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Montréal, April 14 2005

IEEE Montreal - Education Society. Modeling Languages for Instructional Engineering _________________________________ Dr Gilbert Paquette CICE Research Chair LICEF-CIRTA, Télé-Université. Montréal, April 14 2005. Plan. Major International Trends

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Montréal, April 14 2005

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  1. IEEE Montreal - Education Society Modeling Languages for Instructional Engineering_________________________________Dr Gilbert PaquetteCICE Research ChairLICEF-CIRTA, Télé-Université Montréal, April 14 2005

  2. Plan • Major International Trends • Building High Quality Learning System Larger set of decisions • Instructional engineering and Educational modeling languages • Instructional Engineering principles

  3. Internet’s Third Wave • First wave: STATIC TEXT and DATA EXCHANGE • Primarily static HTML pages: text and data services • E-mail, FTP, forum (asychronous communication) • Second wave: DYNAMIC MULTIMEDIA • Dynamic pages, ASP, JSP • Facilitated transfer of images, sound and video • Desktop audio/video conference • Third wave: SEMANTIC and PROGRAMMABLE • Integration of applications, API enabled by web services • Ex: Google Alerts, Amazon’s API • Distributed computing, P2P, and mobile networking • Semantic web and knowledege-based technologies • Open source, sharing of objects and applications • New generation of Distributed Learning Environments

  4. Ontology The Semantic Web • Metadata describes resources • Ontology describes domain knowledge • Metadata linked to knowledge domain • Ontologies enable computer agents to process meaningful information (not only syntax) • Facing exponential information growth on the Internet by associating meaning to Web pages

  5. Metadata Repositories Resource Repositories A Network Reusable Resources

  6. Media Elements Learning Objects Units of Learning Tools Documents Actors Learning Objects (more than just Web pages) Process Scenarios

  7. Faire l ’ingénierie d ’un SA Système d ’apprentissage Diffuser le SA I/P I/P I/P C I/P Réaliser le matériel C C C Services et milieux C C I/P Modèle pédagogique Mettre en place le SA Modèle médiatique Ressources technologiques I/P Modèle de diffusion Modèle des connaissances Components of a Learning System C C C Devis du SA Matériels pédagogiques Infrastructure du SA

  8. Delivery of a Learning System

  9. Plan • Major International Trends • Building High Quality Learning SystemLarger set of decisions • Instructional engineering and Educational modeling languages • Instructional Engineering principles

  10. Self-management of learning Competency-focused, Open/Adaptable Learning Scenarios; Metacognitive Tools

  11. Information Selection Process-guided Information Retrieval Search, Annotation andRebuilding tools

  12. Learner Collaboration Process Oriented; Collaboration model; Visualization & management tools

  13. Assistance from facilitators Caring Heuristic assistance;Learner’s Initiative; Multiple Facilitators

  14. Larger set of decisions What kind of TeleLearning model? Anywhere, anytime, anyplace, anybody? What kind of interactivity, collaboration? Predefined multi-path or learner-constructed scenarios? Which actors, what roles, what resources? Multimedia or pluri-media materials? Reuse or build How to manage resources; Achieve Interoperability and scalability? How to take in account techno-diversity? Reusability, Sustainability, Affordability? ?

  15. Plan • Major International Trends • Building High Quality Learning System Larger set of decisions • Instructional engineering and Educational modeling languages • Instructional Engineering principles

  16. AGD 1992-1995 1995-1997 AGD Conseiller MOT 2.0 MISA 2.0 1995-1997 1997-1998 MISA 3.0 Gen. soft MOT plus Explor@-1 +Advisor 1998-1999 1999-2001 MISA 4.0 ADISA Explor@-2 (multi-actor) 2002-2005 2002-2005 MOT plus LD Editor TELOS (1) Player ADISA2 LICEF Tools & Method History

  17. Problem definition 100 Training system 102 Training objectives 104 Target Learners 106 Actual situation 108 Reference documents Instructional Modeling Knowledge Modeling 210 Knowledge modeling principles 212 Knowledge model 214 Target competencies 310 Learning units content 410 Learning instruments content 610 Knowledge and competency management 220 Instructional principles 222 Learning events network 224 Learning units properties 320 Instructional scenarios 322 Learning activities properties 420 Learning instruments properties 620 Actors and group management Delivery Modeling Materials Modeling 240 Delivery principles 242 Cost-benefit analysis 340 Delivery planning 440 Delivery models 442 Actors and user’s materials 444 Tools and telecommunication 446 Services and delivery locations 540 Assessment planning 640 Maintenance / quality management 230 Media principles 330 Development infrastructure 430 Learning materials list 432 Learning materials models 434 Media elements 436 Source documents 630 Learning system / resource management MISA 4.0 Method

  18. Instructional Engineering Actors • Content Experts • Instructional Designers • Media Designers • Delivery Specialists • Team Leader

  19. Educational Models • Knowledge and Competency Representation • Scenarios, Learning Designs, Learnflow Representations • Media Structure Representation • Delivery Model Represenations

  20. Desired Properties of a GraphicRepresentation Formalim • Simplicity and User Friendliness • Generality • Completeness • Translated to machine (XML) format • Communicable between humans

