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Data Mining in Academic Databases to Detect Behaviors of Students Related to School Dropout and Disapproval

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New Advances in Information Systems and Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 445))

Abstract

This work focuses on data mining in relational databases, aiming to detect behaviors related to school dropouts and disapproval by mapping the factors that influence this dropout. This work is relevant by the fact that the dropout and school disapproval are big factors of concern to all who care about education in Brasil. At the end of it, we intend to point out the need to implement solutions that enable access to results dynamically, thus allowing educators can early diagnose the causes of school dropout and disapproval, and allow for relevant pedagogical actions. This way, we intend to reduce the school dropout and school disapproval, towards a more efficient teaching and learning process at Brazilian federal education institution named Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte - IFRN.

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References

  1. Adachi, A.A.C.T, Evasão e evadidos nos cursos de graduação da Universidade Federal de Minas Gerais, Dissertação de Mestrado. Faculdade de Educação, UFMG (2009)

    Google Scholar 

  2. Baker, R. S. J., CARVALHO, A., M., J., ISOTANI, S., Mineração de Dados Educacionais: Oportunidades para o Brasil, Revista Brasileira de Informática na Educação, Volume 19, Número 2 (2011)

    Google Scholar 

  3. Brachaman, R. J.; Anand, T. Processo f Knowledge discovry in Database. KDD Process for Rxtracting Useful Knowledge from Volumes of Data (1996)

    Google Scholar 

  4. Date, C. J. Introdução aos sistemas de bancos de dados. Campus (2003)

    Google Scholar 

  5. Fayyad, U. M.; Piatetsky-Shapiro, G.; smyth, P. From Data Mining to Knowledge Discovery: An Overview. Knowledge Discovery and Data Mining, Menlo Park: AAAI Press (1996)

    Google Scholar 

  6. Favero, R. V. M., Dialogar ou evadir: Eis a questão!: Um estudo sobre a permanência e a evasão na Educação a Distância, no Estado do Rio Grande do Sul. CINTED-UFRGS. Novas Tecnologias na Educação, V. 4 Nº 2, Dezembro, Porto Alegre (2006)

    Google Scholar 

  7. Globo Comunicação e Participações. http://g1.globo.com/educacao/noticia/2013/11/mec-cria-grupo-para-estudar-evasao-escolar-nos-institutos-federais.html (2013)

  8. Goldschmidt, Ronaldo. Data Mining: Conceitos, técnicas, algoritmos, orientações e aplicações/ Ronaldo Goldschmidt, Eduardo Bezerra. 2.ed, Elsevier, Rio de Janeiro (2015)

    Google Scholar 

  9. Han, Jiawei; Kamber, Micheline. Data Mining: Concepts and Techniques. Second Edition. Elsevier. San Francisco, CA (2006)

    Google Scholar 

  10. Harinath, S.; Pihlgren, R.; Lee G., D.; Simon, J.; Bruckner, R. M. Professional Microsoft SQL Server 20012 Analysis Services with MDX and DAX. Wrox, (2012)

    Google Scholar 

  11. Karinny, Shintani, Leonardo Armond, Vassily Rolim. Dificuldades Escolares http://www.profala.com/ arteducesp19.htm (2014).

  12. Lilian L. Pereira. http://www.direcionaleducador.com.br/edicao-80-set/11/reprovacao-escolar-uma-questao-muitas-facetas (2011)

  13. Lobo, R. L., Hipólito, O. e Lobo, M.B., Estudo: Evasão no Ensino Superior: Causas e Remédios. Jornal Folha de S.Paulo. São Paulo (2007)

    Google Scholar 

  14. MEC/Setec. Documento Orientador para a Superação da Evasão e da Retenção na Rede Federal de Educação Profissional, Científica e Tecnológica (2014)

    Google Scholar 

  15. Piatetsky-Shapiro, G. e Data-Mining Industry Coming of Age. IEEE Inteligent System (1999)

    Google Scholar 

  16. Rainardi, V. Building a Data Warehouse: With Rxamples in SQL Server. Apress (2008)

    Google Scholar 

  17. Ramal, Adrea. Reprovar não é a solução, mas aprovar quem não aprendeu é pior. http://g1.globo.com/educacao/blog/andrea-ramal/post/reprovar-nao-e-solucao-mas-aprovar-quem-nao-aprendeu-e-pior.html (2014)

  18. BRASIL. Diplomação, Retenção e Evasão nos Cursos de Graduação em Instituições de Ensino Superior Públicas. Comissão Especial de Estudos sobre a Evasão nas Universidades Públicas Brasileiras. Brasília (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte (IFRN), Rua Dr. Nilo Bezerra Ramalho 1692, Natal, RN, 59015-300, Brazil

    José Antônio da Cunha & Elionai Moura

  2. Departamento de Informática, Universidade do Minho, Braga, Portugal

    Cesar Analide

Authors
  1. José Antônio da Cunha
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  2. Elionai Moura
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  3. Cesar Analide
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Corresponding author

Correspondence to José Antônio da Cunha .

Editor information

Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. ISEGI,, Universidade Nova de Lisboa, Lisboa, Portugal

    Ana Maria Correia

  3. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  4. DSI, Universidade do Minho, Guimarães, Portugal

    Luis Paulo Reis

  5. Rua Dom Manoel de Medeiros, s/n, DoisIrm, Universidade Federal Rural de Pernambuco, Recife, Brazil

    Marcelo Mendonça Teixeira

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© 2016 Springer International Publishing Switzerland

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da Cunha, J.A., Moura, E., Analide, C. (2016). Data Mining in Academic Databases to Detect Behaviors of Students Related to School Dropout and Disapproval. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-31307-8_19

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  • DOI: https://doi.org/10.1007/978-3-319-31307-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31306-1

  • Online ISBN: 978-3-319-31307-8

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