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Expert Systems
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The improvement of analytics in massive open online courses by applying data mining techniques

Authors: Alejandro Maté; Elisa de Gregorio; José Cámara; Juan Trujillo 0001; Sergio Luján-Mora;

The improvement of analytics in massive open online courses by applying data mining techniques

Abstract

AbstractThe 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.

Country
Spain
Related Organizations
Keywords

Analytics, Text mining, Lenguajes y Sistemas Informáticos, MOOC, Business intelligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
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