Towards a learning analytics approach for supporting discovery and reuse of OER: an approach based on Social Networks Analysis and Linked Open Data

Piedra Pullaguari, Nelson Oswaldo and Chicaiza Espinosa, Janneth Alexandra and López Vargas, Jorge and Tovar Caro, Edmundo (2015). Towards a learning analytics approach for supporting discovery and reuse of OER: an approach based on Social Networks Analysis and Linked Open Data. In: "2015 IEEE Global Engineering Education Conference (EDUCON)", 18-20 Mar 2015, Tallinn, Estonia. ISBN 978-1-4799-1908-6. pp. 978-988. https://doi.org/10.1109/EDUCON.2015.7096092.

Description

Title: Towards a learning analytics approach for supporting discovery and reuse of OER: an approach based on Social Networks Analysis and Linked Open Data
Author/s:
  • Piedra Pullaguari, Nelson Oswaldo
  • Chicaiza Espinosa, Janneth Alexandra
  • López Vargas, Jorge
  • Tovar Caro, Edmundo
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2015 IEEE Global Engineering Education Conference (EDUCON)
Event Dates: 18-20 Mar 2015
Event Location: Tallinn, Estonia
Title of Book: Proceedings of 2015 IEEE Global Engineering Education Conference (EDUCON)
Date: 2015
ISBN: 978-1-4799-1908-6
Volume: 1
Subjects:
Freetext Keywords: OER; Linked data; SNA; Social Networks Analysis; Learning analytics; Data visualization; Architecture
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The OER movement poses challenges inherent to discovering and reuse digital educational materials from highly heterogeneous and distributed digital repositories. Search engines on today?s Web of documents are based on keyword queries. Search engines don?t provide a sufficiently comprehensive solution to answer a query that permits personalization of open educational materials. To find OER on the Web today, users must first be well informed of which OER repositories potentially contain the data they want and what data model describes these datasets, before using this information to create structured queries. Learning analytics requires not only to retrieve the useful information and knowledge about educational resources, learning processes and relations among learning agents, but also to transform the data gathered in actionable e interoperable information. Linked Data is considered as one of the most effective alternatives for creating global shared information spaces, it has become an interesting approach for discovering and enriching open educational resources data, as well as achieving semantic interoperability and re-use between multiple OER repositories. In this work, an approach based on Semantic Web technologies, the Linked Data guidelines, and Social Network Analysis methods are proposed as a fundamental way to describing, analyzing and visualizing knowledge sharing on OER initiatives.

Funding Projects

TypeCodeAcronymLeaderTitle
Madrid Regional GovernmentS2013/ICE-2715UnspecifiedUnspecifiedUnspecified

More information

Item ID: 45031
DC Identifier: http://oa.upm.es/45031/
OAI Identifier: oai:oa.upm.es:45031
DOI: 10.1109/EDUCON.2015.7096092
Official URL: http://ieeexplore.ieee.org/document/7096092/
Deposited by: Memoria Investigacion
Deposited on: 27 Mar 2017 14:52
Last Modified: 27 Mar 2017 14:52
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