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"RGridE-Learning: The Role of Grid Computing in E-Learning"
Christina Braz, 2005 Page 1 of 36 Released 03/10/2005 7:18 PM RGridE-LearningV5.pdf
"RGridE-Learning:
THE ROLE OF GRID COMPUTING IN E-LEARNING"
Date: 3-Oct-05
Summary
This document presents the state-of–the-art regarding the converging field of Grid Computing technology
and e-learning. It addresses how Grid Computing has been employed in wired and mobile (wireless) E-
Learning illustrated here by a diverse spectrum of domains such as Grid Learning Services, Collective
Intelligence Sharing, Semantic Web, and Grid Clients for Mobile Devices.
Reference PROJECT I – "RGridE-LEarning: The Role of Grid Computing in E-Learning"
Course DIC9340 Knowledge-Based Learning Environments
Program of study Ph.D. in Cognitive Computing
Department Computing
Institution Université du Québec à Montréal
Professor Roger Nkambou, Ph.D.
Prepared by Christina Braz
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TABLE OF CONTENTS
1 INTRODUCTION 2
2 ISSUES AND OPPORTUNITIES 2
3 FUNDAMENTAL CONCEPTS 2
3.1 Grid Computing 2 3.1.1 Definition 2 3.1.2 Grid Components 2 3.1.3 Reasons for Using Grid Computing 2
3.1.3.1 Taking Advantage Of Underutilized Resources 2
3.1.3.2 Parallel Central Processing Unit (Cpu) Capability 2
3.1.3.3 Applications 2
3.1.3.4 Virtual Resources And Organizations For Collaboration 2
3.1.3.5 Access To Additional Resources 2
3.1.3.6 Resource Balancing 2
3.1.3.7 Reliability 2
3.1.3.8 Enhanced Management 2 3.2 E-Learning 2
4 STATE-OF-THE-ART OF LEARNING GRIDS 2 4.1 Definition 2 4.2 A General Portal Framework for Learning Grid 2
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4.3 Grid Learning Services 2
4.3.1 Dynamic Service Generation 2
4.3.2 Grid Learning Object 2
4.3.3 Learning Grid Infrastructure 2
4.3.3.1 Semantic And Ontological View Of The Grid 2
4.3.3.2 The Role of the Agents and Networking 2
4.3.3.3 Real-World Content-Rich Environments 2 4.4 Collective Intelligence Sharing 2 4.5 Semantic Grid in E-Learning 2 4.6 Grid for Mobile E-Learning (m-Learning) 2
5 CONCLUSION 2
6 REFERENCES 2
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1 INTRODUCTION
The "RGridE-Learning: The Role of Grid Computing in E-Learning" presents the state-of–the-art regarding
the converging field of Grid Computing technology and e-learning. It addresses how Grid Computing has
been employed in wired and mobile (wireless) e-Learning illustrated here by a diverse spectrum of domains
such as Technological Infrastructure on the Grid, Semantic Grid, Collective Intelligence Sharing, and Grid
for Mobile E-Learning. The RGridE-Learning is based in part on seven representative papers from renowned
authors [Nkambou&al04a], [Nkambou&al04b] [Millard&al05a], [Millard&al05b], [Page&al05],
[Pankratius& Vossen99], [Jonquet&Cerri05] in the E-Learning and Grid Computing research communities.
Let us remind you the very basic idea of e-learning that is to create the conditions enabling and facilitating to
improve human knowledge. For that reason, we have been noticing among many other developments huge
efforts from the current e-Learning (including Intelligent Tutorial Systems) and Grid Computing research
communities in order to effectively develop technological infrastructures for the Learning Grid focusing for
example on real-world learning scenarios (e.g. support learner construction of theories or performance of
experiments) [enCOre05], [CoAKTing05], [CombeChem05], [Bachler&al04], [Underwood&al04], and
[Yatchou&al04] among many other developments in this area. In Section 4.3.3.3, we will be describing in
more details about content real-world learning scenarios.
