A Business Intelligence Framework for Analyzing Educational Data
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http://hdl.handle.net/10045/108094
Título: | A Business Intelligence Framework for Analyzing Educational Data |
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Autor/es: | Villegas-CH, William | Palacios-Pacheco, Xavier | Luján-Mora, Sergio |
Grupo/s de investigación o GITE: | Advanced deveLopment and empIrical research on Software (ALISoft) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Business intelligence (BI) | Educational data mining (EDM) | Learning management systems (LMS) | Learning analytics |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 17-jul-2020 |
Editor: | MDPI |
Cita bibliográfica: | Villegas-Ch W, Palacios-Pacheco X, Luján-Mora S. A Business Intelligence Framework for Analyzing Educational Data. Sustainability. 2020; 12(14):5745. doi:10.3390/su12145745 |
Resumen: | Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university. |
URI: | http://hdl.handle.net/10045/108094 |
ISSN: | 2071-1050 |
DOI: | 10.3390/su12145745 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/su12145745 |
Aparece en las colecciones: | INV - ALISoft - Artículos de Revistas |
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Archivo | Descripción | Tamaño | Formato | |
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Villegas-CH_etal_2020_Sustainability.pdf | 2,26 MB | Adobe PDF | Abrir Vista previa | |
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