ORIGINAL ARTICLE

A grid based software architecture for delivery of adaptiveand personalised learning experiences

Angelo Gaeta Æ Matteo Gaeta Æ Pierluigi Ritrovato

Received: 22 December 2006 / Accepted: 9 May 2007

� Springer-Verlag London Limited 2007

Abstract This paper is centred on one of the main

results of the ELeGI project, namely its software archi-

tecture for the delivery of personalised formal-learning

experiences. The architecture has been designed and

developed: (1) taking into account a general model for the

personalisation of learning experiences, allowing us to

obtain a solution that is flexible with respect to the ped-

agogies, and (2) on top of service oriented grid

technologies, allowing us to obtain several advantages in

the process of creation and delivery of personalised

learning experience like, for instance, ubiquitous and

seamless access to heterogeneous learning resources dis-

tributed over the network. In order to validate our result,

the first prototype of the ELeGI architecture has been

deployed on a virtual organisation consisting of three

geographically distributed nodes. Each node of the VO

provides services and learning resources that have been

adopted in the creation and delivery of a personalised

learning experience about the Torricelli’s law and based

on the virtual scientific experiment model. The case of

study has been successfully executed and has given us a

proof of our assumptions related to the added value of the

service oriented grid mainly in terms of: (1) capabilities

to access educational resources distributed over the net-

work, that is relevant in achieving the personalisation of

learning experiences, and (2) high level of dynamicity and

adaptiveness in the creation and delivery processes of a

personalised learning experience.

Keywords SOA � Grid �User-adaptive learning and personalisation �Architecture for ubiquitous e-learning � IMS-LD

1 Introduction

The European learning grid infrastructure (ELeGI) project

[1], an EU-funded integrated project (23 partners from nine

EU countries), has the ambitious goal to ‘‘radically advance

the effective use of technology-enhanced learning through

the design, implementation and validation of a pedagogy-

driven, service-oriented software architecture based on grid

technologies’’. ELeGI aims at promoting and supporting a

new learning paradigm focused on the knowledge con-

struction using experiential based and collaborative

learning approaches in a ubiquitous, collaborative, con-

textualised, individualized, and personalised way and

taking into account informal learning aspects as well.

In order to Foster this approach toward human learning,

ELeGI creates dynamic contexts and leverage on Grid [2]

technologies for creating highly realistic virtual scientific

experiments and develop active learning processes with

progressive abstraction levels, leading to the knowledge

construction in a dynamic way, sharing knowledge and

experiences with others. Moreover, ELeGI allows the

definition of personalised and individualised learning paths

that take into account learners’ skills and knowledge. To

achieve these goals, ELeGI needs a clear strategy,

A. Gaeta (&) � P. Ritrovato

Centro di Ricerca in Matematica Pura ed Applicata (CRMPA),

Via Ponte Don Melillo, 84084 Fisciano (SA), Italy

e-mail: agaeta@crmpa.unisa.it

P. Ritrovato

e-mail: ritrovato@crmpa.unisa.it

M. Gaeta

Dipartimento di Ingegneria dell’Informazione e Matematica

Applicata (DIIMA), University of Salerno,

Via Ponte Don Melillo, 84084 Fisciano (SA), Italy

e-mail: gaeta@unisa.it

123

Pers Ubiquit Comput

DOI 10.1007/s00779-007-0183-y

formalized through the definition of models (supporting

formal and informal learning scenarios), methodologies,

and technologies, enabling to overcome the drawbacks of

traditional e-learning solutions and to advance the effective

use of technology-enhanced learning. To address the issues

related to both formal and informal learning, the ELeGI

project is structured according two main action lines:

ELeGI-f and ELeGI-i.

This paper is centered on the main results related to the

first action line, ELeGI-f, for formal learning.

We briefly start introducing the general learning model,

which the learning experience personalisation process is

definitely based on, and the virtual scientific experiment

(VSE) model, which the case of study is referred to, also

evidencing how the VSE can be seen as a didactic method

in our general learning model.

Next, we present the ELeGI-f software architecture and

its key services highlighting the added value of grid tech-

nologies for the personalisation of learning experiences.

Then a concrete case of study is presented concerning

the arrangement and execution on top of the service

architecture of a VSE explaining the Torricelli’s law. The

case of study demonstrates how the model and its concrete

instantiation on three processes, and the services of the

ELeGI-f software architecture, allow the personalisation of

learning experiences. Furthermore, the benefits of adopting

grid technologies in the personalisation process are here

described.