Grid Computing is a set of distributed computing resources available over a Local Area Network1 (LAN) or
Wide Area Network2 (WAN) that become visible to an end user or application as one huge virtual computing
system. The objective is to create virtual dynamic organizations through secure, unlimited power,
information access, synchronized resource-sharing among users, institutions and resources.
E-Learning Grid in turn represents the amalgamation of Grid Computing and E-Learning in which of Grid
Computing functionalities are incorporated into E-Learning systems. E-Learning Grid is a collection of
computational resources on demand to match computational needs through a sort of generic service
matchmaking (e.g. a series of algebra exercises (computational resources) is presented to a learner online in
order to improve her/is mathematics capabilities (computational needs)) on the Web. In fact, we can argue
that E-Learning Grid is an expanded notion of diversified resources provision including data resources,
intelligent agent resources and even human tutorial and mentoring resources.
1 Local Area Network (LAN) is a computer network that covers a relatively small area. Most LANs cover a single building or group
of buildings. A system of LANs can be connected over any distance through telephone lines and radio waves, creating a Wide Area
Network (WAN). 2 Wide Area Network (WAN) is a network of computers connected to each other over a long distance, for example on the Internet.
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2 ISSUES AND OPPORTUNITIES
The rapid growth of the Internet and Web has brought an increasing attention in Web-based distributed
computing, and several projects have been developed in the e-Learning domain which intends to take
advantage of the Web as an infrastructure for running distributed and parallel applications.
Secondly, in an era when computers systems carry out more and more of "knowledgeable" work from
individuals and when a significant number of these individuals hold more and more different roles in
different, open (e.g. new agents searching for a learning object (LO) might join and existing ones leave in a
Multi-Agent System (MAS)) and complex systems, the need of mastering a large number of systems and
subsystems of broadly conflicting natures, including specially functionalities and interactions with several
human specialists (i.e. instructional designers, teachers, experts, technicians, operators, etc.) who are
frequently distributed over physical space is critical. In the case of a supervision of wide-ranging networks
(telecommunications, transport and distribution of power, etc.) for example a sort of large-scale application,
the supervision and management of such subsystems are obviously distributed at the nodes of the network,
and require for that large number of technicians to achieve the global objective which is to make the system
work.
Thirdly, those open and complex environments mentioned above have also to provide learning resources for
people on the move that will enable end users anytime, anywhere to download courseware or any other kind
of learning resources such as learning objects on portable digital devices such as personal digital assistants
(PDA), cellular phones, laptops or even tablets PC3. The content built by authors and learners are generally
handled, stored and exchanged in units of learning objects (LOs). Generally speaking, LOs are units of study,
exercise, or practice that may be utilized in a single session, and they are characterized as reusable particles
that may be authored separately of the delivery medium itself and be accessed dynamically (e.g. over the
Web). That's all about nomadic computing4 and information environment which is a heterogeneous collection
of interconnected technological and organizational elements, which allows physical and social mobility of
computing and communication services targeted to users. Social mobility here refers to the ways in which
and the ease with which individuals can move across different social contexts and social roles, and be still
supported by the technology and services [Lytinen&Yoo02]. As society and organizations becomes more
dynamic, individuals adopt multiple social roles at an increased intensity and need their information services
adjusted in a large scale as well (e.g. as a mobile learner might move from one site to another so it is crucial
to maintain her/is current state/profile, that is the management of user mobility).
Fourthly, another important point from [Berstis02] is that “the standardization of communications between
heterogeneous systems created the Internet explosion. The emerging standardization for sharing resources,
along with the availability of higher bandwidth, are driving a possibly equally large evolutionary step in grid
computing”, and also in e-learning domain.
