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Título:
Student Performance Prediction Applying Missing Data Imputation in Electrical Engineering Studies Degree.
Student Performance Prediction Applying Missing Data Imputation in Electrical Engineering Studies Degree.
Autores:
Crespo Turrado, Concepción; Casteleiro Roca, José L.; Sánchez Lasheras, Fernando; López Vázquez, José A.; de Cos Juez, Francisco J.; Calvo Rolle, José L.; Corchado Rodríguez, Emilio
Libro:
Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science. Volumen 9648, pp. 126 - 135.
Fecha de Publicación:
14 April 2016
ISSN:
0302-9743
ISBN:
978-3-319-32033-5
Nowadays the student performance and its evaluation is a challenge in general terms. Frequently, the students’ scores of a specific curriculum have several fails due to different reasons. In this context, the lack of data of any of student scores adversely affects any future analysis to be done for achieving conclusions. When this occurs, a data imputation process must be performed in order to substitute the data that is missing 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 other technique called Multivariate Imputation by Chained Equations (MICE). The results obtained demonstrate that the proposed methods allow good results, specially the AAA algorithm