Eventually, our conclusions and future works are

explained.

2 The ELeGI model for creation and delivery

of adaptive UoL

The ELeGI approach to the creation and delivery of a

formal learning experience consists of the definition of a

general learning model, able to support different peda-

gogical models, allowing to automatically generate a unit

of learning (UoL) and to dynamically adapt it during the

learning process according to the learner’s behaviour. For

the learning model presented in [3], a UoL is somewhat

delimited as educational object, such as a course, a

module or a lesson structured as a sequence of learning

activities represented by learning objects and/or learning

services.

To produce an operational process that allows to create

and deliver a UoL, the theoretical learning model uses

three specific models: knowledge model, learner model and

didactic model. The operational process works on different

structures, that are, for the knowledge model, the macro

ontology (MO), the generic contextualised ontology

(GCO), and the specific personalised ontology (SPO); for

the learner model are the learner’s profile and the context

profile, and for the didactic model are the learning expe-

rience model (LEM) and the didactic method.

The overall operational process to build an adaptive

UoL is shown in Fig. 1.

The operational process can be divided into the three

following processes, each of them addressing to a specific

phase:

1. Knowledge building process is the formalization of

knowledge related to a domain. Three types of

ontology defined according to the knowledge model

are used: MO, GCO, and SPO. The MO formalizes the

knowledge about a domain. The GCO takes into

account a particular context and/or target group. GCO

derives from the MO by adding metadata to concepts.

In the same way, the SPO takes into account the

features of a single learner. SPO can be obtained from

a MO or from a GCO while the information needed for

the metadata annotation are gathered from the

learner’s profile. Both context profile and learner’s

profile are defined in the learner model.

2. UoL building process purpose is to assemble a UoL

by using the ontologies produced in the knowledge

building process. Firstly, the learning objectives to be

achieved, identified by some target concepts within a

GCO or SPO, and the skeleton of the structure to be

used for the whole learning experience (i.e. a lesson

plan), called LEM, must be specified. After that, the

automatic construction of the UoL can be started.

Through the target concepts and the cognitive state

of the learner’s profile, it is possible to generate from

the ontology a contextualised and/or personalized

ordered sequence of concepts (learning path) needed

to explain the target concepts. Then, a workflow of

learning activities is produced by sequencing the

learning activities corresponding to the didactic

method (DM) associated (specified through metadata

fields) to each concept of the learning path. This

workflow is merged with the LEM to produce a

UoL.

3. UoL delivery process entails the run time execution of

the UoL. It performs the operations needed to discover

the resources satisfying the metadata specifications

contained in the learning activities of the UoL and to

bind them on-the-fly to the learning activities of the

UoL. Specific assessment activities are introduced in

the UoL in order to check the progress, to update the

learners’ profile, and to provide remedial work if

needed.

The UoL delivery process is depicted in the Fig. 2

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3 The virtual scientific experiment model

The virtual scientific experiment (VSE) model fits some

experiential learning fundamental aspects within a cogni-

tivist/constructivistic vision: the active role of the learner

(user centred), the importance of context (situated learning)

and collaboration (collaborative learning). These consider-

ations generate a model, representing one of the many

possible interpretations adopted by the current tendencies.

The conceived model combines Kolb’s approach [4] with

the ‘‘Theory of Didactic Situations’’ by Brousseau [5]. For

these reasons, the VSE model is seen as a particular didactic

method based on an inductive-experiential approach.

The VSE model is depicted in Fig. 3, through a

sequence of four macro-phases: presentation, practical

situation, abstract situation and institutionalisation.

The presentation phase provides a description of the

didactic experience that the student is about to start.

The practical situation represents the phase where the

learner lives the concrete experience. This phase is char-

acterised by the simulation and the presence of a

collaborative environment where the concrete and personal

learners’ experience could be mediated by the interaction

with other learners. The phases evolves as follows: (1)

active situation: the learner is personally, actively and

interactively involved in the execution of a simulation, by

moving and manipulating the objects of the simulation

through a set of controls that modify the real-time simu-

lation behaviour, (2) collaborative learning: the learner is

able to mediate his/her personal knowledge through peers’

interaction, (3) assessment: a variety of questions, tables,

and other activities useful to evaluate the learning process

Specification of the whole Learning

Experience

Didactic Model (DM)

Knowledge Model (KM)

General structure of the Learning

Experience

Retrieval and organization of

propaedeutic concepts

Learning Path (LP) as sequencing of

elementary concepts with metadata (EMC)