Finally, the use of Grid Computing in conjunction with wired and wireless e-learning will provide basically
an end-to-end high-bandwidth access and a vast range of distributed computing resources to end-users (e.g. a
learner). This integration may be in theory difficult because of the need to achieve various qualities of
3 A Tablet PC is a computer that allows you to write on its Touch screen with a stylus, as you would with a PDA. The screen is,
however, much larger - about the size of a typical notebook screen. In addition to data entry, you can also use your stylus to emulate
many of the things you would ordinarily do with a mouse. The fundamental approach is power to exceed your needs and simplicity
for unparalleled friendliness. 4 Nomadic computing is the use of portable computing devices (e.g. handhelds) in conjunction with mobile communications
technologies to enable users to access the Internet and data on their home or work computers from anywhere in the world.
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service5 (QoS) which is becoming progressively more important as networks get more populated and more
refined Internet applications and services get spread out. Grid computing even if it is in its very beginning is
already being effectively employed in many scientific e-learning applications where large amounts of data
have to be handled and/or stored as we can observe in several examples in the Section 4, State of the Art of
Learning Grids. In this way, we can assume that the Web has the potential to be a platform for parallel and
collaborative work as well as a key technology to create a pervasive and ubiquitous Learning Grid-based
infrastructure.
3 FUNDAMENTAL CONCEPTS
This section introduces some important concepts that are useful in understanding the landscape underpinning
the application of GRID computing for e-Learning.
3.1 Grid Computing
The research in Grid computing began in 1990 investigating the design and development of a similar
infrastructure called the “computational power Grid” [Foster&Kesselman99] for wide-area parallel and
distributed computing. The purpose for computational Grids was primarily driven by large-scale, resource
(computational and data) intensive scientific applications that demand more resource than a specific
computer (PC, workstation, supercomputer, or cluster) could supply in a single administrative domain. A
Grid enables the sharing (Figure 1), selection, and aggregation of a wide variety of geographically distributed
resources including supercomputers, storage systems, data sources, and specialized devices owned by
different organizations for solving large-scale resource intensive problems in science, engineering, commerce
and also in e-Learning (e.g. photo-realistic visualizations of a complex body model in real-time and display
the computation result on a remote screen).
“Computational Grids are widely regarded as the next logical step in computing infrastructure, following a
path from standalone systems, to tightly linked clusters, to enterprise-wide clusters, to geographically
dispersed computing environments. Generally speaking, we could consider the Grid as the new enabling
technology to transparently access computing and storage resources anywhere, anytime and with guaranteed
Quality of Service (QoS)” [Bruneo&al03]. Currently, grid computing mostly serves computationally
intensive scientific and enterprise applications and operates on cluster computers6 or supercomputers. The
main differences between grids and usual clusters are that grids connect agglomeration of computers which
do not entirely trust each other, and because of that run more like a computing utility than like a single
computer. In addition, grids usually support more heterogeneous agglomerations than are generally supported
in clusters.
5 Quality of service (QoS) refers to a broad collection of networking technologies and techniques. The goal of QoS is to provide
guarantees on the ability of a network to deliver predictable results. Elements of network performance within the scope of QoS often
include availability (uptime), bandwidth (throughput), latency (delay), and error rate. 6 A computer cluster is a group of loosely coupled computers that work together closely so that in many respects it can be viewed as
though it were a single computer. Clusters are commonly (but not always) connected through fast local area networks. Clusters are
usually deployed to improve speed and/or reliability over that provided by a single computer, while typically being much more cost-
effective than single computers of comparable speed or reliability.
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At present, one of the most important implementation of grid computing is the Globus Toolkit Version 3
(GTv.3) with its Open Grid Services Architecture (OGSA). It provides a bundle of services and
specifications which can be integrated separately to form a grid middleware. The component model of GTv.3
is founded on grid services that are in fact Web services7 with particular extensions (e.g. interfaces) for use in
grids. The main idea behind OGSA is to build each of the grid middleware layers as shown in Figure 1 by
utilizing suitable grid services.
