Semantic Technologies in Learning Environments -Promises and Challenges-

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Keynote presentation at CcITA 2009, Merida, Mexico, July 9, 2009

Text of Semantic Technologies in Learning Environments -Promises and Challenges-

  • 1.Semantic Technologiesin Learning Environments- Promises and Challenges- Dragan Ga evi Athabasca University Email: dgasevic@acm.org

2.

  • Without any prior vision
  • the Social Web is here!
  • What now?

3. Topics to discuss about

  • Semantics as a big promise
  • Semantics and metadata
  • Promises for learning environments
  • Open challenges

4.

  • Part I
  • Semantics as a Big Promise

5. Semantic Web

  • To create a universal medium for the exchange of data.
  • to smoothly interconnectpersonal information management,enterprise application integration andthe global sharing ofcommercial, scientific and cultural data.
  • Semantic Web Activity Statement http://www.w3.org/2001/sw/Activity

6. Semantic Web

  • Key characteristics
    • Anyone can say anything about anything
    • Explicit definition of the meaning

7. RDF 8.

  • The Semantic Webis NOT opposite tothe Social Web!

9. Semantic Web

  • Ontologies: Interconnecting applications
    • Shared domain conceptualizations

10.

  • Part II
  • Semantics and Metadata

11. Learning Technology Standards

  • Learning metadata
    • Learning Object Metadata
    • IMS Learning Design, CP, SCORM, etc.
  • Why do need them?
    • Improved search and reusability
    • Promising results
      • SQI, ECL, GLOBE

12. Learning Metadata

  • Why do we need ontologies, then?
    • Not a replacement for LOM
    • Important complement of LOM
      • Topic
      • Pedagogical role
      • Type of content
      • Type of activities

13. Is this all we can get?! 14.

  • Part III
  • Promises forLearning Environments

15. Learning Design

  • Situation is getting even more exciting
    • Learning design with ontologies
      • Good for formal verification
      • Spotting issues in learning designs
  • Not quite personal information management yet

16. Context is Missing!

  • Andrew McAfee (Harvard University)
    • SLATES paradigm search, links, authoring,tagging, extension, and signals
  • Web 2.0 in Enterprise: Enterprise 2.0
    • Learning in enterprise

17. What is that?

  • Julita Vassileva (U of Saskatchewan)
    • Rule: hard/impossible to impose hard rules
  • Gord McCalla (U of Saskatchewan)
    • Ecological approach sharing experience

18. Context is Missing! http://www.w3.org/2006/Talks/1023-sb-W3CTechSemWeb/DataServicesWebAppMetro2.jpg 19. Learning Context Authoring Reusability Packaging Educators Reusability Adaptivity Evolution Collaboration with educators and students 20. Learning Context Authoring Reusability Packaging Educators Feedback Learning and Collaborating Personalization Adaptivity Context-awareness Social interaction Learners 21. Learning Context Authoring Reusability Packaging Learning and Collaborating Community Peer-Review Presenting Administration Mobile Educators Learners 22. PersonalLearning Information Management

  • Connect presently isolated islands

Students/Educators

  • Content
  • LORs
  • Libraries
  • Multimedia
  • Reuse
  • Pedagogy
  • User models
  • Adaptivity
  • Educational models
  • Collaboration
  • Chat
  • Discussion
  • Services
  • Community
  • Course, University,
  • Portfolio
  • Evidence
  • Competencies
  • Peer-review
  • Courses
  • Publishing
  • Platforms
  • Mobile
  • Desktop
  • Domain tools
  • Mashed up with education tools
  • Desktop
  • Email
  • Firefox
  • Authoring
  • Word
  • Frontpage
  • Reload
  • Privacy
  • Policy
  • Identity

23.

  • A crazy problem requiresa crazy solution!
  • (Griff Richards, 2005)

24. Learning ObjectContext Ontology: LOCO

  • Learning context
    • Learner(s)
      • Learner characteristics
    • Learning activity
      • Prerequisites, learning objectives, available time,
    • Learning object used/produced
      • Parts of objects and their pedagogical role
    • Domain concepts

25. LOCO-Analyst

  • LOCO-Analyst and iHelp Courses

26. LOCO-Analyst

  • Semantically rich feedback for educators
    • Learning activities of their students
    • Domain topics difficult for learners
    • Unusual performance of a learner/group
    • Use of the deployed learning content
    • Peculiarities of interactions

27. LOCO-Analyst 28. LOCO-Analyst 29. LOCO-Analyst

  • Collaboration: students - educators
  • Evaluation results show appreciation of
    • Qualitative (over quantitative) feedback
    • Integrated view onthe learning process as a whole
  • Form killing count
    • Student behavior is already feedback

30. DEPTHS

  • DEsign Patterns Teaching Help System
  • Harmonization of
    • Project-based instruction
    • Collaborative learning, and
    • Personal learning goals

31. 32. DEPTHS 33.

  • Part IV
  • Open Challenges

34. Challenges

  • Ontology development
    • Domain-independent
      • Context, content, design, user model
    • Domain-specific
      • Domain ontologies

35. Challenges

  • Ontology development
    • Similar to databases someone needs to develop
  • Some ontology development lessons learned
    • Lightweight ontologies: Little semantics goes a long way
    • Linked with other ontologies
      • Friend of a Friend
      • Semantically-interlinked Online Communities

36. Ontology Development - LOCO 37. Ontology Development - LOCO Integration is enabled! 38. Challenges

  • Ontology development
    • Ontology development tools
      • Semi-automatic ontology learning

39. Educators in ontology action Text2Onto OntoGen 40.

  • If we have big expectations
  • Why dont we build better tools?!

41. Challenges

  • Appreciation of empirical research
    • Google:constantly measuring everything that may and may not be measured

42. Challenges

  • Ontology development
    • Different approaches
      • Wiki-based (DBPedia and Semantic Media Wiki)
      • Folksonomy-based
        • Solving ambiguity of folksonomies
        • Folksonomies require time and community
      • Social bookmarking ( Faviki )

43. Challenges

  • Pedagogical models with new technology
    • Technology is NOT enough
    • What motivates students to contribute
      • E.g., present activity, career or life-long goals
  • Increased interaction and p