exploiting semantic web and knowledge management technologies for e-learning sylvain dehors director...
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Exploiting Semantic Web and Knowledge Management
Technologies for E-learning
Sylvain DehorsDirector
Rose Dieng-KuntzINRIA Sophia Antipolis
University of Nice-Sophia Antipolis/ ED STIC
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E-learning, this ?
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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
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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
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Outline
1. Research question
2. Method Proposal
1. Selection and analysis of existing material
2. Semi automatic annotation
3. Learning activity
4. Analysis
3. Conclusion
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Research question
• Proposal:– apply Knowledge Management techniques
and Semantic Web technology– develop a practical method
• Illustration: a tool (QBLS) and experiments
How can teachers and students better use knowledge sources, such as pedagogical
documents, with computer interfaces ?
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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
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Existing methods and tools (Dieng et al.)
• Corporate semantic web
Semantic annotation
base
ontologies
Knowledge holder
DB services
Knowledge Management Syst.edit O edit A
query
documents
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
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Method description
4 3
21
1 - Selection and analysis of existing material
2 - Semi automatic annotation
3 - Learning activity4 - Analysis
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4 3
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Method description
Original resourcesselection
Semantization
Conceptual navigation
+ adaptation
Activity analysis
tests
Usage feedback
Ontologies :DocumentPedagogyDomain
Annotations
Select Enrich
UseAnalyze
KM tools
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Experiment’s Agenda
2005 2006 2007
QBLS-1 :
2 hours lab
Signal Analysis
QBLS-2 :
3 months course
Java Programming
QBLS-ASPL :
Knowledge Web NoE
Semantic Web studies
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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
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Original documentsPower Point presentations
– Signal analysis / Java programming– Used as hard copy course material
Modularity
Coherence, Vocabulary
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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
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1- Selection and analysis of
existing material
2 – Semi automatic annotation
3 – Learning activity4 - Analysis
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• 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
Annotation
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A semi-automatic process
• 3 steps– Pre-processing– Manual annotation– Automatic extraction
resource to reuse “annotable”
versionannotated
version
annotation
(RDF)
content
(XHTML)
manual annotation
xsl transform.
pre-processing
Ontologies (OWL, RDFS)
(XML)
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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
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Preprocessing
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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)
Interface
Keyword « implements »
skos:broader
Statement
Conditional Statement
AssignmentStatement
skos:broader skos:broader
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Final result: Open Office-Writer
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Final result : MS-Word
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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.
Access rights
public protected private
Method modifiers
public protected private
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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
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1- Selection and analysis of
existing material
2 – Semi automatic annotation
3 – learning activity4 - Analysis
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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
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Semantic Web architecture
content(XHTML)
Pedagogical ontology
Domain vocabulary
Doc. model
(Skos) (OWL)(RDFS)CoreseSemantic Search Engine
XSLT
Queries(Sparql)
HTTP
web-app
Tomcat web server
Answers (Sparql-XML)
Interface(XHTML)Request
Learner
logs(RDF)
Formalized Knowledge
rules
1
2
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Semantic Web at work
• Dynamic SPARQL queries: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
Variable
LocalVariable
Definition
Example3
7
skos:primarySubjectskos:broader
skos:primarySubject
rdf:type
rdf:type
Layout information
edu:order
edu:order
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QBLS-1
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QBLS-2
Variable
Fields
Local variable
skos:broader
Human readable information
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Experimental results: students’ feedback
• Good satisfaction• Structured navigation appreciated for
direct access to information• Use of domain and pedagogical
information
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
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QBLS-ASPL(Advanced Semantic Platform for Learning)
• Existing resources on a portal : REASE,
• MS-PowerPoint files
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QBLS-ASPL
Interesting Web sites for advanced learners
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QBLS-ASPL
Provided by QBLS
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1- Selection and analysis of
existing material
2 – Semi automatic annotation
3 – Exploitation by learners
4 - Analysis
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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
Concept Resource Concept Resourcesubject of mentions subject of
User A Time t
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Visualization
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Visualization
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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 ?conceptOPTIONAL { ?v2 edu:resourceVisited ?aux ?v2 edu:user ?user}FILTER(! bound(?v2))}
Object
Def Ex.
?v
?v2
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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”?
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ConclusionLearning Object Repositories
LOM standard
Scorm?
Learning Design
Adaptive hypermedia
Annotation tools
Linguistic analysis
Learner modeling
Activity tracking
Semantic Web = valid connector
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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”
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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
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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)
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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)
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Acknowledgements
• Catherine Faron-Zucker
• Jean Paul Stromboni
• Peter Sander