1. Theme 2 - Presentation Aggregation and Multi-Actor Designs i2LOR-06 Conference, Montreal November8-10 2.1 Workflow models to provide multi-actor interactions (O. Marino) 2.2 Active Learning Design for on-line real-time applications (H. Saliah) 2.3 Knowledge representation of actors, events and resources (G.Paquette) 2.4 Actors’ adaptive assistance (A. Dufresne) 2.5 Functional aggregation of theme 2 components (G.Paquette) Aude Dufresne, Olga Marino, Gilbert Paquette and Hamadou Saliah-Hassane Projects

2. Project 2.1: Aggregation and Design of Multi-actors Learning Flows Project leader: Olga Marino, PI: Gilbert Paquette Researchers: Karin Lundgen-Cayrol, Michel Leonard, Ileana de la Teja Ph.D. Students: Dario Correal Ms. Student: Alandre Magloire Collaboration: Anis Masmoudi

3. 2.1 Upgrades to the MOT+LD Editor

4. 2.1 MOT+LD Specification for Levels B and C

5. Study and comparison of 4 workflow engines: –WFMopen, –BPEL from Oracle, –JBPM on JBOSS, –OMG workflow API Comparative study of learnflow and workflow meta-models –IMS-LD –XPDL Comparison Aspects –static & dynamic domains –Control flow –Actor Representation –Knowledge Referencing 2.1 State of the art in workflow / learnflow models, engines and languages

6. 2.1 Specification of a generic multi-actor function editor Based on a subset of BPMN Taking into account workflow patterns Taking into account main ressource patterns =» projection of model elements into IMS-LD High[ ] Medium-High [ ] Medium-Low[ ] Low[ ]Priority  A student has the option to select one and only one activity among two possible ones. After finishing this learning activity a support activity starts its execution. IMS-LD Example Only one incoming path was executedUse Diagram “A point in the workflow process where two or more alternative branches come together without synchronization. It is an assumption of this pattern that none of the alternative branches is ever executed in parallel...”[2] Description Simple MergePattern

7. 2.1 Learnflow support using a model driven approach* Meta-model édition, visualisation and manipulation (for IMS-LD level A) using generic metamodeling (eclipse ecore + GMF) * Alandre Magloire, Ms I.T

8. 2.1 Definition and Execution of multiple viewpoints in workflow processes* General Objective –Provide a flexible mechanism to define, weave and execute viewpoints in workflow without modifications on the processes. Viewpoints –Used to express crosscutting concerns in processes, in a modular and independent way, Strategy Proposed –To provide a formal language to define viewpoints at a model level using the AOM (Aspect Object Modeling) principles –To provide a mechanism to weave viewpoints and processes –To provide a mechanism to execute viewpoints and processes *D.Correal, Ph.D. engineering student, U.los Andes / Teluq

9. 2.1 Process Viewpoints at the model level* *Paper, D.Correal & O.Marino

10. Project 2.2: Distributed Aggregation and Control of Learning Objects Project leader : Hamadou Saliah-Hassane Associate Researchers: Ileana de la Teja, Djamal Benslimane (IUT Lyon) Graduate students : Mohamed Mhamdi (PhD); Abdallah Kouri (PhD); Joe Sfeir (M.Sc.A)

11. 2.2 Online Laboratories Practical work online Active learning in real time Various types of resources : learning objects, communication tools Integrated into a multi-environment platform: the environments of the learner, the tutor, the administrator and the instructional designer

12. 2.2 Building a Repository for Online Laboratories Learning Scenarios  We present a pedagogical model for online laboratory repositories of learning scenarios based on IMS-LD  Our approach consists of exporting the IMS-LD compliant XML file for learning scenarios built with MOT+LD  Storage procedure for laboratory metadata (LOM, PROLEARN ), instructional scenarios and learning objects.

13. 2.2 Storage procedure

14. 2.2 Meta Referencing Components and Applications PALOMA

15. 2.2 Metadata In distance-laboratories, learning objects are graphic interfaces and real devices in a remote labs or hooked to a learner’s or tutor’s computer. Various metadata required to ensure granular and aggregative learning objects LOM (Learning Object Metadata) describe the basic properties of the LOs ProLearn metadata supplements the components missing in LOM to describe a distance-laboratory activity.  necessary pre-requisites,  configuration required,  learners’ roles and tasks, etc. Example : Spectrum Analyser with Metadata generated in Prolearn

16. 2.2 Conceptual Model for Future Work  Validate the above model using scenarios with various measuring instruments and actors (users) in remote locations.  Four models used to describe a Learning scenario : content, learning strategy, media, delivery processes  Web services using WSFL or BPEL will be used to link these components.  BPEL Processes used to put laboratory sessions on- line, reserve the required ressources for participants and execute the interaction with the users.  The Virtual Lab Client query BPEL processes to retrieve information of a laboratory session for tutor or trainer supervision.

