Attempts Prediction by Missing Data Imputation in Engineering Degree

  • Esteban Jove
  • Patricia Blanco-Rodríguez
  • José Luis Casteleiro-Roca
  • Javier Moreno-Arboleda
  • José Antonio López-Vázquez
  • Francisco Javier de Cos Juez
  • José Luis Calvo-Rolle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Student performance Data imputation MARS MICE AAA 

Notes

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.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Esteban Jove
    • 1
  • Patricia Blanco-Rodríguez
    • 2
  • José Luis Casteleiro-Roca
    • 1
  • Javier Moreno-Arboleda
    • 3
  • José Antonio López-Vázquez
    • 1
  • Francisco Javier de Cos Juez
    • 4
  • José Luis Calvo-Rolle
    • 1
  1. 1.Department of Industrial EngineeringUniversity of A CoruñaFerrolSpain
  2. 2.Department of Construction and Manufacturing EngineeringUniversity of OviedoGijónSpain
  3. 3.Faculty of MinesUniversity of ColombiaBogotColombia
  4. 4.Department of Mining ExploitationUniversity of OviedoOviedoSpain

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