3.1.1 Definition
The term "grid" makes use of an analogy to an electrical power grid that is the access to computational
resources should be straightforward as the ordinary access to an electric power grid. It means that the grid
would let users take advantage of processing power off the Internet as without effort as electrical power can
be pulled out from the electricity grid that generates that power to our homes. Additionally, a grid user should
not have to take care of how and where this computational power s/he is presently using comes from.
Grid computing refers to a distributed, high performance computing and data management infrastructure that
integrates heterogeneous resources (e.g. storage, computing and/or communications systems, human
collaborators, etc.) and at the same time offers common interfaces for all these resources using standard and
open protocols and interfaces. It is important to highlight that we should not confound cluster computing with
Grid computing. The former generally contains a static number of processors and resources physically
contained in the same or fixed locations, which can be interconnected together. The latter refers to
heterogeneous resources, integrating storage, networking, services and resources. Resources might comprise
machines from different vendors, running various operating systems, and including the capability to control
the workload [Nkambou&al04b].
7 “A Web service is a software system identified by a Uniform Resource Identifier (URI), whose public interfaces and bindings are
defined and described using XML. Its definition can be discovered by other software systems. These systems may then interact with
the Web service in a manner prescribed by its definition, using XML based messages conveyed by Internet protocols” [Aus04].
Figure 1: Layers in a grid middleware [Pankratius&Vossen03].
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Another definition of grid computing from [Foster&al01] is viewed like “Grid technologies and
infrastructures as supporting the sharing and coordinated use of diverse resources in dynamic, distributed
“virtual organizations” (VOs)8”. As noted by this author, the main problem that lays the Grid concept is
coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations.
Figure 2: A high-level view of the Grid and interaction between
its entities in an application service context [Hoheisel&Der03], [Fraunhofer05].
As we have already stressed here, the Grid can be considered as an integrated computational and
collaborative environment. In Figure 2, we depict the high-level view of activities within the Grid. The users
interact with the Grid Resource to resolve problems, which in turn executes resource discovery, scheduling,
and the processing of application jobs on the distributed Grid resources.
From the end-user (e.g. learner) point of view, Grids may provide the following types of services:
Computational Services [Baker02], Data services [Hoschek&al00], and Application services
[Casanova&Dongarra97].
8 Two examples of VOs: the application service providers, storage service providers, cycle providers, and consultants engaged by a
car manufacturer to perform scenario evaluation during planning for a new factory, or members of an industrial consortium bidding
on a new aircraft.
Internet
Resource Grid
Job Submission
Web Server
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3.1.2 Grid Components
The key Grid components of a Grid environment can be viewed in Figure 3: � Portal or User Interface for
user to launch applications that will use the resources and services supplied by the Grid; � A mechanism to
provide security (authentication, authorization, data encryption, etc.); � The Broker identifies the available
and appropriate resources to use within the grid; � Scheduler: Once the resources have been identified, the
next step is to schedule the individual jobs to run on them; � Data Management: if any data – including
application modules – have to be shifted or made accessible to the nodes where an application’s jobs will run,
so there needs to be a secure and reliable method for moving files and data to several nodes within the grid;
� Job and Resource Management: GRAM supplies the services to launch a job on a particular resource,
check its status, and retrieve its results when it is finished.
Figure 3: A high level view of the Grid components [Jacob03].
3.1.3 Reasons for Using Grid Computing
In this sub-section, we highlight briefly from [Berstis02] what grid computing is able to do independently of
e-learning domain whose will be then addressed in Sections 3.2 and 4. We consider that this sort of
detachment gives us the opportunity to better seize the variety and strength of grid computing features and its
relation to e-Learning.
3.1.3.1 Taking Advantage of Underutilized Resources
To illustrate this topic, consider to process an existing application on a distinct machine. The machine on
which the application generally is processed may be busy as a result of a peak in activity and the application
could be processed on an unoccupied machine somewhere else on the grid (e.g. a batch job that uses a
considerable amount of time processing a collection of input data to generate an output collection).
