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Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX

Ruipérez-Valiente, José A. and Muñoz-Merino, Pedro J. and Gascón-Pinedo, José A. and Delgado Kloos, Carlos (2017) Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX. [Journal Articles] (Accepted for publication)

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Abstract

The emergence of massive open online courses (MOOCs) has caused a major impact on online education. However, learning analytics support for MOOCs still needs to improve to fulfill requirements of instructors and students. In addition, MOOCs pose challenges for learning analytics tools due to the number of learners, such as scalability in terms of computing time and visualizations. In this work, we present different visualizations of our “Add-on of the learNing AnaLYtics Support for open Edx” (ANALYSE), which is a learning analytics tool that we have designed and implemented for Open edX, based on MOOC features, teacher feedback, and pedagogical foundations. In addition, we provide a technical solution that addresses scalability at two levels: first, in terms of performance scalability, where we propose an architecture for handling massive amounts of data within educational settings; and, second, regarding the representation of visualizations under massiveness conditions, as well as advice on color usage and plot types. Finally, we provide some examples on how to use these visualizations to evaluate student performance and detect problems in resources.

Item Type: Journal Articles
Uncontrolled Keywords: open edX, Human–machine system, information visualization, learning analytics, massive open online courses (MOOCs)
Subjects: UNSPECIFIED
Divisions: UNSPECIFIED
Depositing User: Jose Antonio Ruiperez
Date Deposited: 25 Jan 2017 15:45
Last Modified: 14 Feb 2017 08:21
URI: http://eprints.networks.imdea.org/id/eprint/1518

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