Plan

1. TICL-08 Symposium, New-York, March 25 2007Ontology Modeling for Comptency-Based Learning Environments Gilbert PaquetteDirector of the CICE Canada Research Chair LICEF Research Center, Télé-universitéwww.licef.teluq.uquebec.ca/gp

2. Plan • Backround: Knowledge-based ID and Semi-formal modeling • Competencies for Structuring KB Learning Environments • Competencies as Meta-processes and Strategies for Learning Scenarios • Ontology for Referencing Resources • Activity Assistance based on Meta-process Principles and Domain ontology • Knowledge Representation Principles

3. 1997-1998 MISA forms 1999-2002 ADISA/ Explor@ 1- Background ID Methology Modeling Tools AGD 1992-1995 MOT 2.0 MISA 2.0 eLearning Systems 1995-1997 1995-1997 MISA 3.0 MOT + 1998-1999 MISA 4.0 MOT+OWL MOT+LD 2006-2007 2005-2006 2004-2005 MISA LD TELOS Scenario Ed. Ontology Ed.

4. Use in Instructional Engineering (MISA) Phase 1- Definition 100 Organization’s Training System 102 Training Objectives 104 Learners’ properties 106 Present Situation 108 Reference Documents Knowledge Axis Pedagogy Axis Media Axis Delivery Axis Phase 2 – Initial solution 210 Knowledge Model Orientation Principles 212 Knowledge Model 214 Competencies 220 Instructional Principles 222 Event Network 224 Learning Unit Properties 230 Media Principles 240 Delivery Principles 242 Cost-Benefit Analysis Phase 3 –Architecture 310 Learning Unit Content 320 Learning Scenarios 322 Activity Properties 330 Development Infrastructure 340 Delivery Planning Phase 4 –Detailed Design 410 Learning Resource Content 420 Learning Resource Properties 430 Learning Resource List 432 Media Models 434 Media Elements 436 Source Documents 440 Delivery Models 442 Actors and their resources 444 Tools and Telecom 446 Delivery Services and Locations Phase 5 – Val. 540 Test Planning 542 Revision Decision Log Phase 6 – Delivery Plan 610 Knowledge/ Competency Management 620 Actors and Group Management 630 Learning System/Resource Management 640 Maintenance/Quality Management

5. MOT Semi-formal Modeling

6. Taxonomy of knowlege models Set of Examples Set of traces Factual Models Set of Statements Typologies Component Conceptual Systems Models Hybrid Conceptual Series Systems Procedures Parallel Knowledge Procedural Procedures Models Models Norms and Iterative Constraints Procedures Laws and Prescriptive Theories Models Decision Trees Processes Control Rules Processes Methods and Methods Multi-actor workflows

7. Informal Semi-formal Formal Written-Oral Communication Conceptual graphs Ontologies (MOT+OWL) Rules and Constraints UML Diagram MOT Knowledge Models Goals for a RepresentationLanguage • Transparent semantic to facilitate design and communication at an informal level • Integrated representation for Concept Maps, Flow Charts, Decision Trees and others. • Generality : domains, types of models, granularity, higher level knowledge • From Semi-formal to Formal Representation

8. Class intersection x: Class3(x) Class1(x)  Class2(x) owl:Class3> <owl:intersectionOf rdf:parseType="Collection"> List of class descriptions </owl:intersectionOf> </owl:Class3> MOT+OWL: A Formal Graphic Ontology Editor

9. COMPETENCY C C C 3. Performance Context 1. Knowledge 2. Generic Skill C C Scale position I/P I/P I/P Combine Performance/ context criteria Select in a Skill’s taxonomy Select in a domain ontology 2- Knowledge Management:Enhancing Human Competency • Goal: knowledge and competency sharing • Competency implies higher level knowledge apply to domain knowledge • Structured competencies: knowledge, skills/attitude and performance/context of use.

10. Generic Skills Taxonomy Exerce a skill S Self- Receive manage S 1-Show S S awareness S S 10-Self- S manage 2-Internalize Reproduce Create Identify Initiate/ Influence 9-Evaluate S S Memorize 3-Instantiate S Adapt/ S S /Detail S Generic skill control Inputs Products 8-Synthesize 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 Illustrate 4-Transpose 6-Analyze 5-Apply 7-Repair Construct Discriminate Deduce Diagnose Plan 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 Simulate Induce Explicitate Classify Predict Utilize Combining viewpoints : • instructional objectives (Bloom) • generic tasks (Chandrasekaran) • meta-knowledge (Pitrat) S

11. Presentation Principles Description Principles (5) Description of Simulation trace of Simulation the process to be I/P I/P the procedure R meta-process simulated R I/P Completeness Principles C I/P C Assemble Execution the simulation I/P C C principles of Produce trace R the simulated examples of the procedure input concepts Example generation Principles Execute the Identify the procedure using its next applicable P execution procedure P I/P principles R R I/P Products of the P procedure P Inputs to the Procedure identification Principles simulated p No more More process procedures to procedures execute to execute 3- Generic Simulation Strategy C I/P I/P I/P

12. Content expert Assistance R Interact by agent Case studies for a email R Designer Interact in method to select Activity 1: I/P scenario procedures I/P R Choose a MM I/P process to simulate Learner/ I/P Prepare expert Interactions learning Interactions Learner/ on examples materials Agent Interactions processed by Activity 2: I/P I/P learners I/P Choose a typical I/P multimedia Activity 6: Produce I/P project a project report on Text presenting Activity 4: examples of the MM process I/P I/P Activity 3: Execute a simulations Identify a MM production task FAQ on presentation norms production task Trainer Activity 5: R Verify is the R I/P I/P process is Presentation and complete discussion of Maintain a Use a forum I/P FAQ completeness software principles Assisted Simulation Scenario(Multimedia Production Domain)

13. 4- Referencing Resources with Ontologies

14. Assistance tree Task tree Input- Outputs 5- Assistance Methodology • Define target competencies: generic process and domain knowledge ontology • Define executable scenario (task structure of the host environment) • Add assistance objects to critical tasks • Integrate assistance: for each critical task define product attributes and progression levels • Define conditions and actions based on the relation between input knowledge and product attribute

15. 6. Properties of the Knowledge Representation Paradigm • Graphic. Reduce ambiguity by the use of standardized objects and links. • User-friendliness. Typed links are preferred, not two few nor two many types of links, clear semantic. • General. Capacity to represent knowledge in very different subject domains, at various levels of granularity and precision • Formalizable. Upward compatible from informal graphs, up to semi-formal and totally unambiguous formal models.

16. Properties of the Knowledge Representation Paradigm (cont’d) • Declarative. Separates knowledge from their processing. Describe processing knowledge declaratively, so that higher order meta-knowledge, applies to specific knowledge. • Standardized. To enlarge communication between persons and/or software agents. • Computable. Formal representation that can be processed by computer agents, in a complete and decidable way (e.g. OWL-DL).

17. TICL-08 Symposium, New-York, March 25 2007Ontology Modeling for Comptency-Based Learning Environments Gilbert PaquetteDirector of the CICE Canada Research Chair LICEF Research Center, Télé-universitéwww.licef.teluq.uquebec.ca/gp