With the rapid growth of e-Learning, a tremendous amount of learning content has been developed by numerous providers. Recently, the Sharable Content Object Reference Model (SCORM) has been widely accepted as a standard of e-Learning for users to share and reuse various teaching materials. Data grids, characterized by their goal to manage large-scale dataset, are promising platforms to support sharing of geographically dispersed learning content. However, current data grid standards have not provided complete solutions to content-based information retrieval. To increase the precision of content retrieval oil data grids, our idea is to propose an ontology-based approach to organize and retrieve learning content in geographically dispersed repositories. We designed a layered architecture to enable learning content organization and retrieval on data grids, implemented in a metropolitan-scale grid environment. Experimental results show that the proposed approach can precisely retrieve SCORM-compliant learning content. (c) 2009 Elsevier B.V. All rights reserved.
Relation:
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING-THEORY METHODS AND APPLICATIONS 25 (6): 687-694
