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

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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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56

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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

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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