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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Attempts Prediction by Missing Data Imputation in Engineering Degree

Authors : 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

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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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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Metadata
Title
Attempts Prediction by Missing Data Imputation in Engineering Degree
Authors
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
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

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

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