattes.cnpq.br/6380923718767667
dc.creatorMaia Júnior, Manoel Isac
dc.date.accessioned2022-06-03T23:36:58Z
dc.date.accessioned2022-10-06T12:44:51Z
dc.date.available2022-06-03T23:36:58Z
dc.date.available2022-10-06T12:44:51Z
dc.date.created2022-06-03T23:36:58Z
dc.date.issued2020-11-12
dc.identifierMAIA JÚNIOR, Manoel Isac. Avaliação de desempenho acadêmico utilizando redes neurais: uma análise exploratória de dados de rankings universitários. 2020. 134f. Dissertação (Mestrado em Engenharia de Produção) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2020.
dc.identifierhttps://repositorio.ufrn.br/handle/123456789/47540
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3958232
dc.description.abstractThe world lives in an era of knowledge and, due to a changing scenario, countries see in universities the possibility of being included in the world circuit of knowledge and skills. University evaluation has been the focus of several rankings worldwide and is an example of influence in the paradigm shift in universities, as the positive results from the evaluation process provide these institutions with a reputation, social and academic prestige, in addition to a benchmark among institutions about the services and practices provided in addition to the strengths and weaknesses of each course / university. In general, information on teaching, research and internationalization activities are combined to generate a grade used in a ranking order. This method is widely criticized for not showing unanimity in the formulation of its indicators and for relating the performance of an institution to just a number. In view of the above, this dissertation proposed an alternative means to academic ranking, the use of clustering techniques with neural networks. Self-organizing maps (SOM) are models of competitive neural networks. Through unsupervised learning, they perform a mapping between multidimensional data, generally two-dimensional, which approximates the original density of the information, being a technique widely used in areas such as data analysis and pattern recognition. This work presents a cross-sectional analysis of data from Brazilian universities through the training of maps with data from the 2014 and 2019 Ranking Universitário da Folha. From the profiles of the clusters, after the segmentation of the trained maps, it is possible to identify the positive points and of each group. With the identification of Higher Education Institutions (HEIs) in these different years, an analysis of the transitions between the clusters in the years 2014 and 2019 was carried out. Comparisons of the profiles of the clusters are shown in order to characterize their behavior in the analyzed period and showing a new one. As an alternative to the analysis of HEI performance data, the study also allows for the verification of disparities between the regions of Brazil.
dc.publisherUniversidade Federal do Rio Grande do Norte
dc.publisherBrasil
dc.publisherUFRN
dc.publisherPROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE PRODUÇÃO
dc.rightsAcesso Aberto
dc.subjectAnálise de dados
dc.subjectMapas auto-organizáveis
dc.subjectInstituições de Ensino Superior
dc.subjectAnálise de agrupamentos
dc.titleAvaliação de desempenho acadêmico utilizando redes neurais: uma análise exploratória de dados de rankings universitários
dc.typemasterThesis


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