17. 2.2 – Towards a General Framework

18. Project 2.3: Knowledge and Competency Modeling of Resources and Actors Project leader : G. Paquette PI(s) : R. Hotte, O.Marino Associate Researchers : I. de la Teja, K. Lundgren-Cayrol, Diane Ruelland, Michel Léonard Graduate Students: J. Contamines, L. Moulet, V. Psyché, D. Rogozan, A. Brisebois

19. 2.3 - MOT+ OWL Graphic Editor  User-friendly totally graphic editor  Compliant with the OWL-DL standard  Based on Description Logic  Guaranties computability  Used for the TELOS technical ontology  Used for a Learning Design ontology  Export OWL-DL XML files  Successful exports to PROTEGE

20. 2.3 Competency-based Semantic Annotation Compare planets by mass autonomously Compare planets by orbital period autonomously Analyze, relations between planet mass and orbital period

21. 2.3 Adding a Metric Self-manage (10) Evaluate (9) Synthesize (8) Repair (7) Analyze (6) Apply (5) Transpose (4) Interpret (3) Identify (2) Memorize (1) Pay attention (0). Planet mass and orbital period Skills Scale Performance Scale Aware Familiarized Productive Expert Peter M 6.3 Video Y. 4.9 Book X 7.7 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

22. 2.3 Identify Abandon Risk* Learner Tutor Designer System LEGEND L T S D Calculate Group Indicators (Ex: actual Competency vs target) S Compare Diagnose S Individual / group diagnosis Communicate Diagnosis to A, T, D and S Diagnosis Interface Trace each learner and tutor evaluation Competency, Affective, Social, metacognitive data (from tools) L TS R R R Build the LD and the envirn’t Model of the envirn’t, the task (LD) the domain ontology and entry/target competency D EC TC L2CL2C G2CG2C T2CT2C L1CL1C G1CG1C T1CT1C * Anne Brisebois, PhD Work

23. 2.3 Equilibrate Competency in a LD*. Components of a function around activities must reach competence equilibrium. C C C C P P P Act 5 Activity 5.4 Activitiy 5.1 Activity 5.2 Activity 5.3 7.4 TC: 7.4 TC: 7.4 EC: 6.4 TC: 7.4 TC: 5.2 EC: 5.2 R IP Product resource Input resource Input resource Trainer Learner IP R * Julien Contamines, PhD Work

24. 2.3 Model Learner for ePortfolio * Analysis of an existing ePortfolio open source : Open Source Portfolio (OSP, http://www.osportfolio.org/)http://www.osportfolio.org/ ePortfolio model structure defined : personal information, domain and transversal competencies, internal links semantic Analyse of standards: –IMS: ePortfolio, Learner Information Package, Reusable Definition of Competency or Educational Objective –CEN: Guidelines for the production of learner information standards and specifications (CWA 14926), Recommendations on a Model for expressing learner competencies (CWA 14927), A European Model for Learner Competencies (CWA 15455), – IEEE: Public and Private Information for Learners (PAPI Learner, IEEE P1484.2) Choice of computer representation of the model and functional specifications * Lucie Moulet, PhD Work

25. 2.3 – Maintain Referencing Consistency through Ontology Evolution* OntoAnalyseur  Identify the effects of ontology changes on the semantic referencing of objects UKIsModificateur  Modify the reference links of objects to allow them to properly refer to the new ontology version A MOT+ Graphic OWL Editor * Delia Rogozan, PhD Work

26. Project 2.4: Adaptive Assistance Models and Tools Project leader : Aude Dufresne Graduate Students : Mohamed Rouatbi - École Polytechnique Patrick Fulgence Goudjo-Ako – LICEF Fethi Guerdelli -Doctorat en Informatique Cognitive – UQAM Villiot-Leclercq, Emmanuelle - CLIPS IMAG, Grenoble

27. 2.4 - Actors Adaptive Assistance Support users in the execution of functions in multiple applications ? ? ? Static or Dynamic Different in their implementations Evolving Running simultaneously Explore integration Communicate structures of information between applications initially and dynamically