  21. L I N K S CONCEPTS PRINCIPLES PROCEDURES C S P I/P R I MOT Graphic Language

  22. Graphic Ontology Language (OWL)

  23. THINGS S S S Agricultural Has Gases Fertilizers Produce R R R R Practices Inputs S R Has S S R Outputs Greenhouse Rice Gases Production Chemical Processes Fertilizers I I I Has Carbon Nitric I Produce R R R Inputs Dioxyde Oxyde R A Certain Rice Production R Has Methane R Outputs A Graphic OWL Editor

  24. Structured Competencies • To say that somebody needs to acquire a certain knowledge is insufficient • What kind of generic skill + performance? • A generic skills’ taxonomy based on different viewpoints : instructional objectives, generic tasks/processes, meta-knowledge • Competency = Meta-process (skill) applied to a knowledge at a certain level of performance • Situate knowledge acquisition goals on a competency/performance scale

  25. Building Competency Models

  26. Scenario X Peter M Video Y . Skill/Performance Scale Skills Multimedia Production Method Self-manage (10) Evaluate (9) Synthesize (8) Repair (7) Analyze (6) Apply (5) Transpose (4) Interpret (3) Identify (2) Memorize (1) Pay attention (0) . Performance Aware Familiarized Productive Expert

  27. Knowledge anc CompetencyDistribution in LDs Module 1 Module 2 Module 3 Course

  28. Instructional Scenario (IMS-LD)

  29. Versailles ACT1: VERSAILLES Simulation ACT8: REFLECT ON OVERVIEW TREATY OUTCOMES C C P C P Play ACT2: C C INTRODUCTION TO ACT7: REVIEW PREPARATORY MAIN PHASE NEGOTIATION DAY C C P P ACT3: C C BACKGROUND STUDY - OFFLINE ACT6: THE MAIN ACTIVITIES NEGOTIATIONS DISPLAYS RESULTS Send Results RETURNED TO THE P Returned results C LO P RECORDER ACT5: IP C ACT4: SIX NATION INTRODUCTION TO P ONLINE STRATEGY MAIN NEGOTIATION PREPARATION DAY Send Results Send Results Read Posted IP Env to Recorder Results C C France Negotiation Day C Main Main C Negotiation IP Negotiating France-Serbia France-Serbia USA-France Forum Chamber IP US-France C IP Forum Side-room Side-room Forum C C C GB-France GB-France IP Side-room Forum C I Poland-France Side-room France-Italy FRANCE-Serbia France-Serbia Side-room Side-room Confererence FRANCE-SERBIA IP IP Negotiate AD res IP I Poland-France France-Italy Forum France-Serbia Forum Forum I-France Serbia Confer SO Versailles Scenario

  30. Knowledge Referencing of LD Components

  31. The actor plays (is responsible for) a role (or an action) A R R Acteur Role C C A role has two components C C Sub-role 2 Sub-role11 Role An actor uses a ressource «Ressource». R I/P R Utiliser User ressource Resource An Actor produces a resource R I/P R I/P Provide a Provider Resource resource Delivery Components

  32. Model of an Evaluation Function O Présenteur A A de test A: Ingénieur O C: Apprenant évalué pédagogique Présentateur O de tests R Éditeur de R R R scénario R R E Stucturer Faire le bilan l'espace des résultats Tests Passer les d'évaluation complétés tests I/P I/P I/P E E O Tests évalués Définition des Éditeur de I/P R et transmis scénarios et des tests I/P activités I/P d'évaluation O R Visualisation Analyser et I/P E du bilan évaluer Tests dans la I/P Concevoir banque des R les tests matériels R D: A R A Formateur O B: Auteur Assistant à évaluateur la correction

  33. Model of a Trainer’s Roles

  34. Plan • Major International Trends • Building High Quality Learning System Larger set of decisions • Instructional engineering and Educational modeling languages • Instructional Engineering principles

  35. An eLearning system is an information system, a complex array of software tools, digitized documents and communication services, more diversified than in the past Follow some software engineering principles Information System’s Approach

  36. Knowledge-Based ISD The actual emphasis on knowledge management recognizes the importance of knowledge and higher order skills, as opposed to simple data or information acquisition • Knowledge engineering can support central tasks of ISD methods : content, activities, media and delivery processes

  37. Multi-Agent view An eLearningsystem at delivery time is a multi-agent society (modularity, sociability, distribution of control, message propagation) • An ISD method should identify clearly the actors , their roles and their interactions, together with the tools and resources that should compose their environment

  38. Process Based Learning Scenarios JUST IN TIME INFORMATION JUST IN CASE INFORMATION EVALUATE SYNTHETIZE ANALYZE APPLY UNDERSTAND MEMORIZE Process-based situated learning scenarios help guide information search and the construction of new knowledge

  39. In summary…. More systematic, structured and visual ISD. Knowledge engineering to support higher order knowledge and skills acquisition. Definition of multi-agent systems for useful interactions at delivery. Support to self-management of learning scenarios and environments for meta-cognition. Integration of multiple assistance agents into process-based scenarios: co-learners, SMEs, coaches, managers, FAQ, Intelligent Help Systems...

  40. IEEE Montreal - Education Society Modeling Languages for Instructional Engineering_________________________________Dr Gilbert PaquetteCICE Research ChairLICEF-CIRTA, Télé-Université Montréal, April 14 2005 www.licef.teluq.uquebec.ca/gp

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