Abstract. Personalised elearning is being heralded as one of the grand challenges of next generation learning systems, in particular, its ability to support greater effectiveness, efficiency and student empowerment. However, a key problem with such systems is their reliance on bespoke content developed for, and only used by, these systems. The challenge for adaptive systems in scalably supporting personalised elearning is its ability to source, harvest and deliver open corpus content to adaptive content services and personalised elearning systems. This paper examines the issues involved in implementing such an adaptive content service. The paper seeks to explore the accurate extraction of content requirements from the adaptive system, the sourcing and identification of suitable learning content, the harvesting and customisation of the content for delivery to adaptive elearning systems.
MotivationeLearning environments are attempting to respond to the demand for personalised ondemand distance/on-line learning by providing increasing support for such functionality as personalisation, adaptivity and on-demand learning object generation [1]. Adaptive Hypermedia is seen as one of the key areas of delivering personalised "just-for-you" eLearning. The benefit of such learning is that it can be dynamically tailored to the individual's experience, goals, preferences etc. This empowers the learner as the learning experience and activities are more suited to that individual.One of the most significant problems with personalised eLearning systems (PeLS) is that they are traditionally restricted to using bespoke proprietary content. This is particularly the case with Intelligent Tutoring Systems (ITS) where, in fact, the personalisation is embedded in the content itself [2]. However in the second generation of adaptive systems the sequencing for adaptivity has been separated from the physical content. This provides the opportunity whereby content can be selected, to create a learning offering, in a sequence that suits each individual learner. This would allow the developers of online learning offerings to concentrate on the pedagogical design of such offerings, rather than on content development. However, in such systems (Knowledge Tree, Aha!, APeLS) the content is, in most cases, still sourced from a private repository of learning resources. This paper discusses ongoing 446 S. Lawless and V. Wade research into the provision of dynamic adaptivity using open corpus content rather than content uniquely created for each system involved.The paper begins with a brief introduction into the state of the art in Adaptive Hypermedia Systems (AHS), how they currently source and utilize learning content, and how this has evolved over the generations of AHS. The paper proceeds by identifying the key issues and challenges in the discovery, harvesting, and delivery of open corpus content for AHS. The paper concludes by presenting a proposed system architecture of an open corpus content service, in its initial stages. The areas where, and m...