Comparative study of imputation algorithms applied to the prediction of student performance
| Autor: | Concepción Crespo-Turrado, Francisco Javier de Cos Juez, Emilio Corchado, Francisco Javier Pérez Castelo, José Luis Calvo-Rolle, Fernando Sánchez-Lasheras, José-Luis Casteleiro-Roca, José Antonio López-Vázquez |
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| Rok vydání: | 2019 |
| Předmět: |
Logic
business.industry Computer science 010401 analytical chemistry MARS 02 engineering and technology Mars Exploration Program Student performance Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences MICE Data imputation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Imputation (statistics) business AAA computer |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname Scopus RUO. Repositorio Institucional de la Universidad de Oviedo |
| ISSN: | 2014-5764 |
| DOI: | 10.1093/jigpal/jzz071 |
| Popis: | [Abstract]: Student performance and its evaluation remain a serious challenge for education systems. Frequently, the recording and processing of students’ scores in a specific curriculum have several f laws for various reasons. In this context, the absence of data from some of the student scores undermines the efficiency of any future analysis carried out in order to reach conclusions. When this is the case, missing data imputation algorithms are needed. These algorithms are capable of substituting, with a high level of accuracy, the missing data for predicted values. This research presents the hybridization of an algorithm previously proposed by the authors called adaptive assignation algorithm (AAA), with a well-known technique called multivariate imputation by chained equations (MICE). The results show how the suggested methodology outperforms both algorithms. Ministerio de Economía y Competitividad ; AYA2014-57648-P Asturias. Consejería de Economía y Empleo ; FC-15-GRUPIN14-017 |
| Databáze: | OpenAIRE |
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