An Enhanced LTSA Model Providing Contextual Knowledge for Intelligent e-Learning Systems *

Learning objects along with their sequencing are being studied to support efficient e-learning systems. They could solve the problem of costly reproduction of learning materials for e-learning systems. The problem arising here is related to the size and complexity of methods used to achieve the appropriate composition of learning objects in order to generate courses with pedagogic efficiency and value. We concentrate our attention on the adaptive and intelligent composition of learning objects. It is the main theme of instructional design, a major concern of which is the representation and organization of subject contents to facilitate the learning process. We believe that modeling the structure of subject contents, i.e., contextual knowledge, in an e-learning system can help an instructional designer to design and implement an adaptive and intelligent sequence of learning. A metadata-based ontology is introduced for this purpose and added to the IEEE LTSA model. Further, UML is used to design of an ontology-based educational contents model based on IMS specifications. In this way, the proposed solution provides a complete solution for the design and development of efficient e-learning systems