Knowledge about the course

to be created

Application of Didactic Method to the LP

Sequence of Learning Activities according to

the LP

Didactic Model (DM)

Composition of the selected MDE and the

previuos sequence

Design of the Learning Experience

Didactic Model (DM)

Execution of algorithm for choosing and binding LOs and

Services associated with previous Learning

design

Unit of learning

Delivery of the UoL

Personal knowledge of student

Implication

knowledgeStudent

Retrieval of Model of Didactic

Experience (MDE)

Retrieval of target of learning (TL)

Set of elementary concepts with

metadata for TL

Fig. 1 UoL building process

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developed in early phases are here submitted to the learner,

(4) addressed situation: the learner, in case of a failing

assessment result, is involved in a facilitated didactic sit-

uation. During this phase, the learner can re-enter the

collaboration (collaborative learning) with the other peers,

to fill his/her gaps and, eventually, can be submitted to a

new assessment (assessment) in order to test his/her real

cognitive state, and (5) knowledge institutionalisation: the

procedural and semantic correctness of the concepts, that

the learner learned autonomously, is approved.

Outcome: Personal Knowledge

Outcome: ( Mediated) Personal KnowledgeOutcome : Personal Knowledge

NOT SUCCESSFUL

SUCCESSFUL orSITUATION END

Outcome: ( Mediate) Personal Knowledge

Outcome: ( Mediate) Personal Knowledge

Outcome: Personal Knowledge

Outcome: ( Mediated) Personal KnowledgeOutcome: Personal Knowledge

NOT SUCCESSFUL

SUCCESSFUL orSITUATION END

Outcome: ( Mediate) Personal Knowledge

Outcome: ( Mediate) Personal Knowledge

Fig. 3 The VSE model

Fig. 2 UoL delivery process

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The abstract situation aims at extrapolating an abstract

model representing, for instances, the law of an experi-

ment. Therefore, the abstract situation has exactly the same

whole structure as the practical situation although the

simulation of the practical situation is replaced by an

experiment, where the learner can interact either with

theory or its practical implications.

Finally, the institutionalisation phase constitutes the

transition from the intuitive knowledge, firstly extracted

from the analysis of a practical situation and secondly from

the abstract situation, to more advanced types of mental

schemas.

The VSE model will be delivered as a didactic method

within the general learning model described in the previous

section.

4 The ELeGI software architecture for formal learning

The ELeGI software architecture for formal learning [6]

(a.k.a. ELeGI-f software architecture) can be defined as

‘‘domain verticalization of the semantic grid improved with

tools, services, standards and technologies for the educa-

tion and training’’ and is presented in Fig. 4.

The grid layer provides a set of infrastructure services

and other services useful to create and manage a virtual

organisation (VO) [7]. This layer is devoted to the VO

operational management and provides an implementation

of both the specification of the open grid services archi-

tecture (OGSA) web services resource framework (WSRF)

basic profile [8] and the services defined in the OGSA V1

[9].

The effort to define the architecture is focused on the top

of the grid layer and the most relevant services belong to

the learning layer, that is mainly devoted to the execution

of the processes related to the learning model. This layer

can be logically divided in two sub-layers.

The first one, the environment management services

sub-layer, provides services and tools to support the crea-

tion, operation, evolution, and maintenance of a learning

community. Functionalities for semantic annotation, dis-

covery and composition of educational contents and

services are provided in the semantic annotation, discovery

and composition subsystem, while functionalities allowing

intra and inter community asynchronous and synchronous

communications are grouped in the communication/col-

laboration sub-system.

The second one, the learning services sub-layer, pro-

vides services and tools to support the execution of the

three processes of the learning model. Of course, there are

services and tools to create and manage the ontologies

(ontology management sub-system), the learner’s profile

(learner model management sub-system) and the didactic

model (didactic model management sub-system), that

represent the three basic structures of the learning model.

The personalization sub-system aims at dynamically

adapting and delivering educational contents and services,

matching the learner’s needs and preferences according to

his/her profile.

The learning experience management sub-system allows

applications or other services to access and manage cour-

ses, modules, and other learning experience (e.g. allocating

student, staff, etc.), while contents and services orchestra-

tion sub-system deals with issues of units of learning

execution, which are described using the IMS learning

design (IMS-LD) constructs [10].

Finally, the application layer uses the services provided

by the underlying layer or their composition to implement

application in the e-learning domain. The portal also

belongs to this layer and, according to the research on grid

portals, is designed by exploiting the web services for

remote portlet (WSRP) standard [11].

