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e Learning and S emantic Web

e Learning and S emantic Web. AIFB. Rudi Studer Christoph Schmitz, Steffen Staab, Gerd Stumme, Julien Tane Learning Lab Lower Saxony http://www. learninglab.de Institute AIFB, University of Karlsruhe http://www.aifb.uni-karlsruhe.de/WBS. Overview. Motivation

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e Learning and S emantic Web

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  1. eLearning and Semantic Web AIFB Rudi Studer Christoph Schmitz, Steffen Staab, Gerd Stumme, Julien Tane Learning Lab Lower Saxony http://www.learninglab.de Institute AIFB, University of Karlsruhe http://www.aifb.uni-karlsruhe.de/WBS

  2. Overview • Motivation • Semantic Web: Ontologies and Metadata • Ontology-based eLearning • Conclusion

  3. Motivation eLearning aims at replacing • time • place • content predetermined learning with a • task-relevant • personalized • just-in-time • active process of learning How do we achieve these objectives?

  4. Metadata eLearning Standards Goal: To promote and facilitate the discovery, retrieval,andcomposition of relevant eLearning materials Approach: • Provide descriptions of learning resources • Enable search and query • Enable configuration of learning components

  5. Current Metadata Standards for eLearning • IEEE LOM(Learning Object Meta-data) • ARIADNE – Alliance of Remote Instructional, Authoring and Distribution Networks for Europe • IMS – Instructional Management System Global Learning Consortium

  6. Characteristics of Current Approaches <Classification> <Taxonpath> <Taxon> <Entry>Engineering</entry> <Taxon> <Entry> Electr. Engineering</entry> <Taxon> <Entry> <langstring xml:lang="en"> Integrated Digital Circuit </langstring> </Entry> </Taxon> </Taxon> </Taxon> </Taxonpath> </Classification> <SemanticsOfTheResource> <Discipline> Engineering </Discipline> <Subdiscipline> Electr. Engineering <Subdiscipline> <MainConcept> Integrated Digital Circuit </MainConcept> <MainConcept> Chip </MainConcept> <OtherConcepts> CMOS </OtherConcepts> </SemanticsOfTheResource> Challenges: • Name Conflicts • Structure Conflicts • Limited Semantics

  7. Limitations of current Web-based solutions • search and query is restricted to keyword-based methods • contents of course components is captured only partially • lack of flexible customization to user needs • lack of flexible configuration of course components

  8. Overview • Motivation • Semantic Web: Ontologies and Metadata • Ontology-based eLearning • Conclusion

  9. Semantic Web “The Semantic Web: a new form of Web content that is meaningful to computers will unleash a revolution of new possibilities“ (Tim Berners-Lee et al., Scientific American, 2001) • Make content accessible and interpretable by machines • Provide metadata that come with precisely defined semantics • Provide shared vocabulary

  10. Meaning Triangle Concept evokes refers to Thing Symbol stands for “People [and machines] can’t share knowledge if they don’t speak a common language” (Davenport) jaguar

  11. Ontologies “An ontology is an explicit specification of a conceptualization“ (Gruber 1993) Ontologies offer • a conceptual foundation for communicationbetween actors • aformal basis to define a vocabulary • a vocabulary that is shared among a group of actors Ontologies are used at run time, they are part of the eLearning application

  12. Ontologies and RDF/RDFSchema • RDF/RDFSchema provide modelling primitives for lightweight ontologies • classes and subClass hierarchies • properties and subProperty hierarchies • domain and range specifications for properties • instances

  13. Ontologies & RDF(S) rdfs:Resource rdfs:Class rdf:Property appl:Topic appl:Title appl:Document appl:content appl:Protocol appl:Service Subject http://www.fzi.de/WIM/Protocol.html Predicate Object HTTP Protocol RDF/RDFS layer and namespace subClassOf instanceOf application specific schema and namespace appl:content application specificactual data appl:Title „RFC2068: Hypertext transfer Protocol“

  14. Semantic Web & Ontologies • More heavyweight ontologies all to capture more semantics of a domain • prevDocument and nextDocument are inverseto each other • subClassOf is transitive • axiomsyield additional knowledge: teaches (prof1, course2) and isAbout (course2, ontologies)  knowsAbout (prof1, ontologies)

  15. Overview • Motivation • Semantic Web: Ontologies and Metadata • Ontology-based eLearning • Conclusion

  16. Semantic Web & eLearning • The Semantic Web is a very suitable framework for realizing some aspects of eLearning systems • Development steps: • collaborative ontology developmentfor eLearning • ontology-based annotation of learning material • integration of learning resources • dynamiccomposition of course material • Access: • proactive delivery of the learning materials • integrated browsing and query facilities

  17. Conventional Metadata for eLearning <Classification> <Taxonpath> <Taxon> <Entry> Engineering </entry> <Taxon> <Entry> Electr. Engineering</entry> <Taxon> <Entry> <langstring xml:lang="en"> Integrated Digital Circuit </langstring> </Entry> </Taxon> </Taxon> </Taxon> </Taxonpath> </Classification> <SemanticsOfTheResource> <Discipline> Engineering </Discipline> <Subdiscipline> Electr. Engineering <Subdiscipline> <MainConcept> Integrated Digital Circuit </MainConcept> <MainConcept> Chip </MainConcept> <OtherConcepts> CMOS </OtherConcepts> </SemanticsOfTheResource> Problems: • Lack offormal semantics • Different vocabulariesfor metadata • Lack of semantic mapping • Ontologiesare suitable as aconceptual backbone • Define shared understanding • Provide semantic underpinning for metadata

  18. Ontology-based Metadata Metadata in eLearning are concerned with severalorthogonal dimensions: What the learning material is about (content) What is the learning situation (context) How is the learning material connected to other learning materials (structure) Context Learning resource Structure Content

  19. Ontology-based Metadata Benefits in the process of providing and accessing information: Synonyms “Agent” and “Actor” Different languages “Lecture” (English) and “Vorlesung”(German) Morphological variations “eLearning”and “e-Learning” Semantic relationships “Computer Science” is a superTopicOf“Database Systems” „Professor“ is related to „Course“ via the teaches relation „CS101“ is an instanceOf „Course“

  20. Overview • Motivation • Semantic Web: Ontologies and Metadata • Ontology-based eLearning • Conclusion

  21. Conclusion Ontologies can support eLearning in three ways: • for describing the semantic content of the learning materials, • for defining the learning context and • for describing the structure of the learning materials

  22. Conclusion This three-dimensional, semantically structured space enables • semanticquerying • PADLR module EdutellaPresentations byChristoph Schmitz and others • semanticnavigation • PADLR module Courseware WatchdogPresentation by Julien Tane • flexible combination and personalization of learning resources

  23. Thank you! AIFB Learning Lab Lower Saxony http://www.learninglab.de Institute AIFB, University of Karlsruhe http://www.aifb.uni-karlsruhe.de/WBS

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