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International Workshop on Soft Computing Models in Industrial and Environmental Applications

International Conference on EUropean Transnational Education

Computational Intelligence in Security for Information Systems Conference

SOCO 2017, ICEUTE 2017, CISIS 2017: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding pp 167–176Cite as

Attempts Prediction by Missing Data Imputation in Engineering Degree

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 649))

Abstract

Nowadays, both students performance and its evaluation are important challenges and play a significant role, in general terms. Frequently, the students attempts to pass a specific curriculum subjects, have several fails due to different reasons and, in this context, lack of data adversely affects interesting future analysis for achieving conclusions. As a consequence, data imputation processes must be performed in order to substitute the missing data for estimated values. This paper presents a comparison between two data imputation methods developed by the authors in previous researches, the Adaptive Assignation Algorithm (AAA) based on Multivariate Adaptive Regression Splines (MARS), and the Multivariate Imputation by Chained Equations methodology (MICE). The results obtained demonstrate that both proposed methods achieve good results, specially AAA algorithm.

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Acknowledgments

Authors greatly appreciate the support both from Spanish Ministry of Economy and Competitivenes through grant AYA2014-57648-P, and from regional Ministry of Economy and Employment through grant FC-15-GRUPIN14-017.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, Avda. 19 de febrero s/n, 15495, Ferrol, A Coruña, Spain

    Esteban Jove, José Luis Casteleiro-Roca, José Antonio López-Vázquez & José Luis Calvo-Rolle

  2. Department of Construction and Manufacturing Engineering, University of Oviedo, Campus de Viesques, 33204, Gijón, Spain

    Patricia Blanco-Rodríguez

  3. Faculty of Mines, University of Colombia, Cra 45, Bogot, Colombia

    Javier Moreno-Arboleda

  4. Department of Mining Exploitation, University of Oviedo, Calle San Francisco, 1, 33004, Oviedo, Spain

    Francisco Javier de Cos Juez

Authors
  1. Esteban Jove
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  2. Patricia Blanco-Rodríguez
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  3. José Luis Casteleiro-Roca
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  4. Javier Moreno-Arboleda
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  5. José Antonio López-Vázquez
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  6. Francisco Javier de Cos Juez
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  7. José Luis Calvo-Rolle
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Corresponding author

Correspondence to Esteban Jove .

Editor information

Editors and Affiliations

  1. University of Leon, Leon, Spain

    Hilde Pérez García

  2. University of Leon, Leon, Spain

    Javier Alfonso-Cendón

  3. University of Leon, Leon, Spain

    Lidia Sánchez González

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Jove, E. et al. (2018). Attempts Prediction by Missing Data Imputation in Engineering Degree. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-67180-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67179-6

  • Online ISBN: 978-3-319-67180-2

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