In addition to the standards and technologies (e.g. RDF,

OWL, OWL-S) of the semantic grid reference model by De

Fig. 4 The ELeGI-f software

architecutre

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Roure et al. [12], the ELeGI-f software architecture fore-

sees: (1) the adoption of IMS-LD specifications, (2) a well

defined set of educational services in the learning layer,

and (3) the adoption of the WSRP standard.

IMS-LD is a cornerstone of our vision. It is a specifi-

cation used to formalize learning scenarios that can

describe a wide variety of pedagogical models, including

group work and collaborative learning. It does not define

individual pedagogical models; instead it provides a high-

level language, or meta-model, that can describe different

models. Without entering into details regarding the lan-

guage, we want emphasise that our approach to investigate

the integration of the IMS-LD specification in grid systems

in order to achieve our goal, is motivated by the convic-

tions that: (1) dynamicity and adaptiveness of grid

technologies can bridge some gaps of the current frame-

works based on IMS-LD, providing effective benefits from

the viewpoint of reuse and repurposing of learning activi-

ties, and (2) semantic grid technologies can provide

advantages in the binding phase during the deployment of a

learning scenario enabling on the fly tailoring with respect

to the learner’s preferences and expectation, making the

learning experience much more attractive from the user’s

perspective.

5 A key enabling technology for the ELeGI-f software

architecture: IWT grid aware

In this section, we present the current state of our research

with respect to the ELeGI-f software architecture as well as

a key enabling technology of the first prototype of the

architecture, namely IWT grid aware (IWT GA) [13]. IWT

GA is the grid version of intelligent web teacher (IWT)

[20] learning platform and arises from the re-engineering

and integration of IWT and GRASP. The latter is a service

oriented grid middleware developed in the frame of the

homonymous FP5 project [15], and successively re-fac-

tored in order to be WSRF compliant by exploiting the

WSRF.NET implementation [16] of the University of

Virginia Grid Computing Group.

It is worth mentioning that our approach has started

from existing technological solutions (like GRASP mid-

dleware and IWT-GA) in order to gain advantages from the

well defined set of services and functionalities offered by

those technologies (both at the grid middleware level and at

the learning level) and also to speed-up the design of the

ELeGI-f software architecture.

Figure 5 present the relationship between IWT GA and

the ELeGI-f software architecture.

The IWT GA exploits OGSA complaint data services to

virtualise repositories and databases and GRASP services

for VO management operations, including the negotiation

on the basis of service level agreement (SLA) documents

and the semantic discovery of services and resources which

provide many of the functionalities of the grid layer of the

ELeGI-f architecture.

On the top, IWT GA presents a set of services belonging

to the learning layer. Specifically: the semantic annotation

and discovery services (providing a partial implementation

of the semantic annotation discovery and composition sub-

system of the ELeGI-f architecture), a LEM authoring tool

(belonging to the didactic model management sub-system),

a learner’s profile authoring tool (belonging to the learner

model management sub-system), the knowledge represen-

tation tool (KRT) an ontology authoring tool able to create

contextualised ontologies (belonging to the ontology

management sub-system), a learning intelligent advisor

(LIA) service, able to compute a personalized learning path

taking into account the learner’s profile and the ontology

representing the knowledge about a didactic domain

(belonging to the personalization sub-system), a IMS-LD

compliant engine (belonging to the content and service

orchestration sub-system) and the driver services, that

provide the virtualisation of educational resources, from

simple LO to more complex UoL. The driver services

implement the WSRP producer’s behaviour, in a way that

they are also able to generate GUIs (the portlets).

Conference XP [17] is the IWT GA collaboration tool

and is used to provide video-conferencing capabilities (for

student collaborations, tutoring activities, etc.).

Finally, the IWT GA portal is based on the adoption of

WSRP and related portlet concept as a way to design user-

centric portals that can be dynamically adapted to a context

[18].

6 The added value of service oriented grid

for education and personalisation of learning

experience

In this section, we draw your attention to the added value

of a service oriented grid for education and, in particular,

for the personalisation of learning experiences.

The added value of grid technologies is graphically

shown in Fig. 6.

At the bottom of the picture some learning key issues

(most of them addressed by ELeGI) are pointed out while

at the top some grid features (that ELeGI is able to provide

exploiting the grid middleware capabilities and the services

of the environment management layer) are presented.

