Feeding back learning resources repurposing patterns into the “information loop”: opportunities and challenges

The paper outlines a model for framing the representation and treatment of information gathered from the reuse and repurposing of learning resources from distributed repositories. The model takes into account as sources of information both static user-edited or automatically generated metadata fields and the emerging, dynamic information clouds that surrounds a learning resource when users comment on it, tags it, or explicitly links it to other learning resources. By coordinating these separate information layers, the advantages that can be achieved are reducing the semantic gap occurring when unanticipated contexts of use are to be described by resorting only to predefined vocabularies; and improvements in the relevance of the retrieved resources after a query. To achieve this “coordination” it is proposed that the textual descriptions of the repurposing activity with respect to the intended learning outcomes and pedagogical strategies are fed to a dynamic unsupervised classification method that operates on the above mentioned information spaces, and that supports exploratory search by suggesting associations. It is argued that the proposed analogical retrieval, as opposed to standard query matching, is more fit to tracking the loci of innovation and sustaining the formation of best practices in the community.