28. Integrated environment for support 2.4 – Integration of support Coordinate assesment, Integrate support Facilitate help definition

29. 2.4 Integration and ontology alignment using SESAME Integration Mohamed Rouatbi

30. 2.4 Interaction using the ontology in SESAME InteractionAssistance Mohamed Rouatbi, Patrick Fulgence, Goudjo-Ako An example EFDAuteurExplor@Graph Generic Advisor

31. 2.4 - Integration of assistance using OWL ontologies RDF Description of objects, rules, user models owl Owl structures exported using Protege API

32. 2.4 - Distributed Integration of support Resource The integration using the ontology adds a visualization of the link to a ressource node and the properties of that ressource Integration with PALOMA Ressource Manager using BPEL Distributed integration of support

33. 2.4 – Project Results Prototypes  Generic Editor - C* Edition of rules using structures  Generic Advisor - Java expert system  Rights Manager C*  Explor@GraphNet VB.Net version of Explor@Graph navigator  In progress : Exportation of Graphs from Expor@Graph Editor  Planned: To use Sesame repository to keep User Models and rules from EG; Integration of Generic advisor in EGN and other TELOS application using JCM Models and applications – Prototype of support in the EXAO environment – Develop the embedding of support to teachers – Case study of a collaborative scenario ICALT’2006 – BEST - Case of the Virtual Doctoral School

34. 2.5 Functional Aggregation Goals of the project –Integrate software components from the other project in theme 2 –Explore new aggregation possibilities and tools report to help orient theme 6 work –Put to the test TELOS central services to built specific aggregates using theme 2 and other components –Put the aggregates to functional tests –Specify improvements needed to theme 6 and other themes

35. 2.5 Project Contributions

36. 2.5 Partial Aggregation in project 2.4 Micro-aggregation PALOMA and Explor@GRAPH Macro aggregation using Sesame, BPEL and the TELOS Function Editor

37. 2.5 Partial Aggregation in project 2.2 Aggregating PALOMA, Concept@ LCMS and Virtual Lab Tools using BPEL and TELOS function editor

38. 2.5 Global Integration Scenario (Components) Function Editor –Function Editor/Paloma aggregate SOCOM (components Manager) Knowledge and Competency Editor Annotator Generic Advisor Explor@Graph –Explor@Graph/Paloma micro-aggregate –Explor@Graph Editor –Explor@Graph User Manager Concept@ Remote Virtual Laboratory (RVL) - RVL micro-aggregate - Spectrum Analyzer Client -Labader User Manager - Spectrum Analyzer Server PALOMA PALOMA Users manager PALOMA Folder Manager PALOMA LOM Manager Sesame

39. 2.5 Global Integration Scenario (Scenario) 1.Technologist (Actor 1) uses the Function Editor to compose a LKMS composed of the above components enhancing the Concept@ LCMS 2. Designer A (Actor 2) –Use the LKMS to create a VLAS (RLV micro-aggregate) –Use the K&C editor to represent the domain and define target competencies for the VLAS –Use PALOMA to search for useful LOMS and associate LOs to VLAS activities –Use the GEN ADVISOR to add advices to activities –Store the VLAS and attachments into SESAME

40. 2.5 Global Integration Scenario (Scenario cont.) 3.Designer B (Actor 3) Loads the VLAS from SESAME into Explor@Graph to view Loads the VLAS from SESAME into the Function Editor to modifiy it to an IMS-LD compliant LD with the same attachments Stores it back into SESAME 4. Tutor (Actor 4) –Authenticates in the VLAS through the tutor view –Uses SpectrumAnalyseServer to book a RVL session –Loads reservation information using the RVL micro-aggregate 5. Learners (Actor 5) –Authenticate with the RVL in their own version of the VLAS –Tutor set learners rights in Spectrum Analyzer –Learners use SpectrumAnalyzerClient assisted by tutor –Learner with tutor evaluate actual competencies and receive advice from GenericAdvisor.

41. 2.5 Principles illustrated in the process Graphic aggregation Ontology referencing Interchangeability of activity editors Multi-actor scenarios (LD) Multi-Technology integration Seemless interfacing ………

42. Theme 2 - Presentation Aggregation and Multi-Actor Designs i2LOR-06 Conference, Montreal November8-10 Aude Dufresne, Olga Marino, Gilbert Paquette and Hamadou Saliah-Hassane