Another utility of the grid is to improve equilibrium resource utilization (e.g. distribute computations and
data transparently across all computers in a grid). In case of unforeseen peaks of activity that normally
demand more resources in an organization, the applications that are related to grid can be moved to
underutilized machines during such peaks.
Virtual Computing Resource
User
Legend: GSI: Grid Security Infrastructure. GASS: Grid Access to Secondary Data. GRAM: Grid Resource Allocation Manager.
Monitoring & Discovery Service (MDS)
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3.1.3.2 Parallel Central Processing Unit (CPU)9 Capability
In case of applications that use algorithms, they can be for example split into running parts. A CPU
demanding grid application can be viewed of separately small "subjobs", each running on a distinct machine
in the grid as consequence the application turn into more "scalable" (e.g. a good scalable application would
finish 10 times faster if it uses 10 times the number of processors).
3.1.3.3 Applications
It is important to highlight that not all applications can be converted to execute in parallel on a grid and reach
scalability. There is no automatic conversion and even tools for converting these applications to take advantage of
the parallel capabilities of a grid yet. However, new applications have been already designed for parallel execution
following promising grid protocols and standards.
3.1.3.4 Virtual Resources and Organizations for Collaboration
Real or virtual organizations can share not only files but several other resources such as equipment, software,
services, licenses, and others. These resources are called "virtualized" enabling them more standardized
interoperability among diverse grid users.
3.1.3.5 Access to Additional Resources
Bigger quantities of other resources and special equipment, software, licenses, etc. can be accessed on the
grid (e.g. a user would like to raise her/is total bandwidth to the Internet to put into operation a data mining10
search engine, the job can be split among grid machines that in turn have separated connections to the
Internet). Moreover, the grid allows more sophisticated access, possibly to remote medical diagnostic and
robotic surgery tools with two-way interaction from a distance.
3.1.3.6 Resource Balancing
The grid enables a larger total virtual resource through the contribution of single machines (e.g. (i) an unforeseen
peak load can be forwarded to quite unoccupied machines in the grid; (ii) a lower priority job can be suspended
and executed again later to give room for a higher priority one).
3.1.3.7 Reliability
Let us consider power supplies and cooling systems that are operated on distinctive power sources that can
fire up generators if service power is cut off. As the systems in a grid can be quite inexpensive and
geographically scattered, a power failure at one location will not affect the other parts of the grid (e.g. grid
management software can automatically resubmit jobs to other machines on a grid when a breakdown is
identified).
9 Central Processing Unit (CPU) is the brains of the computer. Also referred to simply as the processor or central processor, the CPU
is where most calculations take place. In terms of computing power, the CPU is the most important element of a computer system. 10 Data Mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of
data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition.
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3.1.3.8 Enhanced Management
Administrators of IT departments can alter for example policies to better assign resources. The grid provides
management of priorities among various projects. Previously, each project in an organization might have
been responsible for its own IT resource hardware and the costs related with it; this hardware might be
underutilized while another project encounters problems requiring more resources caused by unforeseen
events.
3.2 E-Learning
Electronic Learning (e-Learning) is the distribution of a learning, educational or training material by
electronic means (e.g. Internet, Intranet, CD-ROM, digital video disc (DVD)) using therefore a computer or
electronic device (e.g. mobile phone, PDA, etc.). It uses network technologies to create, deliver, and facilitate
learning, anytime and anywhere and delivers distinct, comprehensive, dynamic learning content in real time,
aiding the development of collaborative communities of knowledge, and also connecting different types of
users such as learners with experts, experts with instructional designers, etc. Moreover, it is a phenomenon
that delivers accountability, accessibility, and opportunity allowing people and organizations to keep up with
the rapid changes that characterize the World Wide Web.