As shown in the mentioned figure, the grid features

have a direct impact and provide added value on the

processes coming from the theoretic models of ELeGI,

namely the general learning model for ELeGI-f and the

collaboration and conversational (C&C) processes for

Pers Ubiquit Comput

123

ELeGI-i. Actually, the picture is not able to show another

added value of grid technologies having such a strong

impact on the execution of the processes: dynamicity,

adaptiveness, and ubiquitous and seamless access to het-

erogeneous resources. Therefore, each arrow that we are

about to describe in the following, is obtained in a

dynamic and adaptable way (to contexts, profiles, envi-

ronments, devices, resources, etc.).

Going down into the picture, we see that, starting from

the processes, a set of learning services belonging to the

ELeGI learning services layer have been defined to address

the learning key issues.

Contents &Services Orchestration

Semantic Annotation,Discovery &Composition

Sub- System

Infrastructure Services

Role&Memb.Management Sub - System

Grid Layer

PersonalizationSub- System

Learner Model Management Sub-System

Learning Experience Management Sub- System

Su

ppo

rtS

ervi

ces

Ontology Management Sub-System

E-Learning ApplicationApplication Layer

Learning Layer

Learning Metadata

Sub-System

Se

man

tic

Se

curi

ty

Communication/Collaboration Sub-

System

Didactic Model Management Sub-System

EnvironmentManagment Services

GRID Middleware for VO Management

LearningServices

GRASP ServiceData Services

Driver ServicesPersonalisation (LIA)

Onotology authoring tool (KRT)Learner Profile authoring tool

LEM authoring toolSemantic Annotation and Discovery

Confrence XP

WSRP portal

UoL engine

Fig. 5 IWT GA deployed on the ELeGI-f software architecture

Fig. 6 The grid added value in

ELeGI

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Moving from the grid technologies, we have selected

five features providing added value. In the picture, each

arrow points to the process on which it has the main impact

even if all the arrows have (more or less) an impact on all

the processes.

The first one is the knowledge representation and man-

agement. This functionality, typical of the semantic grid, is

widely exploited in the knowledge model and in the relative

knowledge building process and, of course, can be exploited

in the collaborative activities of the C&C processes.

The second feature we have pointed out is the semantic

enhancement and late binding of services and resources. As

for the previous feature, this is ‘‘inherited’’ from the

semantic grid and has a strong impact on the processes of

UoL building and delivery (for late binding of educational

resources matching the profiles), in the knowledge model

and knowledge building process (e.g. contextualisation of

ontologies GCO), as well as in the collaborative activities

whenever there is the need to perform a search of services

and tools. The above functionalities help addressing most of

the highlighted learning issues and in particular the domain

knowledge management, content authoring, the personali-

sation, definition and management of learning context.

The third one, namely the service oriented architecture

and virtualisation, is probably the most important of them.

The advantages of a SOA for the learning are presented in

Ref. [19]. Actually, this feature inherits those advantages.

Furthermore, we have added to the advantages presented in

Ref. [19] also the benefits coming from the exploitation of

the OGSA reference model, that is a well defined set of

services to manage a VO based on a set of WS-* standards,

the state and lifetime, and to associate manageability

information to services.

Indeed, the VO concept is really useful for ELeGI since

it allows to create a distributed learning environment in

which different educational institutions, individual learn-

ers, tutors can have easily access to computational and

educational resources in ubiquitous way. This functionality

provides benefits while addressing all the identified learn-

ing key issue. For instance, it allows to manage all the

learning resources as services based on standard interfaces

(e.g. improving the interoperability among educational

resources, allowing an easy content management) and

supports the community/group creation and management.

Following there are other important features:

The resource provisioning, sharing and management is

exploited in all the cases of C&C processes as well as

during the execution of formal learning experience. The

advantages arising from the exploitation of this feature, in

the former case, are noticeable since for instance in a

community a collaboration group share resources in a

transparent way and the resources themselves are provided

on-demand. For formal learning experiences, the feature is

necessary in order to select suitable computational

resources to deploy and execute UoL on the basis of SLA

parameters. This is a basic step in order to guarantee QoS

and, as a consequence, the learner’s satisfaction during the

execution of the learning experience.

The community management feature, finally, has a

direct and visible impact on all the C&C processes. Indeed,

this feature is strongly based on the grid technologies

capability to manage VO but it is enhanced through spe-

cific functionalities as for the role, identity and

membership management as well as the support for virtual

learning communities.

