The ELeGI Project

The next generation GRID for effective human learning

http://www.elegi.org

Technology-enhanced learning and access to cultural heritage

The ELeGI Project

Contact Person: Pierluigi RitrovatoResearch & Technology Director

Centro di Ricerca in Matematica Pura ed Applicata

ELeGI Scientific Coordinatoremail: [email protected]

Overview

ELeGIThe Project VisionThe ApproachGoalsSEES and Demonstrators

The ELeGI architectureGrid technologiesVirtual Learning Communities Learning Services Knowledge and Didactic ModelsE-learning modelContext-based ontologyIMS-LD

The proposed scenario: Physics course in the Open University

The contextScenario Set-UpScenario Execution

The Grid added value to ELeGI

The Project Vision

To produce a breakthrough in current To produce a breakthrough in current (e)Learning practices(e)Learning practices with the creation with the creation of an open, Grid based, distributed and of an open, Grid based, distributed and

pervasive environment pervasive environment wherewhere

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

The ELeGI approach

Design and Implementation of Service Oriented infrastructure

Pedagogical and Usability Evaluation

The Learning GRID Infrastructure

GRID Technologies

Disseminatio

n

Training

Exploitation

Standardisation

SEES &

Dem

os

SEES &

Dem

os

Did

actica

l Models

Know

ledge R

epre

s.

En

hance

d P

rese

nce

Convers. p

roce

sses

ELeGI Goals

1. To create new potential for ubiquitous and collaborative human learning, merging experiential, personalised and contextualised approaches

2. 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)

3. To validate and evaluate the software architecture and the didactical approaches through the use of SEES and Demonstrators

SEES and Demonstrators

SEESInformal 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 coursesLearning services for Accountancy and Business FinanceLearning services for Mechanical Engineering

Knowledge Management Services

Learning Services

ELeGI Architecture

Infrastructure Services

SelfManagement

Services

ExecutionManagement

ServicesInformation

ServicesMonitoring

Services

ResourceManagement

Services Sec

uri

ty

Ser

vice

s

VLC Management Services

PolicyServices

Discovery& Semantic

Annotation Services

Grid Layer

Learner ProfileManagement Services

PersonalizationServices

Ontology Management Services

Course Management Services

Learning MetadataServices

Communication & Collaboration Services

Su

pp

ort

Ser

vice

s

Contents & Services Orchestration

E-Learning Application

E-Learning Layer

Application Layer

Sec

uri

ty

Sem

anti

c

DataServices

Accounting Services

Bil

lin

gS

ervi

ces

Neg

otia

tion

Ser

vice

s

Member ProfileManagement Services

Tru

st

Ser

vice

s

Didactical ModelMangm. Services

Core Services

VLC Services

Grid technologies

Grid

Execution Management

InfrastructureInfrastructure ServicesServices

Monitoring Services

Information Services

Resource Management

Security Services

Accounting Services

Access to Learning Access to Learning Object RepositoryObject Repository

Self Management

The Grid technologies are considered the natural evolution of distributed systems and the Internet

It facilitates the realization of ubiquitous computing conceptIt 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

Member Profile Management Services

allow the management of the profile information of the Community Members

support information privacy

Policy Services allow the management of:

role of the community membersprivilege of the community memberspolicy to access/use resources

Negotiation Services allow negotiation of the

agreement on the provision of a service

support Quality of 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

Billing Services charge the use of services and

resources prepare and send bill

Trust Services provide basic trust capabilities support recommendation support delegation

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 ProfileManagement Services

Policy Services

Discovery& Semantic Annotation Services

Negotiation Services

Billing Services

Trust Services

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)

VLC Management Services provide administration utilities for

the management of the Virtual Community

virtual community definition and creationmember registration/deregistration…

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

…


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 StateLearning 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.

Learning Services

The e-Learning services facilitate and manage the learning process.


