dentification of Factors Influencing Evasion in Undergraduate Courses Through Systems Based on Data Mining: A Quantitative Approach
Abstract
This paper uses data mining techniques to indentify key variables related with students failures in completing their undergraduate studies. In our approach, classification analysis is used to manipulate academic data of students of the largest Brazilian Federal University. Differently from other works, our research shows that even analyzing three different classes of students it was possible to have a global precision above 80%. The Naïve Bayes model was used to visualize the key variables used to separate the distinct classes of students.
Keywords:
Identification, Evasion, Data Mining
References
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Johnston V. (1997) “Why do first year students fail to progress to their second year? An academic staff perspective.” Department of Mathematics, Napier University. Paper presented at the British Educational Research Association Annual Conference. September 11-14: University of York.
Kotsiantis, S., Pierrakeas, C. e Pintelas, P., (2003) “Preventing student dropout in distance learning using machine learning techniques.” KES, eds. V. Palade, R. Howlett & L. Jain, Springer, volume 2774 of Lecture Notes in Computer Science, pp. 267–274
Minaei-Bidgoli, B., Tan, P., Kortemeyer G. e Punch, W.F. (2006) “Association analysis for a web-based educational system.” Livro Data Mining in E-Learning. WitPress. Southampton, Boston.
Moore, R. (1995) “Retention rates research project” final report, Sheffield Hallam University.
Saraiva, S. e Masson. M. (2003) “Evasão e Permanência em uma Instituição de Tradição: um estudo sobre o processo de evasão de estudantes em cursos de Engenharia na Escola Politécnica da UFRJ”, Relatório de Pesquisa.
Soares, I. S. (2006) “Evasão, retenção e orientação acadêmica: UFRJ – Engenharia de Produção – Estudo de Caso”. Anais do XXXIV Congresso Brasileiro de Ensino de Engenharia - COBENGE. Passo Fundo: Ed. Universidade de Passo Fundo.
Sumathi, S. and Sivanandam, S.N. (2006). “Introduction to Data Mining and its Applications”. Springer-Verlag Berlin Heidelberg 2006.
Wu, X., Kumar, V., Ross, Q.J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B.,Yu, P., Zhou, Z., Steinbach, M., Hand, D. and Steinberg, D. (2008) Top 10 algorithms in data mining. Journal of Knowledge and Information Systems. Springer London. page 1-37. vol. 14, Issue 1.
Baker, R., Isotani, S. e Carvalho, A. (2011) Mineração de Dados Educacionais: Oportunidades para o Brasil. Revista Brasileira de Informática na Educação, 19(2), 3-13. http://dx.doi.org/10.5753/RBIE.2011.19.02.03
Barroso, M. F. e Falcão, E. B. M. (2004) “Evasão Universitária: O Caso do Instituto de Física da UFRJ”, IX Encontro Nacional de Pesquisa em Ensino de Física.
Bouckaert R., Eibe F., Mark Hall, Kirkby, R., Reutemann, P., Seewald, A., and Scuse, D. (2010) “WEKA Manual for Version 3-6-4”. December.
Castro F. et al. (2007) “Applying Data Mining Techniques to e-Learning Problems, Studies in Computational Intelligence (SCI).” 62, 183 - 221 (2007) Springer-Verlag Berlin Heidelberg.
Davies, P. (1997) “Within our control?: Improving retention rates” in FE, FEDA.
Dekker G., Pechenizkiy M. and Vleeshouwers J. (2009) “Predicting Students Drop Out: A Case Study”. In Proceedings of the International Conference on Educational Data Mining, Cordoba, Spain, T. BARNES, M. DESMARAIS, C. ROMERO and S. VENTURA Eds., Pages 41-50.
Governo Federal (2007) “Diretrizes Gerais do Programa de Apoio a Planos de Reestruturação e Expansão das Universidades Federais – REUNI”, http://portal.mec.gov.br/sesu/arquivos/pdf/diretrizesreuni.pdf, Fevereiro.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten. I.H. (2009) “The WEKA Data Mining Software: An Update” SIGKDD Explorations, Volume 11, Issue 1.
Hämäläinen, W., Suhonen, J., Sutinen, E., and Toivonen, H. (2004) “Data mining in personalizing distance education courses”. In world conference on open learning and distance education, Hong Kong, pp. 1–11
Han, J. and Kamber, M. (2006), Data Mining Concepts and Techniques, Morgan Kauffmann Publishers, Second Edition.
INEP - Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (2009) “Investimentos Públicos em Educação”, http://portal.inep.gov.br/estatisticas-gastoseducacao e “Censo da Educação Superior’, http://portal.inep.gov.br, Outubro.
Johnston V. (1997) “Why do first year students fail to progress to their second year? An academic staff perspective.” Department of Mathematics, Napier University. Paper presented at the British Educational Research Association Annual Conference. September 11-14: University of York.
Kotsiantis, S., Pierrakeas, C. e Pintelas, P., (2003) “Preventing student dropout in distance learning using machine learning techniques.” KES, eds. V. Palade, R. Howlett & L. Jain, Springer, volume 2774 of Lecture Notes in Computer Science, pp. 267–274
Minaei-Bidgoli, B., Tan, P., Kortemeyer G. e Punch, W.F. (2006) “Association analysis for a web-based educational system.” Livro Data Mining in E-Learning. WitPress. Southampton, Boston.
Moore, R. (1995) “Retention rates research project” final report, Sheffield Hallam University.
Saraiva, S. e Masson. M. (2003) “Evasão e Permanência em uma Instituição de Tradição: um estudo sobre o processo de evasão de estudantes em cursos de Engenharia na Escola Politécnica da UFRJ”, Relatório de Pesquisa.
Soares, I. S. (2006) “Evasão, retenção e orientação acadêmica: UFRJ – Engenharia de Produção – Estudo de Caso”. Anais do XXXIV Congresso Brasileiro de Ensino de Engenharia - COBENGE. Passo Fundo: Ed. Universidade de Passo Fundo.
Sumathi, S. and Sivanandam, S.N. (2006). “Introduction to Data Mining and its Applications”. Springer-Verlag Berlin Heidelberg 2006.
Wu, X., Kumar, V., Ross, Q.J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B.,Yu, P., Zhou, Z., Steinbach, M., Hand, D. and Steinberg, D. (2008) Top 10 algorithms in data mining. Journal of Knowledge and Information Systems. Springer London. page 1-37. vol. 14, Issue 1.
Published
2012-05-16
How to Cite
MANHÃES, Laci M. Barbosa; DA CRUZ, Sérgio M. Serra; COSTA, Raimundo J. Macário; ZAVALETA, Jorge; ZIMBRÃO, Geraldo.
dentification of Factors Influencing Evasion in Undergraduate Courses Through Systems Based on Data Mining: A Quantitative Approach. In: BRAZILIAN SYMPOSIUM ON INFORMATION SYSTEMS (SBSI), 8. , 2012, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 284-295.
DOI: https://doi.org/10.5753/sbsi.2012.14413.
