Competencies: Ontologies and Development Process By Gilbert Paquette Chaire de recherche sur l’ingénierie cognitive et éducative (CICE) Centre de recherche LICEF, Télé-université ShareTec Worshop Venitia, January 21-24, 2009

1- Background MOT + MOT AGD MISA 3.0 MISA MISA 4.0 MISA LD MISA forms ADISA/ TELOS Scenario Ed. Ontology Ed MOT+LD+OWL ID Methodology Modeling Tools ID Support Systems

Method for Knowledge-based Instructional Engineering (MISA) 640 Maintenance/Qua lity Management 630 Learning System/Resource Management 620 Actors and Group Management 610 Knowledge/ Competency Management Phase 6 – Delivery Plan 540 Test Planning 542 Revision Decision Log Phase 5 – Val. 440 Delivery Models 442 Actors and their resources 444 Tools and Telecom 446 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 – Detailed Design 340 Delivery Planning330 Development Infrastructure 320 Learning Scenarios 322 Activity Properties 310 Learning Unit Content Phase 3 – Architecture 240 Delivery Principles 242 Cost-Benefit Analysis 230 Media Principles220 Instructional Principles 222 Event Network 224 Learning Unit Properties 210 Knowledge Model Principles 212 Knowledge Model 214 Competencies Phase 2 – Initial solution Delivery Axis Media Axis Pedagogy Axis Knowledge Axis 100 Organization’s Training System 102 Training Objectives 104 Learners’ properties 106 Present Situation 108 Reference Documents Phase 1- Definition Phase 1- Definition

Why are we interested in Competencies ? Competencies are essential to Competencies are essential to –Set educational objectives –Construct ePortfolios and User models –Select resources in large ressource repositories. –Personalize learning during delivery –Improve learning environments after delivery. Improve semantic annotation in OWL format Improve semantic annotation in OWL format Improve competency standards: Ex: IMS-LD /RDCEO Improve competency standards: Ex: IMS-LD /RDCEO Improve instructional engineering methodology and visual tools Improve instructional engineering methodology and visual tools

Competency Standards for Trainers (IBSPI)

A Sample of the IBSTPI Instructor Competency Profile (18) Professional Foundations (4) Professional Foundations (4) Planning and Preparation (3) Planning and Preparation (3) Instructional Methods and Strategies (8) Instructional Methods and Strategies (8) –Competency 7 : Stimulate and sustain learner motivation and engagement (a ) Gain an maintain learner attention (a ) Gain an maintain learner attention (b) Ensure that goals and objectives are clear (b) Ensure that goals and objectives are clear (c) Foster a favorable attitude toward learning (c) Foster a favorable attitude toward learning (d) Establish relevance to increase learner motiiation (d) Establish relevance to increase learner motiiation (e) Help learners set realistic expectations (e) Help learners set realistic expectations (f) Provide opportunities for learners to participate and succeed (f) Provide opportunities for learners to participate and succeed Assesment and Evaluation (2) Assesment and Evaluation (2) Management (2) Management (2) Level 1 Level 2 Level 3 Domain Competency Performance

Need for a Structural Definition  To say that somebody needs to acquire a certain knowledge is insufficient. What should he be able to do with it ? Knowledge + Generic Skill + Attitude  Most models use natural language statements involving actions on knowledge  Competency statements are most of the time ambiguous and difficult to use  Need to structure competencies: knowledge, skills/attitude, performance context  A generic skills’ taxonomy combining viewpoints : instructional objectives, generic tasks/processes, meta-knowledge

2- Competency Ontology (the basic Concept) COMPETENCY 1. Knowledge 2. Generic Skill/Attitude 3. Performance Context C C C Select in a domain ontology I/P Select in a generic skill’s taxonomy Combined performance context criteria I/P Scale position C C Electrical system of a car Diagnosis Autonomously in simple situtations « Diagnose the electrical system of a car autonomously in simple situations »

Generic Skills Taxonomy Identify Illustrate Memorize Utilize Classify Construct Initiate/ Influence Adapt/ control Discriminate Explicitate Simulate Deduce 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-Synthesize S S 10-Self- manage S Generic skill Inputs Products SimulateProcess 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 ConstructDefinition 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

Meta-processes associated to each Generic Skill (5) Simulation meta-process Description of the process to be simulated Simulation trace of the procedure I/P Produce examples of the input concepts Identify the next applicable procedure Execute the procedure using its execution principles Assemble the simulation trace C C C C Inputs to the simulated process Products of the procedure Control Structure (execution principles) More procedures to execute No more procedures to execute I/P P P P P P