7 A case of study: the process to create and deliver

a personalised learning experience

over a heterogeneous network

In this section we present and describe as a case of study a

VSE explaining the Torricell’s law. In this context, we are

mainly interested in presenting the research results of the

process we have adopted to create and deliver a persona-

lised learning experience (based on the VSE model) over a

heterogeneous network. A detailed description of the case

of study is given in Ref. [21] where the interested readers

can referred to for more information about the experiment.

The Fig. 7 presents the deployment infrastructure of the

EleGI services showing the three main nodes of our case

study. The three nodes of the network provide heteroge-

neous resources and services.

The Salerno’s node (IT) consists of four hosts running

the GRASP middleware services (Data service, Service

Locator and Service Instantiator). It also contains the Portal

that represents the access point to the VO, the content and

service orchestration service that allows the execution of

the UoL, the learning metadata service that allows the

metadata based search of resources and services and the

driver services for the passive LOs and the mathematical

model of the Torricelli’s experiment (the VCLab driver)

that will be executed by the VCLab1 engine in the

Bochum’s node of the VO. The Bochum’s node (DE)

consists of one host running the GRASP middleware and

the VCLab engine and the Milton’s node (UK) provides

only the enhanced presence service (e.g. a wrapper around

the jabber server of BuddySpace [22], that is a collabora-

tive tool developed in the frame of the ELeGI project). The

Globus Toolkit 4 [23] middleware is installed on this node.

1 Virtual Laboratory for Automatics and Control Engineering

developed at the University of Bochum, in the scope of WP9 of

ELeGI. This tool can provide university students with easy access to

engineering applications at anytime and from any computing

environment

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It is worth mentioning that our research objective, with

respect to this case of study, was: (1) to evaluate service

oriented grid technologies with respect to dynamicity and

adaptiveness to the creation and delivery processes of a

personalised learning experience over a heterogeneous

network (e.g. allowing run-time binding of resources and

services in a UoL and/or selection of more suitable com-

putational resources on which to execute high-demanding

virtual scientific experiments), (2) to evaluate semantic

methodologies to improve the run-time search of resources

and services matching learner’s preferences and pedagog-

ical needs (e.g. annotation of educational contents), and (3)

to evaluate portlet standards (e.g. WSRP) and technologies

to aggregate services’ presentation in IMS-LD player.

In the following section we have avoided to report all

the details related to the execution of the knowledge

building, the UoL building process and the UoL delivery

processes, since readers interested in those details can refer

to Ref. [21].

8 The ELeGI architecture and approach for dynamic

adaptive and personalised learning experience.

In the creation of a personalised learning experience, the

UoL building process makes use of the knowledge created

during the knowledge building process to assemble a UoL

with an automatic flow of actions. This process starts with

the specification of the learning objectives to be achieved,

identified by some concepts within the ontology (GCO or

SPO), and the design of a skeleton of the overall structure

to be used in the whole learning experience, called LEM.

The UoL construction is completed by other three steps: (1)

extraction of a personalised learning path (ordered

sequence of concepts) from the selected GCO/SPO

according to the chosen learning objectives; (2) binding of

each concept, in the learning path obtained in the previous

step, with a learning activity2 including formalized

requirements for learning objects and/or learning services

that best fulfil the needs (expressed by the annotated

metadata values) of the concept; (3) packaging of the

learning activities, obtained in the previous step, within a

UoL respecting the IMS-LD specifications.

In the execution of the UoL building process, several

services of the ELeGI software architecture for formal

learning are exploited. The main one is the personalisation

service that is able to browse ontologies and to reason on

the learner’s profile in order to create a suitable sequence of

concepts needed to explain the target concept, to build the

workflow of learning activities associated to the concepts

of the learning path, and to merge this workflow with a

LEM (described in IMS-LD) in order to produce the UoL.

The personalisation service is based on the learning intel-

ligent advisor (LIA) component [14] of the IWT grid

aware.

Next, by exploiting the content and service orchestration

service, the UoL delivery process uses the UoL packaged

to run a contextualised, personalised, adaptive and multi-

user learning experience accessible by a common web

browser.

The contextualisation comes from the UoL production

process that is strongly based on a knowledge formalisation

carried out through the use of GCOs that, as we have

previously said in this section, is a contextualised knowl-

edge structure focusing the learning experience towards a

specific target classroom of learners.

