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TICL-08 Symposium, New-York, March 25 2007 Ontology Modeling for Comptency-Based Learning Environments Gilbert Paquette Director of the CICE Canada Research Chair LICEF Research Center, Télé-université www.licef.teluq.uquebec.ca/gp. Plan. Backround: Knowledge-based ID and Semi-formal modeling - PowerPoint PPT Presentation
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TICL-08 Symposium, New-York, March 25 2007
Ontology Modeling for Comptency-Based Learning
Environments
Gilbert Paquette
Director of the CICE Canada Research Chair LICEF Research Center, Télé-université
www.licef.teluq.uquebec.ca/gp
Plan
1. Backround: Knowledge-based ID and Semi-formal modeling
2. Competencies for Structuring KB Learning Environments
3. Competencies as Meta-processes and Strategies for Learning Scenarios
4. Ontology for Referencing Resources
5. Activity Assistance based on Meta-process Principles and Domain ontology
6. Knowledge Representation Principles
1- Background
MOT +
MOT 2.0
1995-19971995-1997
AGD
1992-19951992-1995
MISA 3.0
MISA 2.0
1995-19971995-1997
MISA 4.0
MISA LD
MISA forms
1997-19981997-1998
ADISA/Explor@
1999-20021999-2002
TELOSScenario Ed.Ontology Ed.
2006-20072006-2007
1998-19991998-1999
MOT+LD
2004-20052004-2005
MOT+OWL2005-20062005-2006
ID MethologyModeling Tools
eLearningSystems
Use in Instructional Engineering (MISA)
640 Maintenance/Quality Management
630 Learning System/Resource Management
620 Actors and Group Management
610 Knowledge/ Competency Management
Phase 6 – Phase 6 – Delivery PlanDelivery Plan
540 Test Planning 542 Revision Decision LogPhase 5 – Val.Phase 5 – Val.
440 Delivery Models
442 Actors and their resources
444 Tools and Telecom446 Delivery Services
and Locations
430 Learning Resource List
432 Media Models 434 Media Elements 436 Source Documents
420 Learning Resource Properties
410 Learning Resource Content
Phase 4 –Phase 4 –Detailed Detailed DesignDesign
340 Delivery Planning330 Development Infrastructure
320 Learning Scenarios
322 Activity Properties
310 Learning Unit Content
Phase 3 –Phase 3 –ArchitectureArchitecture
240 Delivery Principles 242 Cost-Benefit
Analysis
230 Media Principles220 Instructional Principles
222 Event Network224 Learning Unit
Properties
210 Knowledge Model Orientation Principles
212 Knowledge Model
214 Competencies
Phase 2 – Phase 2 – Initial solutionInitial solution
Delivery AxisDelivery AxisMedia AxisMedia AxisPedagogy Pedagogy AxisAxis
Knowledge Knowledge AxisAxis
100 Organization’s Training System 102 Training Objectives 104 Learners’ properties106 Present Situation 108 Reference Documents
Phase 1- Phase 1- DefinitionDefinition
Taxonomy of knowlege models
KnowledgeModels
Factual Models
Set of Examples
Set of Statements
Conceptual Models
Typologies
Component Systems
Hybrid Conceptual
Systems
Procedural Models
Series Procedures
Parallel Procedures
Iterative Procedures
Prescriptive Models
Norms and Constraints
Laws and Theories
Decision Trees
Control Rules
Processesand Methods
Processes
Methods
Multi-actorworkflows
Set of traces
Goals for a RepresentationLanguage
Transparent semantic to facilitate design and Transparent semantic to facilitate design and communication at an informal levelcommunication at an informal level
Integrated representation for Concept Maps, Flow Integrated representation for Concept Maps, Flow Charts, Decision Trees and others. Charts, Decision Trees and others.
Generality : domains, types of models, granularity, Generality : domains, types of models, granularity, higher level knowledgehigher level knowledge
From Semi-formal to Formal RepresentationFrom Semi-formal to Formal Representation
Informal Semi-formal FormalWritten-Oral
CommunicationUML Diagram
MOT KnowledgeModels
Conceptual graphsOntologies (MOT+OWL)Rules and Constraints
MOT+OWL: A Formal Graphic Ontology Editor
RelationalProperty
owl:Class3> <owl:intersectionOf rdf:parseType="Collection">List of class descriptions </owl:intersectionOf></owl:Class3>
Class intersection x: Class3(x) Class1(x) Class2(x)
2- Knowledge Management:Enhancing Human Competency
Goal: knowledge and competency sharingCompetency implies higher level knowledge apply to domain knowledgeStructured competencies: knowledge, skills/attitude and performance/context of use.
