SeLeNe : Self e-Learning Networks

Life-long learning and the knowledge economy have brought about the need to support a broad and diverse community of learners throughout their lifetimes. These learners are geographically distributed and highly heterogeneous in their educational backgrounds and learning needs. The number of learning resources available on the Web is continuously increasing, thus indicating the Web's enormous potential as a significant resource of educational material both for learners and instructors.

The SeLeNe project aims to elaborate new educational metaphors and tools in order to facilitate the formation of learning communities that require world-wide discovery and assimilation of knowledge. To realize this vision, SeLeNe is relying on semantic metadata describing educational material. SeLeNe offers advanced services for the discovery and sharing of learning resources, facilitating a syndicated and personalised access to such resources. These resources may be seen as the modern equivalent of textbooks, comprising rich composition structures, "how to read" prerequisite paths, subject indices, and detailed learning objectives.

The SeLeNe project was funded as an EU FP5 Accompanying Measure (IST-2001-39045) running from 1st November 2002 to 31st January 2004. This project was part of action line V.1.9 CPA9 of the IST 2002 Work Programme, contributing to the objectives of Information and Knowledge Grids by allowing access to widespread information and knowledge, with e-Learning as the test-bed application. We conducted a feasibility study into using Semantic Web technology for syndicating knowledge-intensive resources (such as learning objects) and for creating personalized views over such a Knowledge Grid.

The project had 3 main objectives , each addressed by one Workpackage:

To conduct a study of on-line educational resources and metadata, and of learners' and instructors' expectations of e-Learning systems.
To identify technologies for syndication and personalisation of educational resources, including: semantic reconciliation and integration of heterogeneous educational metadata, structured and unstructured querying of learning object descriptions, language primitives for defining user views, and automatic notification of changes in the descriptions of learning objects.
To identify technologies for managing evolving RDF description bases, and design the high-level system architecture of a Self e-Learning Network.

Novel features of this research include:

registration of composite learning objects (LOs), dependent on other atomic or composite LOs;
definition of personalised views over the LO descriptions and schemas;
event and change notification and propagation services;
automatic generation of taxonomical information for composite LOs;
support of trails and personalisation of query results.

Key transferable outcomes of the SeLeNe project are as follows (see Deliverable 7 for a more detailed analysis of these transferable outcomes):

a survey of e-learning standards and the feasibility of expressing them using Semantic Web languages such as RDF/S, including a discussion of the pedagogical limitations of existing standards (see Deliverable 2.1 );
the specification of the functionality of a self e-learning network (see Deliverable 2.2 );
the specification of a Grid service based architecture supporting this functionality, together with three possible concrete deployment scenarios each of which supports a different kind of learning community (see Deliverables 3 and 5 );
the identification of composite learning objects as a new paradigm for collaborative construction of educational material (see Deliverables 2.2 and 4.1 );
an algorithm for automatic generaction of taxonomical descriptions of composite learning objects (see Deliverable 4.1 );
the specification of a User Profile for SeLeNe users, which combines elements from existing learner profile schemes and adds extra elements where these schemes are insufficiently expressive to adequately support SeLeNe's personalisation requirements (see Deliverable 4.2 );
the RVL view definition language which allows personalised views of learning object descriptions and schemas to be defined (see Deliverable 4.3 );
the XML and RDF event-condition-action rule languages which support SeLeNe's event notification and change detection/propagation services (see Deliverable 4.4 );

After the formal end of the project, research has continued in several of the above directions.

The Project Partners are:

School of Computer Science and Information Systems, Birkbeck College, University of London (coordinator): George Loizou, Alex Poulovassilis, Mark Levene, Peter Wood, Kevin Keenoy (joint BBK/IOE RA), George Papamarkos (RA).
School of Mathematics, Science and Technology, Institute of Education, University of London : Don Peterson, Richard Noss, Kevin Keenoy (joint BBK/IOE RA)
Foundation for Research and Technology, Hellas (FORTH) : Vassilis Christophides, Dimitris Plexousakis, Grigoris Karvounarakis, Giorgos Kokkinidis, Giorgos Serfiotis, Aimilia Maganaraki, and Miltos Stratakis.
Laboratoire de Recherche en Informatique (LRI), Unversite Paris-Sud : Nicholas Spyratos, Philippe Rigaux, Birahim Gueye.
Department of Computer Science, University of Cyprus: George Samaras, Kyriakos Karenos, Eleni Christodoulou.