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Ontology-Based Referencing of Actors, Operation and Resources in eLearning

Systems

Ontology-Based Referencing of Actors, Operation and Resources in eLearning

Systems

byDr Gilbert Paquette

CIRTA-LICEF Research CenterTélé-université, Montréal

http://licef.teluq.quebec.ca/gphttp://licef.teluq.quebec.ca/gp

SWEL’04 Workshop – Einhoven, August 23, 2004

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Backround Work at CIRTA-LICEF

AGD

1992-19951992-1995

MISA 2.0MOT 2.0

MOT plus

1998-19991998-1999

1995-19971995-1997

1995-19971995-1997

MISA 3.0 Gen. soft

MISA 4.0 ADISA

AGDAdvisor

1997-19981997-1998

1999-20011999-2001

Explor@-1w. Advisor

Explor@-2(multi-actor)

TELOSModeler

2002-20042002-2004

TELOS PlayerMOT plus LD+Onto Editors

2002-20042002-2004

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Instructional Engineering

A methodology that supports the analysis, the design and the delivery planning of a learning system, integrating the concepts, the processes and the principles of instructional design, software engineering and cognitive

engineering

InstructionalDesign

Software

EngineeringKnowled

ge

Engineering

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MISA 4.0 Method

Knowledge Modeling

210 Knowledge modeling principles212 Knowledge model214 Target competencies310 Learning units content 410 Learning instruments content 610 Knowledge and competency management

Instructional Modeling

220 Instructional principles222 Learning events network224 Learning units properties320 Instructional scenarios322 Learning activities properties420 Learning instruments properties620 Actors and group management

Materials Modeling

230 Media principles 330 Development infrastructure 430 Learning materials list 432 Learning materials models 434 Media elements 436 Source documents630 Learning system / resource management

Delivery Modeling

240 Delivery principles242 Cost-benefit analysis340 Delivery planning440 Delivery models442 Actors and user’s materials444 Tools and telecommunication446 Services and delivery locations540 Assessment planning640 Maintenance / quality management

Problem definition

100 Training system102 Training objectives

104 Target Learners106 Actual situation

108 Reference documents

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MISA 4.0 Central Tasks

Knowledge Modeling

Axis222

Learning Events Network

212 Knowledge

Model

214 Actors

Competencies

310 Learning

Units Content 410 Learning

Instruments Content

320 Instructional

Scenarios

Instructional Modeling

Axis

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MOT Graphic LanguageCONCEPTS PROCEDURES PRINCIPLES

C

S

P

I/P

R

I

LINKS

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Learning Events and Scenarios

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IMS-Learning Design Information Model

B

C

B

C

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Graphic Representation of a Graphic Representation of a LDLD

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A Simple IMS-LD Method

C

C

Module A(For

beginners)

Activity 2Make a

synthsis

Activity 4Present a

Systhesis on GazProperties

Activity 5Classify Gases bytheir presence in

AgricultureDomain and

methods

C

C

Act 2Analyze GasProperties

Activity 1Explore theAgriculture

Domains andMethods

CC

Course onEcologicalAgriculture

Act 1Synthetize

domains andmethods

P

Activity 3Evaluate and

discusssynthesis

C

Module B(For agriculture

expert)

C

Persons, resourcesfor the

Classification ofAgriculture Domains

and Methods

IP

IP IP

IP

IP

Synthesis

ClassificationTable

IP

LectureNotes andWeb Sites

ValidatedSynthesis

LooselyAggregatedInformation

IP

IP IP

IP

C R

Role 2:Group ofLearners

R

Role 2:Group ofLearners

Role 1:IndividualLearners

R

Role 3:Teacher

R

R

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Improve Knowledge Referencing

IMS-LD is a progress in eLearning specifications– Some IMS-LD scenarios cannot be expressed in

SCORM– IMS-LD is more general, favors innovative methods

Assigning optional objectives and prerequisites according to the IMS RDCEO specification (IMS 2002) is not enough– Consistency checking is not supported between levels nor

between the content of learning activities and resources, and the actors’ competency

– Knowledge in learning resources is not described – Actor’s knowledge and competencies is only indirectly defined

through educational objectives Need for a qualitative structural representation of

knowledge in activities, but also a quantitative one

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Explor@-II Delivery System

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Structured Competencies

To say that somebody needs to acquire a certain knowledge is insufficient

A skills’ taxonomy based on different viewpoints : instructional objectives, generic tasks/processes, meta-knowledge Expandable taxonomy from general to

specific Ordering skills from simple to complex Integrating domains of multiple

intelligence: cognitive, affective, social, psycho-motor

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An integrated skills An integrated skills taxonomytaxonomy

S

Identify S

Illustrate

Memorize

Utilize

S

S

S

Classify

Construct

Initiate/ Influence

Adapt/ control

S

S

S

S

Discriminate

Explicitate

SimulateDeduce

S

S

Predict

Diagnose

Induce

Plan

S

S

S SS

S

Exerce a skill

Receive

Reproduce

S

Create

Self- manage

S

S

1-Show awareness

S

9-Evaluate

S

4-Transpose

S

7-Repair

S

2-Internalize

S

3-Instantiate /Detail

S

5-Apply

S

6-Analyze

8-SynthesizeS

S

10-Self- manage

S

Generic skill Inputs Products

Simulate Process to simulate: inputs, products, sub-procedures, control principles

Trace of the procedure: set of facts obtained through the application of the procedure in a particular case

Construct Definition constraints to be satisfied such as target inputs, products or steps….

