Enhancing Learning Objects with an Ontology-Based Memory

Abstract

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e-Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating "content metadatardquo through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments.