SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão
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| Main Author: | |
|---|---|
| Publication Date: | 2024 |
| Format: | Master thesis |
| Language: | por |
| Source: | Repositório Institucional da UPF |
| Download full: | https://repositorio.upf.br/handle/123456789/1738 |
Summary: | This work aimed to predict the risk of losing a student's residence high school students, employing artificial intelligence, which was used in the area of Machine Learning, machine learning algorithms. The analysis is based in various data related to student residence, including information about the student, course, number of hours taught, measure, sector, number of days suspended, gender, grade and enrollment to generate a predictive system. The risk of losing student residence consists of the fact that the student can no longer remain as residing close to the campus, causing several difficulties, such as living outside the limits of the institution, having to pay rent, a reality not considered for many students from our institution. The act of remaining resident is a reflection of the The student's pattern of behavior is seen as the result of the model. Three Different machine learning algorithms were considered for identify and classify the parameters that affect permanence in the residence student: Naive-Bayes, KNN, Decision Tree. To evaluate the performance of machine learning algorithms, three metrics were used: accuracy, recall and F1-score. The results indicate that KNN outperformed other techniques, generating superior results, followed by Naive-Bayes. It is concluded that there is a way to predict the risk of loss of student residence based on a standard of student behavior. A web application was developed for the presentation of results. According to the experiment carried out, the approach proved to be adequate and can serve as decision-making in actions aimed at better relationship between Student Residence and students, in addition to improving students' social skills, necessary for socializing among peers. |
| _version_ | 1871607631555067904 |
|---|---|
| author | Pereira, Cedemir |
| author_browse | Pereira, Cedemir |
| author_facet | Pereira, Cedemir |
| author_role | author |
| collection | Repositório Institucional da UPF |
| dc.contributor.author.fl_str_mv | Pereira, Cedemir |
| dc.contributor.none.fl_str_mv | Rabello, Roberto dos Santos http://lattes.cnpq.br/1386464075867571 |
| dc.date.none.fl_str_mv | 2024-08-30 2025-05-07T12:44:28Z |
| dc.format.none.fl_str_mv | application/pdf application/pdf |
| dc.identifier.uri.fl_str_mv | PEREIRA, Cedemir. SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão. 2024. 81 f. Dissertação (Mestrado em Computação Aplicada) - Universidade de Passo Fundo, Passo Fundo, RS, 2024. https://repositorio.upf.br/handle/123456789/1738 |
| dc.language.iso.fl_str_mv | por |
| dc.publisher.none.fl_str_mv | Universidade de Passo Fundo Instituto de Tecnologia – ITEC Brasil UPF Programa de Pós-Graduação em Computação Aplicada |
| dc.rights.driver.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:Repositório Institucional da UPF instname:Universidade de Passo Fundo (UPF) instacron:UPF |
| dc.subject.por.fl_str_mv | Inteligência artificial Machine learning Estudantes do ensino médio - Comportamento Algoritmos CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| dc.title.none.fl_str_mv | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| dc.type.driver.fl_str_mv | info:eu-repo/semantics/masterThesis |
| dc.type.status.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | This work aimed to predict the risk of losing a student's residence high school students, employing artificial intelligence, which was used in the area of Machine Learning, machine learning algorithms. The analysis is based in various data related to student residence, including information about the student, course, number of hours taught, measure, sector, number of days suspended, gender, grade and enrollment to generate a predictive system. The risk of losing student residence consists of the fact that the student can no longer remain as residing close to the campus, causing several difficulties, such as living outside the limits of the institution, having to pay rent, a reality not considered for many students from our institution. The act of remaining resident is a reflection of the The student's pattern of behavior is seen as the result of the model. Three Different machine learning algorithms were considered for identify and classify the parameters that affect permanence in the residence student: Naive-Bayes, KNN, Decision Tree. To evaluate the performance of machine learning algorithms, three metrics were used: accuracy, recall and F1-score. The results indicate that KNN outperformed other techniques, generating superior results, followed by Naive-Bayes. It is concluded that there is a way to predict the risk of loss of student residence based on a standard of student behavior. A web application was developed for the presentation of results. According to the experiment carried out, the approach proved to be adequate and can serve as decision-making in actions aimed at better relationship between Student Residence and students, in addition to improving students' social skills, necessary for socializing among peers. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | UPF_636bf0930b2bd83ae4e52493bc2a2fa2 |
| identifier_str_mv | PEREIRA, Cedemir. SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão. 2024. 81 f. Dissertação (Mestrado em Computação Aplicada) - Universidade de Passo Fundo, Passo Fundo, RS, 2024. |
| instacron_str | UPF |
| institution | UPF |
| instname_str | Universidade de Passo Fundo (UPF) |
| language | por |
| network_acronym_str | UPF |
| network_name_str | Repositório Institucional da UPF |
| oai_identifier_str | oai:repositorio.upf.br:123456789/1738 |
| publishDate | 2024 |
| publishDateSort | 2024 |
