Missing data imputation over academic records of electrical engineering students

ESTEBAN JOVE, PATRICIA BLANCO-RODRÍGUEZ, JOSÉ LUIS CASTELEIRO-ROCA, HÉCTOR QUINTIÁN, FRANCISCO JAVIER MORENO ARBOLEDA, JOSÉ ANTONIO LÓPEZ-VÁZQUEZ, BENIGNO ANTONIO RODRÍGUEZ-GÓMEZ, MARÍA DEL CARMEN MEIZOSO-LÓPEZ, ANDRÉS PIÑÓN-PAZOS, FRANCISCO JAVIER DE COS JUEZ, SUNG BAE CHO, JOSÉ LUIS CALVO-ROLLE

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing data. This work deals with the academic records of engineering students, in which imputation techniques are applied.More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm (AAA) based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms.

Original languageEnglish
Pages (from-to)487-501
Number of pages15
JournalLogic Journal of the IGPL
Volume28
Issue number4
DOIs
Publication statusPublished - 2020

Bibliographical note

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

Funding Information:
This work was also supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and grant funded by 2019 IT promotion fund (Development of AI based Precision Medicine Emergency System) of the Korean government (MSIT).

Publisher Copyright:
© 2020 Oxford University Press. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Philosophy

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