Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | https://tede.unioeste.br/handle/tede/6941 |
Resumo: | The research, conducted using data from the AlfaCon Concursos Públicos Virtual Learn ing Environment, aimed primarily to develop and optimize a classifier to assist in the cat aloging of its Educational Objects. The adopted method began with detailed exploratory research on two distinct data sets. To achieve the proposed objectives, various classifi cation algorithms were tested, ranging from traditional techniques to more contempo rary machine learning and deep learning approaches. Among the algorithms evaluated, the Rocchio, Boosting, Bagging, Naïve Bayes, K-Nearest Neighbors, Support Vector Ma chine, Decision Tree, Random Forest, Recurrent Neural Network, Convolutional Neural Network, Deep Neural Network, Recurrent Convolutional Neural Network, X-Class, and PECOS were explored. Additionally, there was a particular emphasis on the use of the Support Vector Machine, due to the algorithm’s performance aligned with non-functional requirements highlighted by the company. The study also benefited from the adaptation of previously established codes, building upon the seminal research of other academics in the field. However, the results presented challenges that need to be addressed for the use of classifiers to assist in the cataloging process. Among these, we mainly highlight the classifications made at different levels of the taxonomies that represent the organization of the contents of the disciplines studied by students and in which the Educational Ob jects should be cataloged. Furthermore, the research identified that the limited number of documents available for certain labels had a direct impact on the classifier’s accuracy. In conclusion, while the research provided valuable insights into the potential and limitations of various classification techniques in the AlfaCon environment, it also emphasized the need for further investigations and optimizations. Through a comprehensive approach, the study explored multiple techniques and methods, providing a solid foundation for improving the accuracy and robustness of classification in the specific context of AlfaCon Concursos Públicos and for understanding the challenges of using them in an applied environment. |
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Rizzi, Claudia BrandeleroRizzi, Claudia BrandeleroRizzi, Rogerio LuisNaves, Thiago Françahttp://lattes.cnpq.br/4813756709802745Diógenes, Carlos Eduardo Rodrigues2023-12-12T14:50:31Z2023-11-06Diógenes, Carlos Eduardo Rodrigues. Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos. 2023. 228 f. Dissertação( Mestrado em Ciência da Computação) - Universidade Estadual do Oeste do Paraná, Cascavel.https://tede.unioeste.br/handle/tede/6941The research, conducted using data from the AlfaCon Concursos Públicos Virtual Learn ing Environment, aimed primarily to develop and optimize a classifier to assist in the cat aloging of its Educational Objects. The adopted method began with detailed exploratory research on two distinct data sets. To achieve the proposed objectives, various classifi cation algorithms were tested, ranging from traditional techniques to more contempo rary machine learning and deep learning approaches. Among the algorithms evaluated, the Rocchio, Boosting, Bagging, Naïve Bayes, K-Nearest Neighbors, Support Vector Ma chine, Decision Tree, Random Forest, Recurrent Neural Network, Convolutional Neural Network, Deep Neural Network, Recurrent Convolutional Neural Network, X-Class, and PECOS were explored. Additionally, there was a particular emphasis on the use of the Support Vector Machine, due to the algorithm’s performance aligned with non-functional requirements highlighted by the company. The study also benefited from the adaptation of previously established codes, building upon the seminal research of other academics in the field. However, the results presented challenges that need to be addressed for the use of classifiers to assist in the cataloging process. Among these, we mainly highlight the classifications made at different levels of the taxonomies that represent the organization of the contents of the disciplines studied by students and in which the Educational Ob jects should be cataloged. Furthermore, the research identified that the limited number of documents available for certain labels had a direct impact on the classifier’s accuracy. In conclusion, while the research