A Virtual Organisation for e-Learning Nicola Capuano, Pierre Carrolaggi, Jerome Combaz, Fabio Crestani, Matteo Gaeta, Erich Herber, Enver Sangineto, Krassen Stefanov Mikel Vergara

Presentation Overview Diogene: a system based on Virtual Organization The different types of users The system’ organizations Conclusions

The Diogene’s system Diogene is an European funded project (IST ) The objective is an e-learning system for automatic course generation and customization exploiting Semantic Web capabilities. The system’s architecture is based on a distributed and cooperating Virtual Organization

Virtual Organization A Virtual Organization (VO) can be defined as a coordinated group of individuals and/or institutions who collaborate for a common interest (a business goal) and share, on the basis of some policies, a set of resources (processors, instruments, data and services).

The Diogene’s Virtual Organization The Diogene’s Virtual Organization is composed of entities offering and consuming services Such entities are grouped into two main categories: users and organizations

Different types of users Learner: is a student registered in the Diogene VO Expert Learner: is a Learner skilled about some topics. He/she offers mentoring support to other learners about such topics, usually for free Freelance Tutor: is a professional teacher offering mentoring support to learners about specific topics under the payment of some price Skill Searcher is an enterprise manager interested in hiring certified staff. He/she performs queries on skill repositories

Organizations The Diogene’s Organizations are software components which automatically provide learning services Each Organization is realized as a Web Service

The system’s Organizations Organizations for the content storage: – Publishing Houses (PH). They store Learning Objects indexed by means of Metadata and provide remote access to them – Web Catcher Agencies (WA). They are able to extract training content directly from Web and Semantic Web

The system’s Organizations Organizations for automatic course generation/personalization: – Training Agencies (TA). They are the entry points of learners to the VO. They accept the user’s queries, are responsible of course delivering and interact with the other organizations

– Knowledge Agencies (KA). They maintain and manage knowledge structures (concept dictionaries and ontologies) for the whole Diogene Network. It is a singleton organization. It receives the user’s training query by the TA and generates the structure of the course (Learning Path) using the abstract knowledge on the didactic domain described by its domain ontology

– Brokerage Offices (BO). They personalize the Learning Path received by the KA using the Learning Objects received by either the PHs or the WAs

The system’s Organizations Other Organizations: – Tutor Agencies (TG). They work as entry point for freelance tutors, they manage the freelance tutor archive (containing tutor models) and provide searching facilities on such archive – Skill Agencies (SA). They provide search engine capabilities on the Learner Models’ Databases in order to allow third parties interested to hire certified staff to find qualified learners

– Café (CA). They support social interactions, mentoring and information exchange by providing users a set of collaborative synchronous and asynchronous facilities. They can also automatically cluster learners with respect to possible similar needs and/or profiles – Bank (BA). It’s a singleton organization which executes and logs transactions information occurred inside the Diogene VO. It interacts with an e- commerce engine to execute transactions involving real payment processes

An example of Diogene Virtual Organization

Conlusions We have presented a distributed e-learning system developed during the European funded project Diogene Diogene is based on a network of independent organizations each of which realized as a Web Service. These agencies provide several e-learning and related facilities, such as course delivering, course generation and customization, free document retrieval and categotization on the Web and on the Semantic Web, Learning Objects’ repositories based on a metadata indexing, human tutor support and external skill search.

Conclusions Eight courses have been adapted from existing “traditional” courses splitting them in different Learning Objects and indexed using Semantic Web standards Finally, the project has been evaluated using 170 users of various types (learners, tutors,...) with very encouraging results