doiSerbia

AMASE: A framework for supporting personalised activity-based learning on the web

Staikopoulos Athanasios (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)
O'Keeffe Ian (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)
Rafter Rachael (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)
Walsh Eddie (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)
Yousuf Bilal (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)
Conlan Owen (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)
Wade Vincent (Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland)

Personalised web information systems have in recent years been evolving to provide richer and more tailored experiences for users than ever before. In order to provide even more interactive experiences as well as to address new opportunities, the next generation of Personalised web information systems needs to be capable of dynamically personalising not just web media but web services as well. In particular, eLearning provides an example of an application domain where learning activities and personalisation are of significant importance in order to provide learners with more engaging and effective learning experiences. This paper presents a novel approach and technical framework called AMASE to support the dynamic generation and enactment of Personalised Learning Activities, which uniquely entails the personalisation of media content and the personalisation of services in a unified manner. In doing so, AMASE follows a narrative approach to personalisation that combines state of the art techniques from both adaptive web and adaptive workflow systems.

Keywords: adaptive framework, personalised learning, learning activities, adaptive services and workflow