provided valuable insights into the potential and limitations of various classification techniques in the AlfaCon environment, it also emphasized the need for further investigations and optimizations. Through a comprehensive approach, the study explored multiple techniques and methods, providing a solid foundation for improving the accuracy and robustness of classification in the specific context of AlfaCon Concursos Públicos and for understanding the challenges of using them in an applied environment.A pesquisa, conduzida com dados do Ambiente Virtual de Aprendizado do AlfaCon Con cursos Públicos, teve como principal objetivo desenvolver e otimizar um classificador para auxiliar na catalogação de seus Objetos Educacionais. O método adotado iniciou-se com uma pesquisa exploratória detalhada em dois conjuntos de dados distintos. Para atingir os objetivos propostos, foram testados diversos algoritmos de classificação, abrangendo desde técnicas tradicionais até abordagens mais contemporâneas de aprendizado de má quina e deep learning. Dentre os algoritmos avaliados, foram explorados o Rocchio, Boos ting, Bagging, Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Recurrent Neural Network, Convolutional Neural Network, Deep Neural Network, Recurrent Convolutional Neural Network, X-Class e PECOS. Em adição, houve uma ênfase particular no uso do Support Vector Machine, devido ao desempenho do al goritmo alinhado a requisitos não-funcionais destacados pela empresa. O estudo também se beneficiou da adaptação de códigos previamente estabelecidos, construindo sobre a pesquisa seminal de outros acadêmicos na área. Os resultados, no entanto, apresentaram desafios que precisam ser solucionados para a utilização dos classificadores para auxiliar no processo de catalogação. Entre estes destacamos principalmente as classificações reali zadas nos diferentes níveis das taxonomias que representam a organização dos conteúdos das disciplinas estudados pelos alunos e nos quais os Objetos Educacionais devem ser catalogadas. Além disso, a pesquisa identificou que a quantidade limitada de documentos disponíveis para certos rótulos teve um impacto direto na precisão do classificador. Em conclusão, enquanto a pesquisa forneceu insights valiosos sobre o potencial e as limita ções de várias técnicas de classificação no ambiente do AlfaCon, ela também destacou a necessidade de investigações e otimizações. Por meio de uma abordagem ampla, o estudo explorou múltiplas técnicas e métodos, fornecendo uma base sólida para melhorar a pre cisão e robustez da classificação no contexto específico do AlfaCon Concursos Públicos e para entender os desafios ao utilizá-las em um ambiente aplicado.Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2023-12-12T14:50:31Z No. of bitstreams: 1 Carlos Eduardo Rodrigues Diógenes.pdf: 9108821 bytes, checksum: 5267d4ba93fdb60bbf3ee502a49cddee (MD5)Made available in DSpace on 2023-12-12T14:50:31Z (GMT). No. of bitstreams: 1 Carlos Eduardo Rodrigues Diógenes.pdf: 9108821 bytes, checksum: 5267d4ba93fdb60bbf3ee502a49cddee (MD5) Previous issue date: 2023-11-06application/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Ciência da ComputaçãoUNIOESTEBrasilCentro de Ciências Exatas e Tecnológicashttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessCatalogação de objetos educacionaisClassificação de textoAprendizado de máquinaCataloging of educational objectsText classificationMachine learningCIÊNCIA DA COMPUTAÇÃOCatalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos PúblicosCataloging of Educational Objects Aided by Machine Learning for the AlfaCon Concursos Públicos Virtual Learning Environmentinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis19749965330812744706006002214374442868382015reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALCarlos Eduardo Rodrigues Diógenes.pdfCarlos Eduardo Rodrigues Diógenes.pdfapplication/pdf9108821http://tede.unioeste.br:8080/tede/bitstream/tede/6941/2/Carlos+Eduardo+Rodrigues+Di%C3%B3genes.pdf5267d4ba93fdb60bbf3ee502a49cddeeMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://tede.unioeste.br:8080/tede/bitstream/tede/6941/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/69412024-01-08 