The improvement of analytics in massive open online courses by applying data mining techniques
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http://hdl.handle.net/10045/62278
Título: | The improvement of analytics in massive open online courses by applying data mining techniques |
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Autor/es: | Maté, Alejandro | Gregorio Medrano, Elisa de | Cámara, José | Trujillo, Juan | Luján-Mora, Sergio |
Grupo/s de investigación o GITE: | Lucentia |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Business intelligence | Analytics | MOOC | Text mining |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | ago-2016 |
Editor: | Wiley |
Cita bibliográfica: | Expert Systems. 2016, 33(4): 374-382. doi:10.1111/exsy.12119 |
Resumen: | The continuous increase in the number of open online courses has radically changed the traditional sector of education during the last years. These new learning approaches are very difficult to manage by using traditional management methods. This is one of the challenges in order to improve the new massive open online courses. In this paper, we propose a big data modelling approach, considering information from a big data analysis perspective, finding out which are the most relevant indicators in order to guarantee the success of the course. This novel approach is described along the paper using the case study of an open online course offered at our university. We describe the lessons learned in this work with the objective of providing general tools and indicators for other online courses. This will enhance the analysis and management of this kind of courses, contributing to their success. |
Patrocinador/es: | This work has partially funded by the project GEODAS-BI (TIN2012-37493-C03-03) from the Ministry of Economy and Competitiveness (MINECO). Alejandro Maté is funded by a Vali+D grant (APOSTD/2014/064). |
URI: | http://hdl.handle.net/10045/62278 |
ISSN: | 0266-4720 (Print) | 1468-0394 (Online) |
DOI: | 10.1111/exsy.12119 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2015 Wiley Publishing Ltd |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1111/exsy.12119 |
Aparece en las colecciones: | INV - LUCENTIA - Artículos de Revistas INV - ALISoft - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2016_Mate_etal_ExpertSystems_final.pdf | Versión final (acceso restringido) | 2,49 MB | Adobe PDF | Abrir Solicitar una copia |
2016_Mate_etal_ExpertSystems_revised.pdf | Versión revisada (acceso abierto) | 2,17 MB | Adobe PDF | Abrir Vista previa |
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