| publisher.none.fl_str_mv | Universidade de Passo Fundo Instituto de Tecnologia – ITEC Brasil UPF Programa de Pós-Graduação em Computação Aplicada |
| reponame_str | Repositório Institucional da UPF |
| repository.mail.fl_str_mv | jucelei@upf.br||biblio@upf.br |
| repository.name.fl_str_mv | Repositório Institucional da UPF - Universidade de Passo Fundo (UPF) |
| repository_id_str | 1610 |
| spelling | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus SertãoInteligência artificialMachine learningEstudantes do ensino médio - ComportamentoAlgoritmosCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOThis work aimed to predict the risk of losing a student's residence high school students, employing artificial intelligence, which was used in the area of Machine Learning, machine learning algorithms. The analysis is based in various data related to student residence, including information about the student, course, number of hours taught, measure, sector, number of days suspended, gender, grade and enrollment to generate a predictive system. The risk of losing student residence consists of the fact that the student can no longer remain as residing close to the campus, causing several difficulties, such as living outside the limits of the institution, having to pay rent, a reality not considered for many students from our institution. The act of remaining resident is a reflection of the The student's pattern of behavior is seen as the result of the model. Three Different machine learning algorithms were considered for identify and classify the parameters that affect permanence in the residence student: Naive-Bayes, KNN, Decision Tree. To evaluate the performance of machine learning algorithms, three metrics were used: accuracy, recall and F1-score. The results indicate that KNN outperformed other techniques, generating superior results, followed by Naive-Bayes. It is concluded that there is a way to predict the risk of loss of student residence based on a standard of student behavior. A web application was developed for the presentation of results. According to the experiment carried out, the approach proved to be adequate and can serve as decision-making in actions aimed at better relationship between Student Residence and students, in addition to improving students' social skills, necessary for socializing among peers.Neste trabalho objetivou-se prever o risco de perda da residência estudantil de alunos do ensino médio, empregando inteligência artificial, a qual utilizou na área de Machine Learning, os algoritmos de aprendizado de máquina. A análise baseia-se em diversos dados relacionados á residência estudantil, incluindo informações sobre o aluno, curso, número das horas orientadas, medida, setor, número de dias suspenso, sexo, série e matrícula para gerar um sistema preditivo. O risco de perder a residência estudantil consiste no fato de o aluno não poder permanecer mais como residente junto ao câmpus, ocasionando diversas dificuldades, como residir fora dos limites da instituição, ter que arcar com aluguel, realidade não contemplada para muitos alunos de nossa instituição. O ato de continuar residente é um reflexo do padrão de comportamento do aluno, é tido como o resultado do modelo. Três algoritmos de aprendizagem de máquina diferentes foram considerados para identificar e classificar os parâmetros que afetam a permanência na residência estudantil: Naive-Bayes, KNN, Árvore de Decisão. Para avaliar o desempenho dos algoritmos de aprendizagem de máquina, três métricas foram utilizadas: precisão, recall e F1-score. Os resultados indicam que o KNN superou as demais técnicas, gerando resultados superiores, seguido pelo Naive-Bayes. Se conclui que há como prever o risco de perda da residência estudantil a partir de um padrão de comportamento dos alunos. Uma aplicação web foi desenvolvida para a apresentação de resultados. Segundo o experimento realizado, a abordagem mostrou-se adequada e pode servir como tomada de decisão em ações que visem o melhor relacionamento entre a Residência Estudantil e os alunos, além de melhorar as habilidades sociais dos estudantes, necessárias para o convívio entre pares.Universidade de Passo FundoInstituto de Tecnologia – ITECBrasilUPFPrograma de Pós-Graduação em Computação AplicadaRabello, Roberto dos Santoshttp://lattes.cnpq.br/1386464075867571Pereira, Cedemir2025-05-07T12:44:28Z2024-08-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfPEREIRA, Cedemir. SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão. 2024. 81 f. Dissertação (Mestrado em Computação Aplicada) - Universidade de Passo Fundo, Passo Fundo, RS, 2024.https://repositorio.upf.br/handle/123456789/1738porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UPFinstname:Universidade de Passo Fundo (UPF)instacron:UPF2025-12-30T19:51:36Zoai:repositorio.upf.br:123456789/1738Repositório InstitucionalPRIhttp://repositorio.upf.br/oai/requestjucelei@upf.br||biblio@upf.bropendoar:16102025-12-30T19:51:36Repositório Institucional da UPF - Universidade de Passo Fundo (UPF) |
| spellingShingle | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão Pereira, Cedemir Inteligência artificial Machine learning Estudantes do ensino médio - Comportamento Algoritmos CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| status_str | publishedVersion |
| title | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| title_full | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| title_fullStr | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| title_full_unstemmed | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| title_short | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| title_sort | SYSDAE: usando Machine Learning numa análise do perfil comportamental dos alunos do ensino médio do IFRS - Campus Sertão |
| topic | Inteligência artificial Machine learning Estudantes do ensino médio - Comportamento Algoritmos CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| url | https://repositorio.upf.br/handle/123456789/1738 |