Exploiting Semantic Web and Knowledge Management Technologies for E-learning

1. Exploiting Semantic Web and Knowledge Management Technologies for E-learning Sylvain Dehors Director Rose Dieng-Kuntz INRIA Sophia Antipolis University of Nice-Sophia Antipolis/ ED STIC

2. E-learning, this ?

3. A vision of e-learning • For us: • Any learning activity mediated by a computer • Buzz Word, but also real change in practices • Use of computers in daily activities • All ages, from youngster to adult teaching • In practice, several types of application • Simulation programs • Tutoring systems • On-line courses

4. Our e-learning situation • Learning organization • Teacher(s) with a group of students • Environment • Computers for daily usage • Either on-line or face-to-face • Knowledge Sources • Course documents • Teacher’s expertise • Provide computer support for taking advantage of the knowledge sources

5. Outline • Research question • Method Proposal • Selection and analysis of existing material • Semi automatic annotation • Learning activity • Analysis • Conclusion

6. Research question How can teachers and students better use knowledge sources, such as pedagogical documents, with computer interfaces ? • Proposal: • apply Knowledge Management techniques and Semantic Web technology • develop a practical method • Illustration: a tool (QBLS) and experiments

7. Inspirations • Knowledge Management • “The objective of a knowledge management structure is to promote knowledge growth, promote knowledge communication, and in general preserve knowledge within the organisation” (Steels L., 93) • Semantic Web: • “The Semantic Web provides a common framework that allows data to be shared across application, enterprise, and common boundaries.” (W3C) • Standards: RDF, RDFS, OWL, SPARQL

8. Existing methods and tools (Dieng et al.) • Corporate semantic web DB Knowledge holder services documents Semantic annotation base ontologies Knowledge Management Syst. edit A edit O query User (collective task) User (Individual task) • Apply to a learning organization • - Tool: Corese semantic search engine to query formalized knowledge • W3C Standards expressing knowledge about the course

9. 2 1 3 4 Method description • 1 - Selection and analysis of existing material • 2 - Semi automatic annotation • 3 - Learning activity • 4 - Analysis

10. 2 1 3 4 Method description Select Enrich Original resources selection KM tools Semantization • Ontologies : • Document • Pedagogy • Domain Usage feedback tests Annotations Activity analysis Conceptual navigation + adaptation Use Analyze

11. Experiment’s Agenda QBLS-2 : 3 months course Java Programming QBLS-1 : 2 hours lab Signal Analysis QBLS-ASPL : Knowledge Web NoE Semantic Web studies 2005 2007 2006

12. Resource selection • First, establish a pedagogical strategy • Collaboration Teacher/QBLS designer • QBLS: Question Based Learning Strategy: Motivation, autonomy, self-directed learning • Existing resources: • Objective criteria • Availability, standard editable format (XML) • Suitability for annotation (modularity, coherence, vocabulary used) • Subjective criteria • Scope, goal, context • Teacher’s acceptance

13. Original documents Power Point presentations • Signal analysis / Java programming • Used as hard copy course material Modularity Coherence, Vocabulary

14. Ontology selection • Selection of existing models, ontologies? • Document: • Must fit the course structure • Document organization Document ontology • Pedagogy: • Appropriate for the pedagogical approach • Domain to learn: • Usually the biggest ontology • Fit the document contents (vocabulary used, conceptualization) • Fit the teacher’s vision Lots of constraints, difficult tofindappropriate ontologies

15. 1- Selection and analysis of existing material 2 – Semi automatic annotation 4 - Analysis 3 – Learning activity

16. Annotation • Express additional knowledge about the course • Based on teacher’s expertise and vision • Principles : • Use existing edition tools • Proceed through visual mark-up • Rely on XML technologies and Semantic Web formalisms

17. A semi-automatic process • 3 steps • Pre-processing • Manual annotation • Automatic extraction resource to reuse (XML) annotated version “annotable” version content (XHTML) annotation (RDF) xsltransform. manual annotation pre-processing Ontologies (OWL, RDFS)

18. Preprocessing • Identification of the content characteristics • Separation in small entities • Automatic annotation • Vocabulary used → domain concepts, automatic annotation with domain ontology • Resource roles → pedagogical ontology • Preparation • Styles → reflect ontological concepts • enrich style lists with ontologies

19. Preprocessing

20. Manual annotation • Exploitation of tools functionalities by the teacher for a visual markup • Evolution/enrichment/creation of corresponding domain ontology • Practical objective: connecting navigation paths • Edition of the content • Linking concepts with semantic hierarchical relations (SKOS) Statement Interface skos:broader skos:broader skos:broader Conditional Statement Assignment Statement Keyword « implements »

