Ontology-based Learning Object Searching Technique with Granular Feature Extraction

The development of e-learning cannot be separated from the creation of learning objects which are always improving and more diverse. In this study, we propose a technique to perform learning object searching that use a granular learning object concept using ontology representation. The concept of granular learning objects is done by separating the materials with their sub-materials. A semantic web-based search engine is also used to explore existing learning objects. The result of the research shows that the implementation of granular leaning object ontology, and semantic web-based search engine, can improve searching performance by 29,7% better than search engine which uses document index-based searching technique. The search results do not only show the general description of a learning object, but can also show the sub-materials and the relationships among those materials. An information in the form of pictures and their descriptions about the retrieved learning object can also be displayed.