ELeGI Project - Distributed Grid Environment for Effective Learning

1. The ELeGI Project Contact Person: Pierluigi Ritrovato Research & Technology Director Centro di Ricerca in Matematica Pura ed Applicata ELeGI Scientific Coordinator email: ritrovato@crmpa.unisa.it

2. Overview • ELeGI • The Project Vision • The Approach • Goals • SEES and Demonstrators • The ELeGI architecture • Grid technologies • Virtual Learning Communities • Learning Services • Knowledge and Didactic Models • E-learningmodel • Context-based ontology • IMS-LD • The proposed scenario: Physics course in the Open University • The context • Scenario Set-Up • Scenario Execution • The Grid added value to ELeGI

3. The Project Vision To produce a breakthrough in current (e)Learning practiceswith the creation of an open, Grid based, distributed and pervasive environment where • effective human learning is the result of a social activity through communications and collaborations • learners will create their knowledge through direct experience in a contextualised and personalised way and share it with others in dynamic Virtual Communities

4. Knowledge Repres. Didactical Models Enhanced Presence Convers. processes GRID Technologies SEES & Demos SEES & Demos Design and Implementation of Service Oriented infrastructure Exploitation Standardisation Dissemination Training Pedagogical and Usability Evaluation The Learning GRID Infrastructure The ELeGI approach

5. ELeGI Goals • To create new potential for ubiquitous and collaborative human learning, merging experiential, personalised and contextualised approaches • To define and implement an advanced service-oriented Grid based software architecture for learning. This objective will be driven by the pedagogical needs and requirements elicited from Service Elicitation and Exploitation Scenarios (SEES) • To validate and evaluate the software architecture and the didactical approaches through the use of SEES and Demonstrators

6. SEES and Demonstrators • SEES • Informal Learning • Alphabetisation for Durable Development • Learning and Training of Researchers in Organic Chemistry • e-Qualification by Open Universities • Formal Learning • Masters in ICT with remote teaching and tutoring activities • Physics course in the Open University • Demonstrators • Virtual Scientific Experiments for teaching high level mathematical courses • Learning services for Accountancy and Business Finance • Learning services for Mechanical Engineering

7. ELeGI Architecture Application Layer E-Learning Application Contents & Services Orchestration Learning Services E-Learning Layer Course Management Services Didactical Model Mangm. Services Learning Metadata Services Support Services Knowledge Management Services Learner Profile Management Services Personalization Services Ontology Management Services Security Semantic Communication & Collaboration Services Discovery& Semantic Annotation Services Trust Services Negotiation Services VLC Services Billing Services VLC Management Services Member Profile Management Services Policy Services Grid Layer Execution Management Services Accounting Services Self Management Services Security Services Data Services Monitoring Services Resource Management Services Information Services Core Services Infrastructure Services

8. InfrastructureServices Access to Learning Object Repository Information Services Monitoring Services Accounting Services ResourceManagement Security Services Execution Management Self Management Grid technologies Grid It facilitates the realization of ubiquitous computing concept The Grid technologies are considered the natural evolution of distributed systems and the Internet It allows the virtualization and sharing of several kind of resources facilitating the dynamic context generation It facilitates the creation of emerging challenging learning scenarios through dynamic VO It provides services and advanced mechanisms for automatic discovery and binding of new suitable contents and services Enabling the creation of dynamic, distributed and heterogeneous Virtual Learning Communities

9. Virtual Learning Communities (VLC) VLC Layer provides general and re-usable services for the lifecycle management of virtual communities. Discovery and Semantic Annotation Services • offer semantically-enabled registries and key features to publish service descriptions • support basic ontology management such as editing, browsing, mapping, consistency and validation, versioning; • capture annotation and dynamically link resources based on those annotations; • take advantage from the semantic enabled registries to enable more sophisticated discovery Communication/Collaboration Services • support synchronous and asynchronous interaction (email, forum, instant messaging, chat, …) • support different media formats (text, image, audio, video, and their combination) • support many communication models (one-to-one, one-to-many, broadcast, many-to-many) Billing Services • charge the use of services and resources • prepare and send bill VLC Management Services • provide administration utilities for the management of the Virtual Community • virtual community definition and creation • member registration/deregistration • … Trust Services • provide basic trust capabilities • support recommendation • support delegation Policy Services • allow the management of: • role of the community members • privilege of the community members • policy to access/use resources Member Profile Management Services • allow the management of the profile information of the Community Members • support information privacy Negotiation Services • allow negotiation of the agreement on the provision of a service • support Quality of Services

10. Learning Services The e-Learning services facilitate and manage the learning process. Support Services • Alert Services • Help Services providing help features to assist learners in achieving their learning objectives • Assessment Services, providing online facility to check learning progress during and at the end of the course • e-Portfolio Services, supporting the management and assessment of artefacts created by learners • Reporting Services, providing facilities for producing standardized and automated reports on data • … Contents & Services Orchestration • searching and collecting dynamically contents and services • composition and orchestration of a didactical course (contents and services) • use the didactical and knowledge models • deliver contextualised learner services Course Management Services • access and manage courses, modules, and other units of learning • administration utilities (assignment management, student/staff management, assignment/submission evaluation, …) Learning Metadata Services • provide metadata services for learners and learning resources, including • Resource registration (i.e. providing metadata), • Metadata management, • Search and evaluation. Didactical Model Management Services • provide operation to manage the didactical models: • create, • edit, • validate, • browse, • … Learner Profile Management Services • allow the management of learner profile information: • Student Cognitive State • Learning Preferences • allows automatic update as a consequence of the new learning experiences performed Personalization Services • dynamically adapting and delivering of the learning resources • personalize the learning paths according to learner profile and needs (i.e. Adaptive Learning Path Generation Services that allow to automatically produce a personalized learning path for each learner) Ontology Management Services • extend the ontology services provided by the lower VLC sub-layer for learning domain.