21. Final result: Open Office-Writer

22. Final result : MS-Word

23. Experimental results: ontology re-use • Pedagogical ontology • Reused directly • Same intention as original: describe ped. role (generic?) • Domain Ontology • Design intention very important: here offer “conceptual views” of the resources • Mostly developed specifically, comparisons with other domain ontologies show striking incompatibilities. Method modifiers Access rights public protected private public protected private

24. Experimental results: annotation cost

25. 1- Selection and analysis of existing material 2 – Semi automatic annotation 4 - Analysis 3 – learning activity

26. Learning activity • Offer “conceptual” navigation in the set of resources while answering questions or performing exercises • Navigation through semantic queries • Take advantage of domain concepts hierarchy (broader links) • Use typology of pedagogical concepts for ordering (subsumption) • Interface generation • Static XSL style sheets: performance, reuse, maintenance

27. Semantic Web architecture Pedagogical ontology Domain vocabulary Doc. model Corese Semantic Search Engine (RDFS) (OWL) (Skos) rules logs (RDF) 4 Formalized Knowledge Answers (Sparql-XML) 3 Queries (Sparql) web-app content (XHTML) XSLT Learner Interface (XHTML) 2 5 Request 6 Tomcat web server HTTP 1

28. Semantic Web at work Variable • Dynamic SPARQL queries: skos:primarySubject skos:broader SELECT * WHERE { FILTER (?c = java:variable) { ?doc skos:primarySubject ?c } UNION { ?doc skos:primarySubject ?c2 . ?c2 skos:broader ?c} ?doc rdf:type ?t ?t edu:order ?order ?doc dc:title ?docTitle ?t rdfs:label ?docLab ?c skos:prefLabel ?cLab } ORDER BY ?order Local Variable skos:primarySubject rdf:type Definition rdf:type edu:order Layout information Example 3 edu:order 7

29. QBLS-1

30. QBLS-2 Human readable information Variable skos:broader Fields Local variable

31. Experimental results: students’ feedback • Good satisfaction • Structured navigation appreciated for direct access to information • Use of domain and pedagogical information

32. QBLS-ASPL(Advanced Semantic Platform for Learning) • Existing resources on a portal : REASE, • MS-PowerPoint files

33. QBLS-ASPL Interesting Web sites for advanced learners

34. QBLS-ASPL Provided by QBLS

35. 1- Selection and analysis of existing material 2 – Semi automatic annotation 4 - Analysis 3 – Exploitation by learners

36. Analysis • Modeling user activity • A navigation model based on a graph representation • Exploitation of logs • Visualization through automatically generated graphs • Use semantic querying to highlight particular characteristics of the graphs represented in RDF mentions Concept subject of subject of Concept Resource Resource User A Time t

37. Visualization

38. Visualization

39. Semantic querying • Find regularities, patterns? • Using the graph structure • Relying on the ontology • SELECT ?user count ?v WHERE { • ?aux skos:primarySubject ?concept • ?aux rdf:type edu:Auxilliary • ?v edu:user ?user • ?v edu:conceptVisited ?concept • OPTIONAL { ?v2 edu:resourceVisited ?aux • ?v2 edu:user ?user} • FILTER(! bound(?v2)) • } ?v Object ?v2 Def Ex.

40. Experimental Results • Involve teacher’s in the analysis • Problem with large size graphs • Visualization tools not sufficient yet • Needs to be coupled with other sources of information • First step towards automated interpretation • Define a collection of patterns -> behavioral patterns • Use in “real-time”?

41. Conclusion Learning Object Repositories LOM standard Scorm? Annotation tools Linguistic analysis Learner modeling Activity tracking Learning Design Adaptive hypermedia Semantic Web = valid connector

42. Conclusion (2) • Semantic web interests: • Existing tools, Corese, Protégé, etc. • Existing models, in standard language • Unification and connection with other systems • Ontologies for e-learning • Interest, reusability of domain might be limited • Need for simple expressivity, “goal oriented design”

43. Conclusion(3) • Resource Reuse • Observed use and good satisfaction level • Definite interest, cost still high • Knowledge management approach • Satisfaction of users • Initial goal fulfilled • May apply to other learning contexts

44. Perspectives (1) • Short term • Further develop annotation system based on existing tools • Administrative tools to make teachers fully autonomous • Middle term • Enhance scalability with large RDF bases ( when triples are generated by learner activity) • Generalize log visualization, work on usage of such representations (e.g. teachers’ interpretations)

45. Perspectives (2) • Long term • Investigate the cognitive implications for learning of the annotations • Importance of the pedagogical concepts • Structure of the domain • Enhance user tracking (more information, refine model)

46. Acknowledgements • Catherine Faron-Zucker • Jean Paul Stromboni • Peter Sander