COMPETENCY
1. Knowledge 2. Generic Skill3. Performance Context
C CC
Select in a domain ontology
I/P
Select in a Skill’s taxonomy
CombinePerformance/ context
criteria
I/P I/PScale position
C C
Combining viewpoints : instructional objectives (Bloom) generic tasks (Chandrasekaran) meta-knowledge (Pitrat)
Generic Skills Taxonomy
Identify
Illustrate
Memorize
Utilize Classify
Construct
Initiate/ Influence
Adapt/ control
Discriminate
Explicitate
SimulateDeduce
Predict
Diagnose
Induce
Plan
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
3- Generic Simulation Strategy
(5) Simulation
meta-process
Produce examples of the input concepts
Identify the next applicable
procedure
Execute the procedure using its
execution principles
Assemble the simulation
trace
Description of the process to be
simulated
Inputs to the simulated
process
Products of the procedure
Simulation trace of
the procedure
Execution principles of
the simulated procedure
More procedures to execute
No more procedures to
execute
I/P I/P
I/P
I/P
I/P
P
P P
P
I/P
I/P
C
C C
C
p
I/P
I/P
DescriptionPrinciples
PresentationPrinciples
ExamplegenerationPrinciples
Procedureidentification
Principles
CompletenessPrinciples
R
RR
R
R
I/P
C
Assisted Simulation Scenario(Multimedia Production Domain)
DesignerCase studies for a method to select
procedures
Interactions on examples processed by
learners
Text presenting examples of simulations
I/P
I/P
I/P
Prepare learning materials
RI/P
I/PI/P
Content expert
Learner/expert
Interactions
I/P
Interact by email
R
I/P
R
Trainer
Presentation and discussion of completeness
principles
FAQ onpresentation
norms
I/P
I/P
Use a forum software
Maintain a FAQI/P
R I/P
Activity 1: Choose a MM
process tosimulate
Activity 2: Choose a typical
multimedia project
Activity 3: Identify a MM
production task
Activity 4: Execute a
production task
Activity 5: Verify is the
process is complete
Activity 6: Produce a project report on
the MM process
Assistance agent
Learner/Agent
Interactions
I/P
Interact in scenario
R
I/P
5- Assistance Methodology1.1. Define target competencies: generic Define target competencies: generic
process and domain knowledge ontologyprocess and domain knowledge ontology2.2. Define executable scenario (task structure of Define executable scenario (task structure of
the host environment) the host environment) 3.3. Add assistance objects to critical tasksAdd assistance objects to critical tasks4.4. Integrate assistance: for each critical task Integrate assistance: for each critical task
define product attributes and progression define product attributes and progression levelslevels
5.5. Define conditions and actions based on the Define conditions and actions based on the relation between input knowledge and relation between input knowledge and product attributeproduct attribute
Assistancetree
Tasktree
Input-Outputs
6. Properties of the Knowledge Representation Paradigm
GraphicGraphic. Reduce ambiguity by the use of . Reduce ambiguity by the use of standardized objects and links.standardized objects and links.User-friendliness.User-friendliness. Typed links are preferred, Typed links are preferred, not two few nor two many types of links, clear not two few nor two many types of links, clear semantic.semantic.General.General. Capacity to represent knowledge in Capacity to represent knowledge in very different subject domains, at various levels very different subject domains, at various levels of granularity and precisionof granularity and precisionFormalizableFormalizable. Upward compatible from informal . Upward compatible from informal graphs, up to semi-formal and totally graphs, up to semi-formal and totally unambiguous formal models.unambiguous formal models.
Properties of the Knowledge Representation Paradigm (cont’d)
DeclarativeDeclarative. Separates knowledge from their . Separates knowledge from their processing. Describe processing knowledge processing. Describe processing knowledge declaratively, so that higher order meta-declaratively, so that higher order meta-knowledge, applies to specific knowledge.knowledge, applies to specific knowledge.
StandardizedStandardized. To enlarge communication . To enlarge communication between persons and/or software agents.between persons and/or software agents.
Computable.Computable. Formal representation that can be Formal representation that can be processed by computer agents, in a complete processed by computer agents, in a complete and decidable way (e.g. OWL-DL).and decidable way (e.g. OWL-DL).