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Educational Data Mining: An Application of Regressors in Predicting School Dropout

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10935))

Abstract

School dropout is one of the great challenges for the educational system. Educational data mining seeks to study and contribute with results that aim to hidden problems and find possible solutions. Considering its importance, this work aims to use two nonparametric techniques, Quantile Regression and Support Vector Regression, to predict the results of school dropout in the Brazilian scenario. The development of the work followed the phases of CRISP-DM. The evaluation metric of the models is the mean of the absolute error. The results show more significant results for Support Vector Regression.

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Authors and Affiliations

  1. University of Pernambuco, Recife, Pernambuco, Brazil

    Rafaella Leandra Souza do Nascimento, Ricardo Batista das Neves Junior, Manoel Alves de Almeida Neto & Roberta Andrade de Araújo Fagundes

Authors
  1. Rafaella Leandra Souza do Nascimento
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  2. Ricardo Batista das Neves Junior
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  3. Manoel Alves de Almeida Neto
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  4. Roberta Andrade de Araújo Fagundes
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Correspondence to Rafaella Leandra Souza do Nascimento .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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do Nascimento, R.L.S., das Neves Junior, R.B., de Almeida Neto, M.A., de Araújo Fagundes, R.A. (2018). Educational Data Mining: An Application of Regressors in Predicting School Dropout. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-96133-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96132-3

  • Online ISBN: 978-3-319-96133-0

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