Maycock, Keith (2010) A Framework for Adaptive e-Learning. PhD thesis, National University of Ireland Maynooth.

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Abstract

Adaptive learning systems attempt to adapt learning content to suit the needs of the learners using the system. Most adaptive techniques, however, are constrained 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 described that can be used to automatically generate instructional content to suit the pedagogical preference and cognitive ability of a learner in real time. This thesis 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 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 (MELE) to suit the cognitive ability and pedagogical preference of a learner. The MELE is an approximation to the expected exam result of a learner after a learning experience has taken place. Additionally the thesis 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.

Item Type:	Thesis (PhD)
Keywords:	Adaptive e-Learning;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	2571
Depositing User:	IR eTheses
Date Deposited:	16 Jun 2011 13:54
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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