Course Management Services

Learning Metadata Services

Didactical Model Management Services

Learner ProfileManagement Services

Personalization Services

Ontology Management Services

Support Services

Knowledge and Didactic Models

E-learning model

· Didactic Model

· Knowledge Model

· Student Model

E-learning model

· Didactic transposition

· Knowledge representation

· Context-based learning

· Personalised learning

The general e-learning model allows the construction of context-based and personalised learning paths

Extensibility and flexibility

Learning scenario

· Situated

· Experiential

· Active

· Collaborative

Implication of the student

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

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

Retrieval of target of learning (TL)

Set of elementary concepts with

metadata for TL

Execution of algorithm for choosing LOs associated to

ECM

Unit of learning(Sequence of LOs)

Delivery of a sequence of LOs

Personal knowledge of student

Implication

knowledge

Retrieval and organization of propaedeutic

concepts

Sequencing of elementary concepts with metadata (ECM)

Student

Automatic building of Unit of learning

Knowledge about the course

to be created

Teacher

Context: Physics, University course, Second CycleDM/M: DeductiveDD: 9

H = Has Part IR = Is Required By

DM/M = Didactic Model/MethodDD = Difficulty Degree

Gravity Acceleration

Equation of Motion

Differential Equation

H

Law of the Pendulum’s

Motion

IR

AngularAcceleration

IR IR

DM/M: Deductive

DM/M: Inductive/Learning by ExampleDD: 6

Context: Physics, University course, First CycleDM/M: Inductive/ProblemSolvingDD: 7


DM/M = Didactic Model/MethodDD = Difficulty Degree


Equation of Motion


H


Motion

I R

AngularAcceleration

IR

AngularSpeed

Time

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.


Equation of Motion


H


Motion

IR


Meta-ontology for Physics

AngularAcceleration

Length of Pendulum

IR

AngularSpeed

Angle Time

IR

IMS-LD: our way to define learning scenarios

Describe and implement learning activities based on different pedagogies, including group work and collaborative learningCoordinate multiple learners and multiple roles within a multi-learner model, or, alternatively, support single learner activitiesCoordinate the use of learning content with collaborative servicesSupport multiple delivery models, including mixed-mode learningIMS Learning Design also enables:

Transfer of learning designs between systemsReuse of learning designs and materialsReuse of parts of a learning design, e.g. individual activities or rolesInternationalisation, accessibility, tracking, reporting, and performance analysis, through the use of properties for people, roles and learning designs

Purpose:

Scenario Description: Physics course in the Open University

Collaborative/Social Learning in Physics Course at HOU (Hellenic Open University)

HOU students

students perform experiments/ simulations and construct knowledge through the exchange of data and knowledge

Virtual Experiments/ VirtualCommunities Support

Target Group:

Main Characteristics:

Type of learning:

Type of servicesneeded:

formal (but highly diverse student population)

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)

Physics Lab

DMSC Lab

Students organized in classes based in specific cities

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

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)

Scenario Setup

Legend Super Node (Patras) Super Node (Athens)

Nodes (Iraklion, Piraeus, Ioannina, Volos, Thessalonica, Xanthi) Backbone : GUNet (155 Mbps)

Scenario Execution

Resource “Y”

Data layer

Resource “X”Data layer

Resource “Z”

Data layer

Course Personalization

service

Web GUI(WSRP)

Data layerLocalization

Service

Invoke the Localization Service in order to find

the list of Course Services

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

Gravity Accelleration

Equation of Motion


H


Motion

IR


Meta-ontology for Physics

AngularAccelleration

Length of Pendulum

IR

AngularSpeed

Angle Time

Invoke the IS in order to create a Corse Driver

Instance

Request the delivery of the Course

Scenario Execution

Instantiator Service (IS)

Web GUI(WSRP)

Asks for a Personalized Learning Path

Builds Web GUI for delivery of

Resource X

LocatorService

Find the list of the

drivers which are able to

delivery the Resource

UDDI

Course Driver Instance


service

Instantiates a suitable driver for Resource

“Y”

The Client interacts with the Localization Service to find a list

of Course Services

The Client interacts with the Instantiator Service to create a new

Course Driver Instance

Requests the delivery of Resource

“X”Driver #1

Instantiates a suitable driver for Resource X

Driver #2

Data Layer


service

Data Layer(Learning Object

Repository)

Data Layer(Learning Object

Repository)

Builds Web GUI for delivery of Resource “Y”

Retrieve LO

Retrieve LO

Personalized Learning Path

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)

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 knowledgeThrough the virtualization and sharing of several kind of resources facilitate the dynamic contexts generationThe dynamic service discovery and creation will allow the true personalisation

Thank you very muchThank you very much for your attention for your attention

Documents

The ELeGI Project