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Domain Categorization of Open Educational Resources Based on Linked Data

2014; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-11716-4_2

1865-0937

Janneth Chicaiza, Nelson Piedra, Jorge López, Edmundo Tovar,

Biomedical Text Mining and Ontologies

One of the main objectives of open knowledge, and specifically of Open Educational Resource movement, is to allow people to access the resources they need for learning. The first step to that a learner starts this process is to find information and resources according to his/her needs. One of the reasons why OERs could stay hidden and therefore to be underutilized is that each institution and producer of this kind of resources, labels them using tags or informal and heterogeneous knowledge schemes. This issue was identified in the Open Education Consortium (until recently called OpenCourseWare Consortium) study, where respondents noted that one way to improve the courses is to make a "major better categorization of courses according to subject areas". In previous works, the authors present the Linked OpenCourseWare Data project, which published metadata of courses coming from different open educational datasets. So far there are over 7000 indexed courses associated to 626 topic names or knowledge fields, however, appear different names meaning similar areas or they are written in different languages and also correspond to different detail level. The semantic lack in the relations between areas and subjects make it difficult to find associations between topics and to list recommendations about resources for learners. In this work, authors present a process to support semi-automatic classification of Open Educational Resources, taking advantage from linked data available in the Web through systems made by people who can converge to a formal knowledge organization system.