Abstract
E-learning involves the computer and network-enabled transfer of skills and knowledge. The recent years have witnessed an increased interest in intelligent E-Learning platforms that incorporate adaptive educational systems which enable the creation of personalized learning environments to suit the students' individual requirements and needs. Such systems aim to correlate the student characteristics (such as knowledge level, personality and learning style) with instructional variables, (such as the presentation of learning materials and feedback). Various artificial intelligence based methodologies have been used to realize adaptive educational systems. However, the vast majority of the existing adaptive educational systems do not learn from the users' behaviors to create white box models which could be easily read and analyzed by the lay user. This paper presents a fuzzy logic based system that can learn the users' preferred knowledge delivery based on the students characteristics to generate a personalized learning environment. The proposed methodology employs a self-learning system which enables to generate a fuzzy logic based model from data. The fuzzy model is generated from data representing various students' capabilities and their desired learning needs. The learnt fuzzy based model is then used to improve the knowledge delivery to the various students based on their individual characteristics. The proposed system is adaptive where it is continuously adapting in a lifelong learning mode to make sure that the generated models adapt to the students individual preferences. We will present experiments carried with the proposed system which involved 17 students. The experiments will show how the proposed system learnt the students' preferences and created a model which allowed providing a personalized learning environment tailored according to the students' needs and requirements. This allowed improving the knowledge delivery which resulted in improving the students' performance.