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: [email protected]
P. Ritrovato
e-mail: [email protected]
M. Gaeta
Dipartimento di Ingegneria dell’Informazione e Matematica
Applicata (DIIMA), University of Salerno,
Via Ponte Don Melillo, 84084 Fisciano (SA), Italy
e-mail: [email protected]
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
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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.
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