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dc.contributor.authorAbeywardena, Ishan S
dc.contributor.authorTham, Choy Y
dc.contributor.authorChan, Chee S
dc.contributor.authorBalaji, Venkataraman
dc.date.accessioned2015-02-06T09:48:34Z
dc.date.available2015-02-06T09:48:34Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11599/225
dc.description.abstractThe Open Educational Resources (OER) movement has gained momentum in the past few years. With this new drive towards making knowledge open and accessible, a large number of OER repositories have been established and made available online throughout the globe. However, despite the fact that these repositories hold a large number of high quality material, the use and re-use of OER has not taken off as anticipated due to various geographic, socio and technological limitations. One such technological limitation is the present day inability to effectively search and locate OER materials which are specific and relevant to a particular academic domain. As a first step towards a possible solution to this issue, this research paper discusses the design and development of a clustering algorithm which accurately clusters text based OER materials by building a Keyword-Document Matrix (KDM) using autonomously identified domain specific keywords. This algorithm is the first phase of a larger technology framework named “OERScout” which is a potentially new methodology for effectively searching and locating desirable OER for academic use.en_US
dc.language.isoenen_US
dc.publisherAsian Association of Open Universitiesen_US
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0en_US
dc.subjectOpen Educational Resources (OER)en_US
dc.titleOERScout: Autonomous Clustering of Open Educational Resourcesen_US
dc.typeReporten_US
dc.subject.otherSearch Enginesen_US
col.proceedings.conf26th Asian Association of Open Universities (AAOU) Annula Conferenceen_US
col.proceedings.confdate16-18 Octoberen_US
col.proceedings.confplaceChiba, Japanen_US


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