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IMS Learning Impact 2008 Austin, Texas May 14th, 2008 The TELOS System Gilbert Paquette, LORNET Scientific Director François Magnan, TELOS System Architect Suzanne Lapointe, Technology Transfer Officer LICEF Research Center, Télé-université www.licef.teluq.uquebec.ca/gp. Télé-université.
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IMS Learning Impact 2008Austin, Texas May 14th, 2008The TELOS SystemGilbert Paquette, LORNET Scientific DirectorFrançois Magnan, TELOS System ArchitectSuzanne Lapointe, Technology Transfer OfficerLICEF Research Center, Télé-universitéwww.licef.teluq.uquebec.ca/gp
Télé-université • A higher institution for education and research • A multimedia editor of learning resources • A broadcaster of over 350 courses to support learners wherever they are • LICEF, the research center • Cogigraph (TCI), the transfer spin-off
Learning Design Portal www.idld.org • IMS-LD scenarios referential • Suite of Tools • Methodological Aids • Documentation
Internet Generations K 3 Semantic Web 4 Intelligent Web Increasing Knowledge Networking 1 Information Web 2 Social Web Increasing Social Networking S
2. Multi-actor Design 1. Inter -operability 6. TELOS 3. Adaptive Resources 5. Advanced Multimedia 4. Knowledge Extraction The LORNET Research Network Pan-canadian Research Network 6 Universities, 4 Research Chairs, 15 entreprises 5 years, 7.5 M $, 120 researchers and grad students • New Knowledge on Web 3.0 • 60 Innovative prototype tools • Design and Construction of TELOS Achievements
Task Manager Ontology Editor (OWL) Ressource Manager (LOM/DRI) Scenario Editor (IMS-LD/BPMN) TELOS SOA Framework
U U U U U U U U IMS-LD Services
Two-Way Semantic Referencing • Any kind of resource can be an instance in more than one ontology • The technical ontology tells TELOS how to execute the resource • A domain ontology tells a search engine what knowledge and competency is own by the ressource
ROI and Learning Impact(compare to traditional LCMS) • Global Systemic View • Extended and flexible set of actors • Multi-actor process coordination • Visual scenarios and workflows • Flexible and adaptable environments (through ontology extension) • Multi-technology interoperability • IMS-LD and OWL modeling for all • Simple and Powerful Visual Language • Reduced Time an Effort • Focus on learning
ROI and Learning Impact(Compare to form-based IMS-LD) • Visual representation and execution at level B and C for personalization • Extension of the types of conditions and multiple activities from BPMN • Visual representation of services • More transparent visual representation of learning scenarios • Embedded Metadata referencing by ontologies (including the LOM) • Simple and Powerful Visual Language can make possible the dissemination of IMS-LD
IMS Learning Impact 2008Austin, Texas May 14th, 2008Merci!Gilbert Paquette, LORNET Scientific DirectorFrançois Magnan, TELOS System ArchitectSuzanne Lapointe, Technology Transfer OfficerLICEF Research Center, Télé-universitéwww.licef.teluq.uquebec.ca/gp