eProfile
The project proposes to build an ELF toolkit based around an eProfile server (ELF Producer) that will maintain individuals personal profiles and will support the social networks and relationships that exist between these users.
Summary
The toolkit will be demonstrated in the context of how it can be used to support the formation and establishment of online learning communities, and will include the development of an innovative TouchGraph based client application (ELF Consumer). Some of this functionality (eg. FOAF profiles) is already available in other open source systems, such as eLGG (see elgg.net) which includes user profiling alongside many other functionalities. However, this is not in a form which is suitable for the ELF. We intend to produce a stand-alone light-weight profile server that can be incorporated into the ELF framework, and can easily be tailored and combined with other ELF components, such as resource repositories and community services, to form new e-learning applications and services.
Methodology
The methodology we used was to use the friend-of-a-friend vocabulary (FOAF) 1 , as the basis for the creation of a toolkit for the discovery and formation of new research communities. Underlying the system was the concept of social networks that represent both users and existing learning communities/resources. Two types of relationships were emphasized: user to user, and user to community/resource. Discovery of people and resources is possible by traversing these social networks. Figure 1 illustrates a touch graph interface to the system. Users can enter keywords describing the relevant learning areas sought, e.g. ‘Bluetooth positioning’. The system will then return the relevant results as a clickable social network containing nodes that represent individual users. Navigating over a node will display a user’s, profile together with any links to the relevant information space. Clicking on an individual node will expand the node’s social network hence allowing additional nodes to be discovered. This approach will potentially allow extremely large numbers of users and communities to be visualized in a manner where users are not overwhelmed with irrelevant results, together with allowing users to narrow down discovery by clicking through the various parts of a social network they find relevant.