Abstract:

Adaptive learning allows learners to choose their modular components to customize their learning-centered learning environments. It offers flexible solutions that dynamically adapt content to the real-time learning needs of individuals. While, adaptive learning systems and adaptive learning content according to the needs of learners use the system. Most adaptive techniques are constraints by the pedagogical preference of the author of the system and are always constrained to the system for the people who have been developed and to the content of the domain.

Research question:
• This research is involved with the following research question: Is it possible to construct an automated learning component that generates instructional content suited to the cognitive ability and pedagogical preference of a learner?
Research goal:
• The goal of this research is to describe a suitable personal profile, consisting of the cognitive ability and pedagogical preference of a learner that has associated cognitive metrics found within instructional content. This thesis discusses the design, construction and evaluation of an automated learning component that is built to automatically generate instructional content using an evolutionary strategy, suited to the defined optimal personal profile.
• This paper presents a new method for adapting content in MOOC. A personal profile can be used to automatically generate pedagogical content according to the learning preference and the cognitive ability of a learner in real time. This research deals the manifestation of measurable cognitive traits in MOOC and identifies the cognitive resources within the pedagogical content that can be used to stimulate these manifestations.

Methodology:
• There exists two main components for the learning component: Content Analyser and a Selection Model. The Content Analyser is used to automatically generate metadata to encapsulate cognitive resources within instructional content. The analyser is designed to bridge the perceived gap found within instructional repositories between inconsistent metadata created for instructional content and multiple metadata standards being used. All instructional content that is analysed is repackaged as Sharable Content Object Reference Model (SCORM) conforming content.
• The Selection Model uses an evolutionary algorithm to evolve instructional content to a Minimum Expected Learning Experience to suit the cognitive ability and pedagogical preference of a learner. The Minimum is an approximation to the expected exam result of a learner after a learning experience has taken place. Additionally the research investigates the correlation between the cognitive ability and pedagogic preference of an author of instructional content and the cognitive resources used to generate instructional content. Furthermore the effectiveness of the learning component is investigated by analysing the learners increase in performance using the learning component against a typical classroom environment.

Keywords:

e-learning, adaptive learning, MOOC.