Towards Adaptive Learning Systems Based on Fuzzy-Logic

E-learning systems have the ability to facilitate the interaction between learners and teachers without being limited by temporal and/or spatial constraints. However, the high number of students at universities, the huge number of available learning in the web, the differences between learners in term of characteristics and needs make the traditional e-learning systems more limited. For this purpose, adaptive learning has been recently explored in order to cope with these limitations and to meet the individual needs of learner. In this context, many artificial intelligence methods and approaches have been integrated in such computer-based systems in order to create effective learner models, structured domain models, adaptive learning paths, personalized learning format, etc. Such methods are highly recommended for designing adaptive e-learning and m-learning systems with good quality. In this paper, we focus only on one of these methods, called fuzzy logic, which is widely used in educational area. We present the integration of fuzzy logic as a valuable approach that has the ability to deal with the high level of uncertainties and imprecision related to learners’ characteristics and learning contexts.