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N. Capuano 1 , M. Gaeta 1 , R. Iannone 1 , F. Orciuoli 2

Learning Design and run-time resource binding in a distributed e-learning environment. N. Capuano 1 , M. Gaeta 1 , R. Iannone 1 , F. Orciuoli 2 1 Centro di Ricerca in Matematica Pura ed Applicata, {capuano, gaeta, iannone}@crmpa.unisa.it 2 MoMA Srl, {orciuoli}@momanet.it. Introduction.

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N. Capuano 1 , M. Gaeta 1 , R. Iannone 1 , F. Orciuoli 2

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  1. Learning Design and run-time resource binding in a distributed e-learning environment N. Capuano1, M. Gaeta1, R. Iannone1, F. Orciuoli2 1Centro di Ricerca in Matematica Pura ed Applicata, {capuano, gaeta, iannone}@crmpa.unisa.it 2MoMA Srl, {orciuoli}@momanet.it

  2. Introduction • Pedagogies in e-learning systems. • Personalized Learning experience. • Educational e-content repositories and networked infrastructures.

  3. Our Goals • Providing an extension of IMS LD to support domain-independent pedagogies. • Building personalized Unit of Learning (UoL) based on didactical domain ontology-based. • Defining a distributed UoL delivery architecture.

  4. IMS Learning Design (Brief introduction) • Level A: meta language XML-based • Level B: enhances sequencing by properties and condictions • Level C: notification support

  5. IMS Learning Design (Brief introduction) • Step 1: title, learning-objectives, prerequisites

  6. IMS Learning Design (Brief introduction) • Step 2: roles, environments, activities

  7. IMS Learning Design (Brief introduction) • Step 3: multi-user sequencing

  8. IMS Learning Design (Brief introduction) • Level B: properties

  9. IMS Learning Design (Brief introduction) • Level B: conditions

  10. IMS Learning Design (Brief introduction) • Level C: notification

  11. IMS LD drawbacks • Learning design scenarios implement domain-dependent pedagogies. • Learning processes cannot be really adaptive (based on learner profiles). • E-learning scenarios don’t exploit some advantages of distributed infrastructures.

  12. Scenario: Building a UoL • Pedagogy construction Instructional designer builds a pedagogy using IMS LD language …

  13. Scenario: Building a UoL • Didactic domain modeling Domain experts model didactic domain through ontologies …

  14. Scenario: Building a UoL • UoL construction Teacher realize UoL using pedagogy and concepts …

  15. Scenario: UoL delivery architecture Is based on three type of distributed services: • UoL Delivery Service • Localization Service • Repositories

  16. Scenario: UoL delivery architecture

  17. Scenario: UoL delivery architecture UoL Delivery Service that is composed by three tiers:

  18. Scenario: UoL delivery architecture An UoL delivery will walk across two phase: startup and run. Startup phase: • Retrieve target concepts • Query Localization Service for repositories • Query repository for LO metadata • Filter LO metadata respect activity parameters • Perform binding between activities and real LO

  19. Scenario: UoL delivery architecture An UoL delivery will walk across two phase: startup and run. Run phase: • Detects the resource identificator and repository URL • Get WSRP mark-up from the repository’s delivery port • Compose the overall GUI of UoL and return complete WSRP mark-up to the client

  20. Conclusion and … • Extension of IMS LD to meet the need for domain-independent pedagogies. • An ontology-based approach merged with independent pedagogies to obtain personalized units of learning. • A distributed infrastructure for personalized units of learning delivery.

  21. … future work • Develop the presented delivery architecture extending the IWT (Intelligent Web Teacher) platform implemented by MoMA. • Enrich the IWT platform with a set of authoring tools to cover all phases of units of learning building process. • Nowadays we are making experience with Coppercore and Reload opensource projects.

  22. IWT overwiev • Intelligent Web Teacher is an extensible application framework for building learning solutions • It provides software and technologies building blocks for implementing domain specific learning solutions • It has been designed for supporting the emerging learning scenarios (personalised learning path, knowledge management, any time, any place and any pace access to learning services etc.) • It doesn’t exist a solution that fit all need • It should facilitate Learning Object reuse

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