09:28:01.877oai:tede.unioeste.br:tede/6941Tk9UQTogQ09MT1FVRSBBUVVJIEEgU1VBIFBSw5NQUklBIExJQ0VOw4dBCkVzdGEgbGljZW7Dp2EgZGUgZXhlbXBsbyDDqSBmb3JuZWNpZGEgYXBlbmFzIHBhcmEgZmlucyBpbmZvcm1hdGl2b3MuCgpMSUNFTsOHQSBERSBESVNUUklCVUnDh8ODTyBOw4NPLUVYQ0xVU0lWQQoKQ29tIGEgYXByZXNlbnRhw6fDo28gZGVzdGEgbGljZW7Dp2EsIHZvY8OqIChvIGF1dG9yIChlcykgb3UgbyB0aXR1bGFyIGRvcyBkaXJlaXRvcyBkZSBhdXRvcikgY29uY2VkZSDDoCBVbml2ZXJzaWRhZGUgClhYWCAoU2lnbGEgZGEgVW5pdmVyc2lkYWRlKSBvIGRpcmVpdG8gbsOjby1leGNsdXNpdm8gZGUgcmVwcm9kdXppciwgIHRyYWR1emlyIChjb25mb3JtZSBkZWZpbmlkbyBhYmFpeG8pLCBlL291IApkaXN0cmlidWlyIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyAoaW5jbHVpbmRvIG8gcmVzdW1vKSBwb3IgdG9kbyBvIG11bmRvIG5vIGZvcm1hdG8gaW1wcmVzc28gZSBlbGV0csO0bmljbyBlIAplbSBxdWFscXVlciBtZWlvLCBpbmNsdWluZG8gb3MgZm9ybWF0b3Mgw6F1ZGlvIG91IHbDrWRlby4KClZvY8OqIGNvbmNvcmRhIHF1ZSBhIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSBwb2RlLCBzZW0gYWx0ZXJhciBvIGNvbnRlw7pkbywgdHJhbnNwb3IgYSBzdWEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIApwYXJhIHF1YWxxdWVyIG1laW8gb3UgZm9ybWF0byBwYXJhIGZpbnMgZGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIHRhbWLDqW0gY29uY29yZGEgcXVlIGEgU2lnbGEgZGUgVW5pdmVyc2lkYWRlIHBvZGUgbWFudGVyIG1haXMgZGUgdW1hIGPDs3BpYSBhIHN1YSB0ZXNlIG91IApkaXNzZXJ0YcOnw6NvIHBhcmEgZmlucyBkZSBzZWd1cmFuw6dhLCBiYWNrLXVwIGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIGRlY2xhcmEgcXVlIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyDDqSBvcmlnaW5hbCBlIHF1ZSB2b2PDqiB0ZW0gbyBwb2RlciBkZSBjb25jZWRlciBvcyBkaXJlaXRvcyBjb250aWRvcyAKbmVzdGEgbGljZW7Dp2EuIFZvY8OqIHRhbWLDqW0gZGVjbGFyYSBxdWUgbyBkZXDDs3NpdG8gZGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyBuw6NvLCBxdWUgc2VqYSBkZSBzZXUgCmNvbmhlY2ltZW50bywgaW5mcmluZ2UgZGlyZWl0b3MgYXV0b3JhaXMgZGUgbmluZ3XDqW0uCgpDYXNvIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyBjb250ZW5oYSBtYXRlcmlhbCBxdWUgdm9jw6ogbsOjbyBwb3NzdWkgYSB0aXR1bGFyaWRhZGUgZG9zIGRpcmVpdG9zIGF1dG9yYWlzLCB2b2PDqiAKZGVjbGFyYSBxdWUgb2J0ZXZlIGEgcGVybWlzc8OjbyBpcnJlc3RyaXRhIGRvIGRldGVudG9yIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBwYXJhIGNvbmNlZGVyIMOgIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSAKb3MgZGlyZWl0b3MgYXByZXNlbnRhZG9zIG5lc3RhIGxpY2Vuw6dhLCBlIHF1ZSBlc3NlIG1hdGVyaWFsIGRlIHByb3ByaWVkYWRlIGRlIHRlcmNlaXJvcyBlc3TDoSBjbGFyYW1lbnRlIAppZGVudGlmaWNhZG8gZSByZWNvbmhlY2lkbyBubyB0ZXh0byBvdSBubyBjb250ZcO6ZG8gZGEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIG9yYSBkZXBvc2l0YWRhLgoKQ0FTTyBBIFRFU0UgT1UgRElTU0VSVEHDh8ODTyBPUkEgREVQT1NJVEFEQSBURU5IQSBTSURPIFJFU1VMVEFETyBERSBVTSBQQVRST0PDjU5JTyBPVSAKQVBPSU8gREUgVU1BIEFHw4pOQ0lBIERFIEZPTUVOVE8gT1UgT1VUUk8gT1JHQU5JU01PIFFVRSBOw4NPIFNFSkEgQSBTSUdMQSBERSAKVU5JVkVSU0lEQURFLCBWT0PDiiBERUNMQVJBIFFVRSBSRVNQRUlUT1UgVE9ET1MgRSBRVUFJU1FVRVIgRElSRUlUT1MgREUgUkVWSVPDg08gQ09NTyAKVEFNQsOJTSBBUyBERU1BSVMgT0JSSUdBw4fDlUVTIEVYSUdJREFTIFBPUiBDT05UUkFUTyBPVSBBQ09SRE8uCgpBIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSBzZSBjb21wcm9tZXRlIGEgaWRlbnRpZmljYXIgY2xhcmFtZW50ZSBvIHNldSBub21lIChzKSBvdSBvKHMpIG5vbWUocykgZG8ocykgCmRldGVudG9yKGVzKSBkb3MgZGlyZWl0b3MgYXV0b3JhaXMgZGEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvLCBlIG7Do28gZmFyw6EgcXVhbHF1ZXIgYWx0ZXJhw6fDo28sIGFsw6ltIGRhcXVlbGFzIApjb25jZWRpZGFzIHBvciBlc3RhIGxpY2Vuw6dhLgo=Biblioteca Digital de Teses e Dissertaçõeshttp://tede.unioeste.br/PUBhttp://tede.unioeste.br/oai/requestbiblioteca.repositorio@unioeste.bropendoar:2024-01-08T12:28:01Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE)false |
dc.title.por.fl_str_mv |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
dc.title.alternative.eng.fl_str_mv |
Cataloging of Educational Objects Aided by Machine Learning for the AlfaCon Concursos Públicos Virtual Learning Environment |
title |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
spellingShingle |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos Diógenes, Carlos Eduardo Rodrigues Catalogação de objetos educacionais Classificação de texto Aprendizado de máquina Cataloging of educational objects Text classification Machine learning CIÊNCIA DA COMPUTAÇÃO |
title_short |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
title_full |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
title_fullStr |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
title_full_unstemmed |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
title_sort |
Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos |
author |
Diógenes, Carlos Eduardo Rodrigues |
author_facet |
Diógenes, Carlos Eduardo Rodrigues |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Rizzi, Claudia Brandelero |
dc.contributor.referee1.fl_str_mv |
Rizzi, Claudia Brandelero |
dc.contributor.referee2.fl_str_mv |
Rizzi, Rogerio Luis |
dc.contributor.referee3.fl_str_mv |
