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Title: Towards a learning analytics approach for supporting discovery and reuse of OER an approach based on Social Networks Analysis and Linked Open Data
Authors: Tovar, E.
Piedra Pullaguari, N.
Chicaiza Espinosa, J.
Lopez Vargas, J.
Keywords: architecture
Data Visualization
Learning Analytics
Linked Data
Social Networks Analysis 2017-06-16T22:02:40Z
Issue Date: 1-Jan-2015
Publisher: IEEE Global Engineering Education Conference, EDUCON
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 © 2015 IEEE.
metadata.dc.identifier.other: 10.1109/EDUCON.2015.7096092
ISBN: 21659559
ISSN: 9.78E+17
Other Identifiers: 10.1109/EDUCON.2015.7096092
Other Identifiers: 10.1109/EDUCON.2015.7096092
metadata.dc.type: Article
Appears in Collections:Artículos de revistas Científicas

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