Generation of an adaptive e-learning domain model based on a fuzzy logic approach

The domain model is one of the important components used by adaptive e-learning systems to generate customized courses for the learners. The domain model acts as a data repository that consists of topics, contents, pages or nodes, and navigation links related to the design structure of the represented data. The most important aspect of the domain model is the relations between the course elements in other word the navigation links between pages which are used to decide upon adaptations. In this paper we propose a generation of an adaptive e-learning domain model based on fuzzy logic approach. Our approach uses a predefined domain model provided by expert of a specific field. However to allow the experts to find the best domain model in order to adapt the learners' differences, we consider that the prerequisite relations between the concepts in predefined domain model are fuzzy relations, and they could be established with a certain percentage of uncertainty fixed by the experts. Indeed, we generate many intermediate domain models using various combinations of four parameters of fuzzy logic, then we use the percentage of uncertainty to calculate similarity index between intermediate domain models and predefined domain model by expert, finally we provide the final domain model.