A Topic Map-based System for Identifying Relevant Learning Objects

We propose a Topic Map based system to facilitate study at various levels. The objectives of this system are to help the learner recognize the structure of relevant knowledge, and to locate relevant learning objects (LOs) that help in overcoming conceptual difficulties at any given step. This system would use a graphical interface for the learner as a front end with Topic Map technology at the back. A new form of Learning Object Metadata is proposed, to include machine processable description of content, going well beyond keywords. We call such metadata as “Content Metadata in the Topic Map Format” (CMTMF). This metadata will be a Topic Map fragment, each in the form of a triple showing two topics and the association between them. CMTMF queries, again in the form of triples will be matched with the CMTMF contained in potentially relevant LOs identified by a search engine.