OERScout: Autonomous Clustering of Open Educational Resources

Abstract

The 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.