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Learning object repositories are software systems used to store and manage learning content and/or the metadata that describes it. Common metadata formats include standards such as Dublin Core (DC) and IEEE Learning Object Metadata (LOM). These metadata specifications provide data models that capture various characteristics (e.g., general, educational, technical) of a learning object. 

Metadata descriptions of learning objects can be gathered to create searchable catalogues of learning objects. Currently, looking up such metadata catalogues is the main way to search for learning objects, assess their usefulness, and retrieve them. Typically, metadata catalogues are stored in repositories that can be searched programmatically using a standard Application Programming Interface (API) such as the Simple Query Interface (SQI) or Search/Retrieve with URL (SRU). Very large catalogues can be created by harvesting (i.e., mirroring) metadata stored in repositories using protocols such as the Open Archives Initiative – Protocol for Metadata Harvesting (OAI-PMH). Metadata contained within these repositories can also be published into one of these centralized catalogues using protocols such as the Simple Publishing Interface (SPI).

Given the abundance of learning object references available in most metadata catalogues, user experiences should be further enhanced when search systems rely on social data (i.e., data obtained from end-users using web-2.0 techniques: tags, ratings, comments, bookmarks). This will improve precision and search results with rankings by perceived quality or usability of learning objects.  Search results can then be limited to learning objects previously assessed by other users and perceived as high quality and sorted according to these criteria.  This data can also be used to provide for social retrieval methods (e.g. recommendations and social navigation).


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