A Framework for Adaptive e-Learning

Adaptive learning systems attempt to adapt learning content to suit the needs of the learners using the system. Most adaptive techniques, however, are con-strained by the pedagogical preference of the author of the system and are always constrained to the system they were developed for and the domain content. This thesis presents a novel method for content adaptation. A personal profile is de-scribed that can be used to automatically generate instructional content to suit the pedagogical preference and cognitive ability of a learner in real time. This the-sis discusses the manifestation of measurable cognitive traits in an online learning environment and identifies cognitive resources, within instructional content, that can be used to stimulate these manifestations. There exists two main components for the learning component: Content Anal-yser and a Selection Model. The Content Analyser is used to automatically gen-erate metadata to encapsulate cognitive resources within instructional content. The analyser is designed to bridge the perceived gap found within instructiona