Applies to various stimulus/product domains 7Repair Replace faulty components in a damage system Correct mouvements in a choreography Modify one’s attitude in a tense situation Change classroom activities to foster collaboration 8Synthesize Construct a taxonomy with give examples Invent a new way to draw a figure Develop a new attitude towards a dangerous situation Find a new way to change the climate in a group Cognitive Psycho- motor Affective Social DOMAINS

CRITERIA Performance Context Frequency Scope Autonomy Complexity Context Familiarity 2.5 – 5.0 Always Partial With assistance Weak Familiar Expertise Always Total Without help High Unfamiliar PERFORMANCE LEVELS Awareness 0.0 – 2.5 Sometimes Partial With assistance Weak Familiar Mastery 5.0 – 7.5 Always Partial Without help Middle Familiar

An Ontology for Competencies (Using MOT+OWL or MOWL)

3- Competency Development Process (Example from the Bar of Quebec)

Knowledge Modeling

IdCompetency Table Priority Initial Gap A1(6) Analyze applicable law texts, without help, to new situations with middle or high complexity. 1(2)4 A2(6) Analyze applicable jurisprudence with some help in complex situations. 1(2)4 A3(3) State applicable rules of law in any situation2(1)2 A4(5) Apply appropriate civil rights elements without help in famliar situation and middle complexity. 1(2)3 … Competency Profile - Distribution GapCompetenciesCourse 1 Course 2 Course 3 Course A3, A4, A5, A6, A7, A8, B4, C2, C6 x 4-5 A1, A2, A9, A10, B1, B3, C1, C3, C5, D2, D5, D6, E1, E2, E3, E8 xx 6-7 C4, D1, D3, D4, E9xxx 8 B2, E4, E5, E6, E7xxxx

Competency-Based Scenario Building

4- Competency Uses Assessing competency Assessing competency Selecting appropriate resources in a repository Selecting appropriate resources in a repository Analyzing competency gap Analyzing competency gap Guide Knowledge Modeling Guide Knowledge Modeling Designing competency-based scenarios Designing competency-based scenarios Competency annotations to guide designs Competency annotations to guide designs

Competency Assessment

Selecting Resources for a User Self-manage (10) Evaluate (9) Synthesize (8) Repair (7) Analyze (6) Apply (5) Transpose (4) Interpret (3) Identify (2) Memorize (1) Pay attention (0). Multimedia Production Method Skills Performance Aware Familiarized Productive Expert Peter M 8.4 Video Y. 6.9 Book X

Competency Gap Analysis PERFORMANCE CRITERIA Awareness (0,0-2,5) Familiarity [2,5-5.0) Mastery [ ) Expertise [7,5-10,0] FrequencySometimesAlways ScopePartial Total AutonomyAssistance Without help Task complexityLow MiddleHigh Context of useFamiliar Unfamiliar Competency scale– For each target population State : AwarenessFamiliarityMasteryExpertise Value :02,557,510 A : Construct a multimedia production method B : Discriminate between audio- visual supports C : Simulate a production process D : Plan a project definition

Scenario Design from a Generic Skill Model Designer Case studies for a method to select procedures Interactions on examples processed by learners Text presenting examples of simulations I/P Prepare learning materials R I/P Content expert Learner/ expert Interactions I/P Interact by R I/P R Trainer Presentation and discussion of completeness principles FAQ on presentation norms I/P Use a forum software Maintain a FAQ I/P R Activity 1: Choose a MM process to simulate 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 Activities in the scenario « Simulate a multimedia production process » Correspondence in the generic skill’s process « Simulate any process » Activity 1: Choose a multimedia development process to simulate Inputs to the generic process, defining the case to be simulated Activity 2: Choose a typical multimedia projectFirst applicable procedure to execute Activity 3: Identify a multimedia production taskNext applicable procedure to execute Activity 4: Execute the multimedia production taskExecute the chosen procedure Activity 5: Examine if the process is complete; if so, proceed to activity 6; if not, repeat activities 3 and 4 Decision to continue or not Activity 6: Summarize your simulation processAssemble the trace

Competency-based Semantic Annotation Compare planets by mass autonomously and totally Compare planets by mass autonomously and totally Compare planets by orbital period autonomously and toally Compare planets by orbital period autonomously and toally Analyze, deduce properties of objects (here Planet name and orbital duration)

Any Questions? Chaire de recherche sur l’ingénierie cognitive et éducative (CICE) Centre de recherches LICEF, Télé-université IMS Conference – TEST FEST PennState, November 5, 2008