11. Knowledge and Didactic Models The general e-learning model allows the construction of context-based and personalised learning paths Extensibility and flexibility Implication of the student

12. E-learningmodel Didactic Transposition • From the knowledge to the concrete knowledge • From the concrete knowledge to the contextualised didactic knowledge • From the contextualised didactic knowledge to the personalised didactic knowledge

13. E-learning model Didactic Transposition • Definition of the Target of Learning • Definition of the sequencing of Elementary Metadata Concepts(ECM) • Definition of the Unit of Learning

14. Context-based ontology The Generic Contextualised Ontology (GCO) will keep the same base structure of the meta-ontology but will bring with itself some metadata, derived from the Context, that will describe one or more families of concepts.

15. IMS-LD: our way to define learning scenarios • Describe and implement learning activities based on different pedagogies, including group work and collaborative learning • Coordinate multiple learners and multiple roles within a multi-learner model, or, alternatively, support single learner activities • Coordinate the use of learning content with collaborative services • Support multiple delivery models, including mixed-mode learning • IMS Learning Design also enables: • Transfer of learning designs between systems • Reuse of learning designs and materials • Reuse of parts of a learning design, e.g. individual activities or roles • Internationalisation, accessibility, tracking, reporting, and performance analysis, through the use of properties for people, roles and learning designs

16. Scenario Description:Physics course in the Open University Collaborative/Social Learning in Physics Course at HOU (Hellenic Open University) Purpose: Target Group: HOU students students perform experiments/ simulations and construct knowledge through the exchange of data and knowledge Main Characteristics: formal (but highly diverse student population) Type of learning: Type of services needed: Virtual Experiments/ Virtual Communities Support

17. The context Physics Course: • 4-year course leading to a Bachelor Degree in Natural Sciences • 12 modules + 3 laboratory • (3 modules related to Physics: 7 text books suitable for Open and Distance Learning) • Student attendance: > 2500 students • Permanent Academic Staff (Prof., Ass. Prof.) • Tutors (Phd holders) • Students organized in classes based in specific cities Physics Lab DMSC Lab

18. The context: City coverage Teaching method: • Text books • Synchronous & Asynchronous collaboration tools (…but mainly email/WWW is used) • Class meetings (a form of social learning) • Assignments (4-6 per module) Class/student distribution

19. The context:User Needs • Knowledge construction : • Perform experiment (visualisation of data sets and output) • Search for resources and/or share results • Access supporting educational material • Perform on-line test/essay • Virtual Communities support (social learning): • Collaborate using asynchronous sharing services (e.g. sharing documents, knowledge, VSE results etc.) • Collaborate using synchronous sharing services during an experiment (with other students and/or the tutor)

20. Scenario Setup • Legend • Super Node (Patras) • Super Node (Athens) • Nodes (Iraklion, Piraeus, Ioannina, Volos, Thessalonica, Xanthi) Backbone : GUNet (155 Mbps)

21. Data layer Resource “Z” Data layer Data layer Resource “Y” Course Personalization service Localization Service Web GUI (WSRP) Data layer Resource “X” Scenario Execution Invoke the Localization Service in order to find the list of Course Services

22. Locator Service UDDI Data Layer (Learning Object Repository) Data Layer (Learning Object Repository) Data Layer Course Driver Instance The Course Driver Service contacts the Data Layer to retrieve the Student Model and Ontology and it invokes the Course Personalisation Service The Course Personalization Service, on the basis of the Student Model and the Ontology, generates the personalized learning path Obtained the Learning Path, the Course Driver is able to find and create an instance of a Driver service able to manage the resource of the Course The Client interacts with the Instantiator Service to create a new Course Driver Instance The Client interacts with the Localization Service to find a list of Course Services Course Personalization service Course Personalization service Request the delivery of the Course Find the list of the drivers which are able to delivery the Resource Requests the delivery of Resource “X” Scenario Execution Asks for a Personalized Learning Path Invoke the IS in order to create a Corse Driver Instance Personalized Learning Path Instantiates a suitable driver for Resource X Builds Web GUI for delivery of Resource X Retrieve LO Instantiates a suitable driver for Resource “Y” Builds Web GUI for delivery of Resource “Y” Retrieve LO

23. The Grid added value to ELeGI (1) • Grid technologies: • Rely upon a dynamic and stateful service model (e.g. WSRF or WS-I+) and this affects also the development of learning scenarios (need state management in conversational processes) • The key technologies to build the VO (VirtualOrganization) paradigm (VO are the right place for carrying out collaborative learning experiences) • Provide dynamicity and adaptiveness to LD scenarios (our learning process is pedagogical driven) • Provide the scale of computational power and data storage needed to support realistic and experiential based learning approaches involving responsive resources, 3d simulations and immersive VR (Virtual Reality)

24. The Grid added value to ELeGI (2) • Grid technologies: • Are demonstrating their effectiveness for implementing e-Science infrastructure for sharing and manage data, applications and also knowledge • Through the virtualization and sharing of several kind of resources facilitate the dynamic contexts generation • The dynamic service discovery and creation will allow the true personalisation

25. Thank you very much for your attention