The multi-model, metadata driven approach to personalised eLearning services

Abstract

One of the major obstacles in developing quality eLearning content is the substantial development costs involved and development time required [Marchionini, 95]. Educational providers, such as those in the university sector and corporate learning, are under increasing pressure to enhance the pedagogical quality and technical richness of their course offerings while at the same time achieving improved return on investment. One means of enhancing the educational impact of eLearning courses, while still optimising the return on investment, is to facilitate the personalisation and repurposing of learning objects across multiple related courses. However, eLearning courses typically differ strongly in ethos, learning goals and pedagogical approach whilst learners, even within the same course, may have different personal learning goals, motivations, prior knowledge and learning style preferences. This thesis proposes an innovative multi-model approach to the dynamic composition and delivery of personalised learning utilising reusable learning objects. The thesis describes a generic and extensible adaptive metadata driven engine that composes, at runtime, tailored educational experiences across a single educational content base. This thesis presents the theoretical models, design and implementation of an adaptive hypermedia educational service. It also describes how this multi-model, metadata driven approach, and the adaptive engine built in accordance with this approach, was trialled and evaluated from pedagogical, reusability and technical perspectives.