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Learning management systems (LMS), such as WebCT, Blackboard, and Moodle, are commonly employed in modern educational settings. However, while these applications have proven their worth, they do not typically take the individual needs of students into consideration, especially those with learning difficulties. Furthermore, research into the value adaptive e-learning systems can offer students with learning difficulties is lacking. \ud \ud This thesis aims to address gaps in existing research by examining the use of an adaptive e-learning system that can adapt to the needs of students with learning disabilities (LD). The research conducted in this thesis focused on dyslexia because there are various forms of dyslexia and, as such, it cannot be categorised as a single condition that can be served by one specific e-learning system.\ud \ud Following an extensive literature review, three models by which the characteristics of students with dyslexia could be taken into consideration within an adaptive LMS were formulated: a consensus-based model of dyslexia signs and symptoms, which includes consideration of reading, writing, memory, mathematics and speaking difficulties; a comprehensive listing of the existing assistive technologies that can be employed to assist dyslexic students in their educational process mapped according to dyslexia symptoms; and a learning styles model that was based on the Felder-Silverman learning style model that establish relationships between dyslexia type and learning styles.\ud \ud The three models were subsequently incorporated into the Dyslexia Adaptive E-Learning (DAEL) Framework, which specified the design requirements by which an e-learning technology that can facilitate dyslexic student’s learning can be developed. The proposed framework was validated using a quality assessment approach.\ud \ud This thesis proposes an innovative semantic approach to dynamically generate personalised learning materials in the form of the Adaptive E-Learning Management System (DAELMS). The DAELMS is an ontology-based engine that composes and adapts learning experiences according to learner’s dyslexia type and learning styles. This novel approach aims to improve flexibility, extensibility and reusability of systems, while offering a pedagogically effective and satisfactory learning experience for learners with dyslexia. The evaluation of the system revealed that the proposed concept successfully supported students learning in general and students with dyslexia specifically. It is anticipated that this research will pave the way for the development of advanced adaptive learning systems that can support the needs of multiple learners and learning disability types.