The personalisation comes from two sources: the use of

SPOs and the adoption of a late binding strategy for

learning objects and learning services. For this binding

strategy, grid capabilities to access in ubiquitous and

seamless way the educational resources and services is

extremely useful. Indeed, when a learner accesses a

learning activity, the system has to bind concrete learning

objects and learning services to its environment (a para-

digm in IMS-LD language representing a container to

collect content and services). The selection of learning

objects and learning services to bind, is based on the formal

requirements included in the specification of the learning

activity in the UoL package. Obviously, the requirements

can be augmented at run-time with other information

deriving from, for instance, the device used by learners. So,

the learners can enjoy the most effective sequence of

learning activities available at that time.

The adaptation comes from the possibility for the

ELeGI UoL delivery engine to assemble and deliver a

remedial work for learners who did not successfully

overcome the assessment points in the learning experi-

ence. The remedial work is adapted especially focusing

on those concepts the learner has not well acquired. In

particular, in the case of a practical situation (according to

the VSE model) the personalisation service, after the

evaluation of the assessment results, will automatically re-

configure the simulation (fixing some parameters) in order

to guide (addressed situation) the student towards a better

understanding of the concepts that he/she has not properly

understood.

Eventually, an important feature of the UoL delivery in

ELeGI is the multi-users modality. The multi-users feature

(and mostly the ELeGI learning model, exploiting its

didactic model) allows the delivery of learning experiences

based of a wide range of pedagogical strategies, also

including didactic methods focused on the collaboration

between learners in the same virtual classroom.

2 A Learning Activity is a paradigm provided by IMS-LD language (

http://www.imsproject.it).

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9 Conclusions and future works

The VSE based on the Torricelli’s law has been executed

on the first prototype of the ELeGI software architecture.

This case of study, from one side, has validated the

learning model and the processes to create and deliver

personalised learning experiences. It is worth mentioning

that the case of study has turned to be a good testbed also to

validate some of our architectural and technological

decisions.

From a technological point of view, the case of study

has proved the goodness of grid as enabling technologies

for ubiquitous computing.

Most important, it has been showed how grid capabili-

ties to access educational resources distributed over the

network are very relevant in achieving the personalisation

of learning experiences. As evidenced in the previous

section, in fact, the personalisation service in the delivery

of a personalised learning experience strongly relies upon a

run-time binding strategy for learning objects and services

that are dynamically discovered and accessed in the VO.

Last but not the least, we have also noticed that by

exploiting the combination of IMS-LD and service oriented

grid middleware and standards we can have clear benefits

with respect to traditional IMS-LD frameworks concerning

the following points:

• Dynamicity and effective re-use of resources: exploit-

ing the ELeGI software architecture it is actually

possible to re-use independently all the ‘‘building

blocks’’ of a UoL (ontologies, LEMs, DMs, etc.), that

are resources semantically annotated and discoverable

on-the-fly over the VO presented in Fig. 7.

• Extensibility wrt services integration: exploiting the

ELeGI software architecture it is possible to use, in the

UoL, any kind of service or educational resource

virtualised as a service and rendered by WSRP Portlets.

Future works will concern the full implementation of the

ELeGI-f software architecture, which has been conducted

using an incremental approach, with the review and

improvement of the work already done, and exploiting

the feedback received from the first implementations,

including the enhancement and the addition of the services

supporting informal learning, and an improvement of the

semantic annotation and discovery mechanisms.

Acknowledgments This work is partially supported by the Euro-

pean Commission under the Information Society Technologies (IST)

programme of the 6th Framework Programme for RTD: project

ELeGI, contract IST-002205. This document does not represent the

opinion of the European Community, and the European Community is

not responsible for any use that might be made of data appearing

therein. We are very pleased to thank all the people involved in the

ELeGI project and, especially, the ones involved in the Architecture

Definition, Design and Implementation.

References

1. Gaeta M, Ritrovato P, Salerno S (2005) Making e-learning a

service oriented utility: the European learning grid infrastructure

project. In: Towards the learning GRID: advances in human

learning services. ‘‘Frontiers in Artificial Intelligence and

Applications’’, IOS, Amsterdam, August 2005, pp 63–75. ISBN:

1-58603-534-7

2. Foster I, Kesselman C (1999) The grid: blueprint for a new

computing infrastructure, Morgan Kaufmann

3. Albano G, Gaeta M, Salerno S: E-learning: a model and process

proposal in the International Journal of Knowledge and Learning,

Fig. 7 The VO deployment

infrastructure for the Torricelli

law experiment

Pers Ubiquit Comput

123

vol 2, issue 1/2 2006 Inderscience Publisher, ISSN (Online):

