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

2. Overview ELeGI The ELeGI architecture
The Project Vision
The Approach
Goals
SEES and Demonstrators
The ELeGI architecture
Grid technologies
Virtual Learning Communities
Learning Services
Knowledge and Didactic Models
E-learning model
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. To produce a breakthrough in current
The Project Vision
To produce a breakthrough in current
(e)Learning practices with 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. The ELeGI approach The Learning GRID Infrastructure SEES & Demos
Design and Implementation of Service
Oriented infrastructure
Pedagogical and Usability
Evaluation
The Learning GRID Infrastructure
GRID Technologies
Dissemination
Training
Exploitation
Standardisation
SEES & Demos
Didactical Models
Knowledge Repres.
Convers. processes
Enhanced Presence

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
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 Infrastructure Services Application Layer
E-Learning Application
Contents & Services Orchestration
Learning Services
E-Learning Layer
Course Management
Services
Learning Metadata
Services
Didactical Model
Mangm. Services
Support Services
Knowledge Management Services
Learner Profile
Management Services
Personalization
Services
Ontology Management
Services
Security
Semantic
Discovery&
Semantic
Annotation
Services
Communication & Collaboration Services
VLC Services
Trust
Services
Negotiation
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
Information
Services
Monitoring
Services
Resource
Management
Services
Core Services
Infrastructure Services

8. Infrastructure Services
Grid technologies
Execution Management
Infrastructure Services
Monitoring Services
Information Services
Resource Management
Security Services
Accounting Services
Access to Learning
Object Repository
Self Management
Grid
It facilitates the realization of ubiquitous computing concept
It allows the virtualization and sharing of several kind of resources facilitating the dynamic context generation
The Grid technologies are considered the natural evolution of distributed systems and the Internet
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.
Communication &
Collaboration Services
VLC Management Services
Member Profile
Management Services
Policy Services
Discovery& Semantic
Annotation Services
Negotiation Services
Billing Services
Trust Services
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 ( , 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
Negotiation Services
allow negotiation of the agreement on the provision of a service
support Quality of Services
Member Profile Management Services
allow the management of the profile information of the Community Members
support information privacy

10. Learning Services
The e-Learning services facilitate and manage the learning process.
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)
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 …
Ontology Management Services
extend the ontology services provided by the lower VLC sub-layer for learning domain.
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
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
Didactical Model Management Services
provide operation to manage the didactical models:
create,
edit,
validate,
browse, …
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.
Contents & Services
Orchestration
Course Management Services
Learning Metadata Services
Didactical Model
Management Services
Learner Profile
Personalization Services
Ontology Management Services
Support Services

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
Formulation of an initial open theoretical model aimed to create personalised learning path based on the assumption that
«Learning does not occur because of one specific type of interaction, but because of the availability of all of them. One type of interaction, or one type of agent, being selected depending on the needs of the learner at the time when the interaction is looked for, as well as of the specific characteristics of the knowledge at stake.» (Balacheff, 2000)
So we have studied how the knowledge representation can take into account the context in which the learning occurs and the characteristics of the involved learner

12. E-learning model 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
In the following we will define a theoretical model that, taking advantage of the cited three models, allow IWT to built a “Unit of Learning”, where with this term we mean a whatever delimited educational object, such as a course, a module, a lesson, etc. (IMS Learning Design Specification). The presented model takes particular care of the didactic transposition and the devolution.
From the knowledge to the concrete knowledge:
the domain expert, thanks to the directions derived from the institutions (e.g. ministerial programmes), selects from the knowledge coming from the research the knowledge as teaching object described by a meta-ontology.
From the concrete knowledge to the contextualised didactic knowledge:
starting from the meta-ontology, the Domain Expert builds a particular “view” of the meta-ontology, that we will call Generic Contextualised Ontology (GCO), taking into account the context in which the teaching/learning process occurs.
From the didactic contextualised knowledge to the didactic personalised knowledge:
if learning is individual, the system automatically creates what we call Specific Personalised Ontology (SPO), obtained by specialising the meta-ontology on the basis of the characteristics of the single learner (SM) in the framework of the fixed context.

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
Definition of the Target of Learning:
once an ontology (GCO or SPO) has been defined, through the definition and retrieval of the Target of Learning (TL), the teacher defines the set of elementary concepts (atomic) representing the learning objectives; such concepts contain all the information (metadata) deriving from the ontology we have defined in the previous passage (which depend on the SM and DM).
Definition of the sequencing of ECM:
as the elementary metadata concepts composing the TL have been fixed, through the Propaedeutic relationship (that we believe compulsorily present into the ontology), the system automatically determines the order for the acquisition of the elementary concepts, thus producing a sequencing.
Definition of the Unit of Learning:
applying a proper algorithm of choice of Learning Object (LO) associated to the EMC, the LOs are then defined for. each elementary concept and the LOs sequence is therefore fixed in accordance with the sequencing defined before, which ends with constituting the Unit of Learning to be delivered to the learner.

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
Purpose:
Collaborative/Social Learning in Physics Course at HOU (Hellenic Open University)
Target Group:
HOU students
Main Characteristics:
students perform experiments/ simulations and construct knowledge through the exchange of data and knowledge
Type of learning:
formal (but highly diverse student population)
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 /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. Course Personalization service
Scenario Execution
Web GUI
(WSRP)
Resource “Z”
Data layer
Resource “Y”
Data layer
Course Personalization service
Resource “X”
Data layer
Data layer
Localization Service
Invoke the Localization Service in order to find the list of Course Services

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

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 (Virtual Organization) 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