Using Linked Data as a basis for a Learning Resource Recommendation System

Shabir, N. and Clarke, C. (2009) Using Linked Data as a basis for a Learning Resource Recommendation System. In: 1st International Workshop on Semantic Web Applications for Learning and Teaching Support in Higher Education (SemHE'09), 30 September 2009, ECTEL'09, Nice, France.

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Abstract

Resource List Management Systems (RLMS) allow the electronic publication of course reading lists. Aside from electronic access, existing systems in this area provide little utility for teachers and learners above and beyond the traditional paper based reading lists. Our vision is that resource lists could in actual fact become Open Educational Resources that can be shared, re-mixed and re-used across institutions and borders. This paper introduces how we used linked data to architect a RLMS to meet this vision. However, in implementing this system, questions arose around the provenance, sustainability, licensing and reliability of today's linked data cloud. This paper documents the steps we took to address these critisms in our implementation. The paper goes on to discuss how the ecosystem of learning data managed by this application opens the way for future work, which involves leveraging typed relationships between learning goals, educational resources and system actors to provide recommendation-like services for academics creating new content.

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