Frameworks for the Automatic Indexation of Learning Management Systems Content into Learning Object Repositories

Abstract

Learning Management Systems (LMS) generally offer big repositories of learning material, but the lack of publicly available metadata to describe the learning objects makes it very difficult to share and reuse these objects. If metadata could be generated for these objects in order to index them in Learning Object Repositories (LOR), the gained amount of resources could solve the sub-critical mass problem of most existing Learning Object Repositories. This work proposes two orthogonal frameworks that could facilitate the analysis, design and implementation of Automatic Indexers for LMS content. One of them focuses on the methodology needed to pass from a LMS to a LOR, while the other focuses on the technological aspect of the Automatic Metadata Generation. Prototype implementations of Automatic Indexing Systems for real LMSs (SIDWeb and Toledo-Blackboard) show that the amount of effort needed to construct them was small compared with the benefits that this kind of systems could have in educational technologies. While the metadata seems to be good enough at first sight, further research of the quality of automatic generated metadata is needed.