Naves, Thiago França |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4813756709802745 |
dc.contributor.author.fl_str_mv |
Diógenes, Carlos Eduardo Rodrigues |
contributor_str_mv |
Rizzi, Claudia Brandelero Rizzi, Claudia Brandelero Rizzi, Rogerio Luis Naves, Thiago França |
dc.subject.por.fl_str_mv |
Catalogação de objetos educacionais Classificação de texto Aprendizado de máquina |
topic |
Catalogação de objetos educacionais Classificação de texto Aprendizado de máquina Cataloging of educational objects Text classification Machine learning CIÊNCIA DA COMPUTAÇÃO |
dc.subject.eng.fl_str_mv |
Cataloging of educational objects Text classification Machine learning |
dc.subject.cnpq.fl_str_mv |
CIÊNCIA DA COMPUTAÇÃO |
description |
The research, conducted using data from the AlfaCon Concursos Públicos Virtual Learn ing Environment, aimed primarily to develop and optimize a classifier to assist in the cat aloging of its Educational Objects. The adopted method began with detailed exploratory research on two distinct data sets. To achieve the proposed objectives, various classifi cation algorithms were tested, ranging from traditional techniques to more contempo rary machine learning and deep learning approaches. Among the algorithms evaluated, the Rocchio, Boosting, Bagging, Naïve Bayes, K-Nearest Neighbors, Support Vector Ma chine, Decision Tree, Random Forest, Recurrent Neural Network, Convolutional Neural Network, Deep Neural Network, Recurrent Convolutional Neural Network, X-Class, and PECOS were explored. Additionally, there was a particular emphasis on the use of the Support Vector Machine, due to the algorithm’s performance aligned with non-functional requirements highlighted by the company. The study also benefited from the adaptation of previously established codes, building upon the seminal research of other academics in the field. However, the results presented challenges that need to be addressed for the use of classifiers to assist in the cataloging process. Among these, we mainly highlight the classifications made at different levels of the taxonomies that represent the organization of the contents of the disciplines studied by students and in which the Educational Ob jects should be cataloged. Furthermore, the research identified that the limited number of documents available for certain labels had a direct impact on the classifier’s accuracy. In conclusion, while the research provided valuable insights into the potential and limitations of various classification techniques in the AlfaCon environment, it also emphasized the need for further investigations and optimizations. Through a comprehensive approach, the study explored multiple techniques and methods, providing a solid foundation for improving the accuracy and robustness of classification in the specific context of AlfaCon Concursos Públicos and for understanding the challenges of using them in an applied environment. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-12-12T14:50:31Z |
dc.date.issued.fl_str_mv |
2023-11-06 |
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 |
Diógenes, Carlos Eduardo Rodrigues. Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos. 2023. 228 f. Dissertação( Mestrado em Ciência da Computação) - Universidade Estadual do Oeste do Paraná, Cascavel. |
dc.identifier.uri.fl_str_mv |
https://tede.unioeste.br/handle/tede/6941 |
identifier_str_mv |
Diógenes, Carlos Eduardo Rodrigues. Catalogação de Objetos Educacionais Auxiliado por Aprendizado de Máquina para o Ambiente Virtual de Aprendizado do AlfaCon Concursos Públicos. 2023. 228 f. Dissertação( Mestrado em Ciência da Computação) - Universidade Estadual do Oeste do Paraná, Cascavel. |
url |
https://tede.unioeste.br/handle/tede/6941 |
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600 600 |
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http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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Universidade Estadual do Oeste do Paraná Cascavel |
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Programa de Pós-Graduação em Ciência da Computação |
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UNIOESTE |
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Brasil |
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Centro de Ciências Exatas e Tecnológicas |
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Universidade Estadual do Oeste do Paraná Cascavel |
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