1741–1017 - ISSN (print): 1741–1009, pp73–88

4. Kolb DA (1984) Experiential learning. Prentice Hall, Englewood

Cliffs

5. Brousseau G (1997) Theory of didactic situations in mathematics.

Kluwer Academic, Dordrecht

6. Gaeta A, Gaeta M, Orciuoli F, Ritrovato P (2005) Enabling

technologies for future learning scenarios: the semantic grid for

human learning. In: Proceedings of the 2 international workshop

on collaborative and learning applications of grid technology and

grid education CLAG + Grid.edu, in conjunction with the IEEE

international symposium on cluster computing and the grid

(CCGrid 2005), Cardiff, 9–12 May 2005

7. Foster I, Kesselman C, Tuecke S (2001) The anatomy of the grid:

enabling scalable virtual organizations. Int J Supercomput Appl

15(3):200–222

8. Foster I et al.: OGSATM WSRF basic profile 1.0. Recommen-

dation GWD-R GGF, OGSA WG. Available at https://forge.

gridforum.org/projects/ogsa-wg/document/draft-ggf-ogsa-wsrf-

basic-profile/en/38. Accessed 7 Sep 2005

9. Foster I et al.: The open grid services architecture, version 1.0.

Report GFD-I.030. GGF, OGSA WG. Available at http://www.

ggf.org/documents/GFD.30.pdf. Accessed 25 Jan 2005

10. IMS (2003) Global learning consortium, IMS learning design

v1.0 final specification. Available at http://www.imsglobal.org/

learningdesign/index.cfm

11. Schaeck T, Thompson R: Web services for remote portlets

(WSRP) Whitepaper. Available at http://www.oasis-open.org/

committees/tc_home.php?wg_abbrev=wsrp. Accessed 28 May

2003

12. De Roure D, Jennings NR, Shadbolt NR (2003) The semantic

grid: a future e-science infrastructure. In: Berman F, Fox G, Hey

AJG (Eds), Grid computing––making the global infrastructure a

reality, Wiley, London, pp 437–470

13. Capuano N, Gaeta A, Laria G, Orciuoli F, Ritrovato P (2005)

How to use grid technology for building next generation learning

environments. In: Ritrovato P, Allison C, Cerri SA, Dimitrakos T,

Gaeta M, Salerno S (Eds) Towards the Learning GRID: advances

in human learning services, vol 127, frontiers in artificial intel-

ligence and applications, pp 252. ISBN: 1-58603-534-7,

Hardcover, pp 182–192 (November 2005)

14. Capuano N, Gaeta M, Micarelli A, Sangineto E (2003) An

intelligent web tutoring system for learning personalization and

semantic web compatibility. In: Proceeding of PEG 2003, St.

Petersburg, Russia

15. Dimitrakos T, Gaeta M, Laria G, Mac Randal D, Ritrovato P,

Serhan B, Yuan F, Wesner S, Wulf K (2003) An emerging

architecture enabling grid based application service provision. In:

Proceedings of 7th IEEE international conference EDOC, Bris-

bane, Australia, 16–19 Sept 2003, pp 240–250

16. Humphrey M, Wasson G (2005) ‘‘Architectural foundations of

WSRF.NET’’. Int J Web Serv Res 2(2):83–97

17. ConferenceXP, Microsoft ConferenceXP homepage, available at

http://www.conferencexp.net/community/default.aspx

18. Allison C, Michaelson R (2004) Design consideration for an

ELeGI portal. In: Proceedings of the 3rd international LeGE-WG

workshop, Berlin, Germany, 3 Dec 2003

19. K., Blinco et al. (2004) Trends and issues in E-learning infra-

structure development. White paper for alt-i-lab 2004 prepared on

behalf of DEST (Australia) and JISC-CETIS (UK), July

20. Albano G, Gaeta M, Ritrovato P, IWT: an innovative solution for

AGS e-Learning model, vol 3, (double) issue 2/3, 2007 of the

international journal of knowledge and learning. Interscience,

New York, ISSN (Online): 1741–1017, ISSN (Print): pp 1741–

1009

21. Gaeta A, Gaeta M, Meo G, Orciuoli F, Ritrovato P, Stefanucci S

(2006) How the ELeGI software architecture enables the creation

and delivery of adaptive unit of learning: the implementation of

the VSE model, proceeding of the workshop 4d: next generation

in technology enhanced learning, e-challenges, Barcelona, Spain,

25–27 Oct 2006

22. BuddySpace, Available at http://kmi.open.ac.uk/projects/

buddyspace/

23. Globus Toolkit, Available at http://www.globus.org/toolkit/

Pers Ubiquit Comput

123