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Sylvain Dehors Director Rose Dieng-Kuntz INRIA Sophia Antipolis

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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 In the context of learning we are primarily interested in knowledge communication, but as we’ll see other aspects are important E-learning = broad term Connection with KM very natural

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 Conclusion
Selection and analysis of existing material Semi automatic annotation Learning activity Analysis Conclusion

6 Research question Proposal:
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 Semantic Web:
“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 Knowledge holder DB documents services Semantic annotation base ontologies Knowledge Management Syst. edit O edit A 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 Method description 2 1 - Selection and analysis of existing material 2 - Semi automatic annotation 1 4 3 3 - Learning activity 4 - Analysis

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

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

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 Mostly human process

13 Original documents Power Point presentations Modularity
Signal analysis / Java programming Used as hard copy course material Two different experiments: One on an original subject : signal processing One on a classic subject for comparison with other approaches : JAVA programming 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 to find appropriate 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 Principles :
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 3 key principles that are crucial for annotation (both from literature review and experience) 3 steps

17 A semi-automatic process
3 steps Pre-processing Manual annotation Automatic extraction resource to reuse (XML) “annotable” version annotated version content (XHTML) annotation (RDF) pre-processing manual annotation xsl transform. 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 Other indicators usually employed in resource management: date, course, author.

19 Preprocessing

20 Preprocessing <draw:page draw:name="Fundamental concepts">
<draw:text-box> <text:p> Fundamental concepts</text:p> </draw:text-box> <text:unordered-list> <text:list-item> <text:p text:style-name="P19">object</text:p> </text:list-item> <text:p text:style-name="P19">class</text:p> </text:unordered-list> ... </draw:page> <draw:page draw:name="Objects and classes"> <draw:text-box>... <text:p> Objects and classes</text:p> </draw:text-box> <draw:text-box> <text:unordered-list> <text:list-item> <text:p>objects</text:p> </text:list-item> </text:unordered-list> ... </draw:page> <draw:page draw:name="Methods and parameters"> <draw:text-box> <text:p> Methods and parameters </text:p> </draw:text-box> <text:unordered-list> <text:list-item> <text:p> objects have operations which can be invoked (Java calls them methods) </text:p> </text:list-item> <text:p> methods may have parameters to pass additional information needed to execute </text:p> </text:unordered-list> </draw:page>

21 Preprocessing

22 Preprocessing Ici indiquer pas de lemmatisation mais pluriels/singuliers

23 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 »

24 Final result: Open Office-Writer

25 Final result : MS-Word

26 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

27 Experimental results: annotation cost
QBLS-1 QBLS-2 Number of resources 92 359 Num. of resources discarded None 54 Course duration 2H 3 months Number of pedagogical types used (directly) 8/8 12/27 Num. of domain concepts 41 171 Editing Tool Microsoft Word OpenOffice Writer Annotation time N/A 20H Modification of content Yes No

28 1- Selection and analysis of existing material
2 – Semi automatic annotation 4 - Analysis 3 – learning activity

29 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

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

31 Semantic Web at work Dynamic SPARQL queries: Variable SELECT * WHERE {
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 ?doc edu:belongsTo java:Chapter1 ?doc skos:subject ?ext_concept FILTER{ ?ext_doc skos:primirySubject ?ext_concept OR {?ext_doc skos:primirySubject ?c2 . ?c2 skos:broader ?ext_concept}} edu:order rdf:type Layout information Example 3 edu:order 7

32 Semantic Web at work(2) Refining the query Contextual information
User adaptation ?doc skos:subject ?ext_concept { ?ext_doc skos:primirySubject ?ext_concept } UNION { ?ext_doc skos:primirySubject ?c2 . ?c2 skos:broader ?ext_concept } ?ext-doc edu:belongsTo java:Chapter1 OPTIONAL { FILTER (?user = epu:user1) ?user edu:profile ?profile ?profile edu:orderingRelationType ?p ?t ?p ?order }

33 QBLS-1 Simple conceptual navigation Question Based Navigation

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

35 Experimental results: students’ feedback
QBLS-1 QBLS-2 Num. of students using the system 100% 30% Num. of resources visited 90% 80% Overall Satisfaction 4.3/5 3.9/5 Off-hours access N/A 50% of connections Good satisfaction Structured navigation appreciated for direct access to information Use of domain and pedagogical information

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

37 QBLS-ASPL Interesting Web sites for advanced learners

38 QBLS-ASPL Provided by QBLS

39 1- Selection and analysis of existing material
2 – Semi automatic annotation 4 - Analysis 3 – Exploitation by learners

40 Analysis Modeling user activity Exploitation of logs
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 Concept subject of mentions Concept subject of Resource Resource User A Time t

41 Visualization

42 Visualization

43 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 Show the interest of graph based vision of RDF + semantics Object ?v2 Def Ex.

44 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”?

45 Conclusion Semantic Web = valid connector Learning Object Repositories
LOM standard Scorm? Annotation tools Linguistic analysis How it fits all together A coherent and complete method but only few pieces of the global picture Learner modeling Activity tracking Learning Design Adaptive hypermedia Semantic Web = valid connector

46 Conclusion (2) Semantic web interests: Ontologies for e-learning
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”

47 Conclusion(3) Resource Reuse Knowledge management approach
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

48 Perspectives (1) Short term Middle 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)

49 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) All the tools are now there for extensive cognitive experiments.

50 Acknowledgements Catherine Faron-Zucker Jean Paul Stromboni
Peter Sander

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