ELeGI:The European Learning

GRID Infrastructure

Pierluigi Ritrovato,Matteo Gaeta

CRMPA,

University of Salerno,

Italy

Alan Ruddle, Colin AllisonSchool of Computer Science

University of St Andrews,

Scotland

Overview• Motivation• Project Aims• Advanced Learning Environment Features• Grid Service-based Learning Environments• ELeGI Activities Summary• Example Demonstrator: Finesse Learning

environment• Monitoring and Adaptation • Conclusions

Motivation

• Lack of effective existing e-Learning practices and environments

• Based on the information transfer paradigm with focus on the content and the “teacher”

• Technology driven approach– e-Learning becomes an activity in which teachers

produce, and students consume, multimedia books on the Web

• In the new approach, knowledge construction, rather than information transfer, is the key.

• The focus is on the learner and on the learning strategies that better satisfy the learner characteristics

Project Consortium

• 23 partners from 8 Countries– 13 Universities (2 Open Universities)– 4 Industrial partners– 6 research Centres

characteristics of future learning scenarios

• Based on new learning approaches – Experiential– Contextualised– Collaborative– Personalised

• Extensive use of advanced technologies and software solutions virtualised as services– Virtual Reality– Virtual Laboratory– Video conferencing

• Service Orientation – not product oriented!

• Dynamicity

• Strong need for interoperability

• Open Architecture and standards

• Security and Trust Issues

Main Goals

1. To define new models of human learning enabling ubiquitous and collaborative learning, merging experiential, personalised and contextualised approaches

2. To define and implement an advanced service-oriented Grid based software architecture for accessing and integrating different technologies, resources and contents needed to realise the new paradigm

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

Features of the new paradigm

• Collaboration: group working should be supported routinely; dynamically formed virtual communities

• Experiential: the learner is genuinely involved• Realism: real-world input should be easy to incorporate, as

should simulations, ranging from simple interactive animations to immersive VR

• Personalised: students should find themselves at the centre of their online environment, with their individual needs addressed

• Ubiquity and Accessibility: – wider, more flexible access to educational resources should be

provided, often referred to as “anytime/anywhere” learning.– multiple different types of devices, interfaces, and network

connection types should be supported where possible

• Contextualised: appropriate learning contexts may naturally be short-lived, as well as the more traditional static situations such as the classroom and the library – this calls for dynamicity in the creation of contexts

What does the Grid bring ?

• Common infrastructure– OGSA, WSDL, UDDI, etc

• Component sharing• Dynamic Service

Composition• Combined resources for

enhanced functionality and power

• Open architecture and open standards

Registry

ServiceRequester

ServiceProvider

12

4

3

5

ELeGI Activities

Design and Implementation of Service Oriented infrastructure

Pedagogical and Usability Evaluation

The Learning GRID Infrastructure

GRID Technologies

Disseminatio

nExploitation

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

Example Learning Environment: Finesse

• Finesse: finance education in a scaleable software environment

• Supports teaching of fund management• Virtual portfolios at the core

– Buy/sell shares– Try to make a profit relative to market

• Inspect historic data• “Notebook” messaging tool for

asynchronous collaboration

Finesse future

• Re-engineer to be GSDL based

• Synchronous communications– Video conferencing– Synchronous groupware

• Device independence

Enhanced Presence

FiGS – Finesse Grid Services

Browser

Video

User Web Servlets

GS:

Manager

GS: Notebook

GS: Conferencing

GS: Portfolio

GS: Stock Data Source 1 GS: Stock

Data Source 2

FinesseServices

Grid ServicesWeb Services

Technical implications

• Grid-service-based learning environments (LEs) -> Quality of Service (QoS)

Dangerous assumption – that there will be QoS “on demand” for the Grid

• Realistic assumption: Grid-based LEs must make use of available infrastructure e.g. the Internet

Internet QoS Approaches

• Ostrich algorithm– No requirements from infrastructure– No guarantees! Wide variance in quality

• Resource Reservation– Admission Control, schemes: RSVP, IntServ– Major requirements on infrastructure

• Aggregate Flows – Modest requirements on infrastructure, MPLS– Deployed at edge, no state at ‘core’ routers:

DiffServ– Scales well within single administrative domains

• Adaptive QoS– End-to-end for users– No requirements on infrastructure– Deployable at the local network level

Adaptive QoS Provision

• Past network conditions -> statistics

– TCP/ RTP/RTCP traffic monitoring

• Estimate likely network path conditions

– Temporal & spatial patterns in traffic

• Inform learning resource at start

• Learning resource adapts to changes

Conceptual Framework:Location Information Server

Conference Controller Architecture for Video Conferencing

QoS Summary• the assumption of QoS provision in Grid based

applications, which use the Internet, is an unsafe one

• it is beneficial to use measures which predict the likely QoS provision and adapt to it

• how to provide a QoS aware service to a Grid conferencing service so that it can be adaptive, and to that extent, contextualized for each participant

• we have successfully integrated a prototype of this system into an existing learning environment (Finesse)

• the feasibility and potential of mechanisms for achieving adaptive, dynamically constituted conference sessions has been shown

• Future: QoS advisory service more automated and available for use in any Grid-based collaborative learning environment

ELeGI Expected results• The service oriented GRID based Software

Architecture;• Formalisation of didactical models for the new

learning approaches;• Methodologies for evaluating the effectiveness of

these new learning approaches from the pedagogical and usability points of view;

• Prototypes for demonstrating the potential offered by the ELeGI technologies and methodologies;

• Methodologies and techniques for making existing applications Grid-aware;

• Contribution to the technical standards in the Learning, semantic Web, and Grid domains;

• Workshops, Conferences, Publications, Information Web sites, and Demonstrator Web sites;