A model of the process: its inputs, products, sub-procedures each with their own inputs, products and control principles

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Skill/Performance Scale

Self-manage (10)

Evaluate (9)

Synthesize (8)

Repair (7)

Analyze (6)

Apply (5)

Transpose (4)

Interpret (3)

Identify (2)

Memorize (1)

Pay attention (0)

Peter M

.

Book X

Video Y.

Multimedia Production Method

Skills

Performance Aware Familiarized Productive Expert

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Representing Knowledge in Explor@-II

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A Multi Actor Workflow A Multi Actor Workflow InterfaceInterface

Essay

Produceessay

Analyze thevidéo

Video

Analysis

Coach

Forum

Profiles

Evaluateessay

Evaluation

Obtain

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Functions : Multi-Actor Functions : Multi-Actor ProcessesProcesses

Learning Design Learner’s assessment Knowledge processing Learning Object management Collaboration management Emerging activities management Delivery Models (Blended,

Communities of Practice, EPSS,…)

…..

A function is a multi-actor process graph…part of a delivery model represeting…a use-case of the TELOS delivery system…and a physiology of the DLS

A function is a multi-actor process graph…part of a delivery model represeting…a use-case of the TELOS delivery system…and a physiology of the DLS

In LORNET, a TELOS LKMS is an aggregation of FUNCTIONS

In LORNET, a TELOS LKMS is an aggregation of FUNCTIONS

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A Graphic OWL Editor

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A Simple Ontology

C

ChemicalFertilizers

S

RiceProductionProcesses

I

I

I

RR Produce

RR Produce

CarbonDioxyde

S

Gases

S

R

Fertilizers

R

R

R

NitricOxyde

HasInputs

MethaneHas

Outputs

I

RR

RR

HasOutputs

HasInputs

SS

TraditionalRice

Production

AgriculturalPractices

GreenhouseGases

THINGS

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Referencing with an Ontology

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Skill/Performance Scale

Self-manage (10)

Evaluate (9)

Synthesize (8)

Repair (7)

Analyze (6)

Apply (5)

Transpose (4)

Interpret (3)

Identify (2)

Memorize (1)

Pay attention (0)

Peter M

.

Book X

Video Y.

Multimedia Production Method

Skills

Performance Aware Familiarized Productive Expert

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Competency Equations

CC

C

C

Act 5

P

P

P

Activity 5.4

Activitiy 5.1

Activity 5.2

IP

IP

Productresource

Inputresource

Inputresource

Activity 5.3

R

TrainerLearner

IP

R

Components of a Function Components of a Function must reach competence must reach competence equilibrium . equilibrium . Ex: Learning resources (persons, Ex: Learning resources (persons, documents and tools) must enable documents and tools) must enable learners to progress from an entry learners to progress from an entry level to a target level required by the level to a target level required by the activity.activity.

Components of a Function Components of a Function must reach competence must reach competence equilibrium . equilibrium . Ex: Learning resources (persons, Ex: Learning resources (persons, documents and tools) must enable documents and tools) must enable learners to progress from an entry learners to progress from an entry level to a target level required by the level to a target level required by the activity.activity.

7.4

TC:7.4

TC: TC:7.4

EC:6.4

TC:7.4

TC:5.2

EC:5.2

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Referencing PrinciplesReferencing Principles

1. Tree organization of the knowledge referential: – allows competence inheritance from parent node to children– reduce significantly the mechanisms of competence

analysis and management. 2. Must be completed by relational logic to sustain

more refined mechanism of conceptual matching. 3. Ontology referencing is insufficient without mastery

levels– prevent coarse granulation of sense– weak semantic management services.

4. Quantitative measures to weight ability on knowledge – level scale to be reasonably simple, manageable – levels correspondomg to clearly identify cognitive processes– to be materialized as a learning design

1. Tree organization of the knowledge referential: – allows competence inheritance from parent node to children– reduce significantly the mechanisms of competence

analysis and management. 2. Must be completed by relational logic to sustain

more refined mechanism of conceptual matching. 3. Ontology referencing is insufficient without mastery

levels– prevent coarse granulation of sense– weak semantic management services.

4. Quantitative measures to weight ability on knowledge – level scale to be reasonably simple, manageable – levels correspondomg to clearly identify cognitive processes– to be materialized as a learning design

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LORNET Research Network

A major Canadian Initiative in A major Canadian Initiative in Learning and Semantic Web Learning and Semantic Web

technologiestechnologies 8 M $ in 5 years on learning and knowledge-based

technologies Joint research with 27 industrial partners and

organizations A team of 129 researchers, students and

professionals. Consolidation of 7 research labs and centers and 4

Research Chairs Convergence of complementary research areas Interaction with leading international projects: OKI,

IMS, CETIS, COLIS, Kaleidoscope

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Future Research in Future Research in LORNETLORNET

New knowledge– On Semantic Web techniques and applications

– On ICT support to Knowledge Management and training

– On learning objects creation, processing and use

Tools and methods– For ontologies creation and use

– For design, static and dynamic aggregation of LOs

– For content repurposing and adaptive assistance

– For knowledge extraction to model learning objects

– For advanced multimedia creation, search, delivery

Systems– TELOS : Telelearning Operating System

– 5 LKMS and LKMA: Learning and Knowledge Management Design and Delivery systems and application

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Ontology-Based Referencing of Actors, Operation and Resources in eLearning

Systems

Ontology-Based Referencing of Actors, Operation and Resources in eLearning

Systems

byDr Gilbert Paquette

CIRTA-LICEF Research CenterTélé-université, Montréal

http://licef.teluq.quebec.ca/gphttp://licef.teluq.quebec.ca/gp

SWEL’04 Workshop – Einhoven, August 23, 2004