Adaptive Personalisation in Self e-Learning Networks

Kevin Keenoy (1) , Alexandra Poulovassilis (1) , George Papamarkos (1) , Peter Wood (1) , Vassilis Christophides (2) , Aimilia Magkanaraki (2) , Miltos Stratakis (2) , Philippe Rigaux (3) , Nicolas Spyratos (3)

1 School of Computer Science and Information Systems
2 ICS-FORTH - Institute of Computer Science [FORTH, Heraklion]
3 LRI - Laboratoire de Recherche en Informatique

This paper presents some of the personalisation services designed for self e-learning networks in the SeLeNe project. A self e-learning network consists of web-based learning objects that have been made available to the network by its users, along with metadata descriptions of these learning objects and of the network's users. The architecture of the network is distributed and service-oriented. The personalisation facilities include: querying learning object descriptions to return results tailored towards users' individual goals and preferences; the ability to define views over the learning object metadata; facilities for defining new composite learning objects; and facilities for subscribing to personalised event and change notification services.

Keywords: learning grids; learning trails; personalisation; self e-learning networks; resource description frame (RDF) view definitions; event-condition-action (ECA) rules; resource description frame (RDF) rules

Domains: Computer Science [cs]; Technology for Human Learning

Kevin Keenoy, Alexandra Poulovassilis, George Papamarkos, Peter Wood, Vassilis Christophides, et al.. Adaptive Personalisation in Self e-Learning Networks. 1st International ELeGI Conference on Advanced Technology for Enhanced Learning, 2005, Vico Equense (Naples), Italy. pp.9. ⟨hal-00190584⟩