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Title: Recommendation of OERs shared in social media based-on social networks analysis approach
Authors: Tovar, E.
Piedra Pullaguari, N.
Chicaiza Espinosa, J.
Lopez Vargas, J.
Keywords: Discovery
URLs 2014-06-06
Issue Date: 1-Jan-2015
Publisher: Proceedings - Frontiers in Education Conference, FIE
Abstract: The access to information is essential to learning as much as instruction. The evolution of the Web, from Web 1.0, where we were consumers of information, to a Web 2.0 where now we are producers and consumers of information, has allowed the Web becomes a huge database and in constant expansion. In these days much of the information published on the Web is published on social media, represented through social networks such as Facebook, Twitter, to name only the most prominent. Each of the media and social networks has its own scheme of operation and different working characteristics, ranging from the length of text that can be used, the use of different forms to identify topics until reaching the reciprocity of relationship between the participants. For example Twitter is a social network where millions of daily messages called Tweets are exchanged, within the message can be used labels, called hashtags, to identify the subject of the message, the message also may include links to other resources that expand the original content or showing interesting information and the relationships between users are represented as non reciprocal relationships named "Following". The extraction of information posted on social networks is solved in this research through the use of linked data, that allow retrieving resources and link with other external sources, graphs databases that help represent the working scheme of a social network, and with social network analysis (SNA), technique to discover relevant information that goes beyond the individual properties. The scope of this paper is to use information that is published on Twitter to extract and recommend Open Educational Resources in order to help with the learning process. The results obtained are a set of recommendations on users (identified as experts) and virtual communities (lists of Twitter users) and related events, according to the learning needs described as tags.
metadata.dc.identifier.other: 10.1109/FIE.2014.7044454
ISBN: 15394565
ISSN: 978-1-4799-3921-3
Other Identifiers: 10.1109/FIE.2014.7044454
Other Identifiers: 10.1109/FIE.2014.7044454
metadata.dc.language: Inglés
metadata.dc.type: Article
Appears in Collections:Artículos de revistas Científicas

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