According to [Pankratius&Vossen99], in an e-Learning system, the key players are the learners (i.e. student
or company’s employee/apprentice) and the authors (i.e. teachers or instructional designers) but also trainers
and administrators. Authors conceive content, which is put in storage in a learning management system
(LMS) and in a database as well. That content may be updated, or exchanged with other systems. A LMS is
controlled by an administrator, and it interacts with a runtime environment which is addressed by learners
who might be instructed by a trainer. It is important to highlight that these three components of an e-Learning
system might be logically and physically distributed (i.e. installed on different machines) and provided by
diverse vendors or content suppliers. Now, to make this distribution viable, standards seek to ensure plug-
and-play compatibility [IMSGLOBAL01].
E-Learning systems should provide customization of features to a specific learner’s needs
(e.g. Knowledge-Based Learning Systems that include Intelligent Tutorial Systems). A Knowledge-Based
Learning System is a program that is built to model problem solving skills of humans; it is considered as a
“learning interactive environment. They put further the accent on the simulation of the model than on its
construction. The learner “learns” by modifying the parameters and observing the consequences of her/is
actions in the simulated environment [Nkambou05]. Intelligent Tutorial Systems (ITS) are learning systems
one-to-one (tutor/learner). The goal here is to reconstitute the behaviour of an intelligent tutor in order to
provide a personalized education to the learner.
As a matter of fact, the learners can diverge considerably in several aspects such as their prerequisites,
their abilities, their goals for dealing with a learning system, their rate and way of learning, and the time and
money the learner is able to spend on learning. Hence, an e-learning system is generally able to supply
and offer content for all those groups (e.g. a student who would like to learn about database concepts or for a
company employee who would like to grasp company processes and their execution). In order to implement
this system, a learning platform needs to encounter some of the most important requirements such as
personalization, customization and adaptation, the integration of a multiplicity of materials, the
“responsiveness” of the system towards the user (e.g. a "troublemaker" agent can provide pedagogical
interventions of the system [Mengelle&Frasson96]).
As we have previously mentioned, in an e-learning context, learners and authors interact through units of
learning objects that can be accessed dynamically (e.g. over the Web). These learning objects can be stored in
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a database as well as any other information pertinent to a learning system such as user profile (personal data,
learner profile), course maps, LO sequencing or presentation information. “E-learning consists of a
multiplicity of complex activities such as content authoring or learner tracking and administration which
interact with resources (including people such as learners and authors), with one another (some activities
trigger others), and with the outside world (such as existing software systems) in a predefined way”
[Pankratius&Vossen99].
We consider the e-learning standpoint of [Cerri05] an excellent and pertinent one. The author affirms that e-
learning according to own experience and also the research in the domain, that e-learning is really not an
electronic variation of traditional Education. Actually, the environment of the education is completely
different, and also the cognitive and social attitude of humans. E-Learning is in this way “not an application
of technologies to human learning, in the sense that assuming to know what to apply (the technologies) and
how (the pedagogy) one puts things together and the result will be a success (people learn). On the contrary,
each serious effort has to be considered unique in the sense that it requires specific technologies and specific
pedagogical principles to be developed and applied in a trial and error fashion within a specific context. This
is the fundamental challenge of e-Learning: services and products have to be combined differently each time,
according to each e-Learning situation”.
In the wireless field, Mobile learning delivered in electronic mobile devices, anytime, anywhere, reveals new
possibilities for technology to augment or facilitate the processes of learning and teaching (e.g. mobile
participants) and also to offer new applications that are not viable with conventional desk-top setups.
Consider the following scenario: a group of students, all equipped with a PDA, that for their Archaeology
spring assignment are working on the Field Trip project. During their activity they store information,
experience, emotion in photos, video clips, text notes, audio comments, etc. The mobile aspect of e-learning
will be detailed in the Section 4.6. Grid for Mobile E-Learning (m-Learning).
4 STATE-OF-THE-ART OF LEARNING GRIDS
In this section, we first introduce some basic concepts of Learning Grids and then we address in a very
segmental way how Grid Computing has been currently employed including aspects of infrastructure,
services and resources such as Grid learning services, semantic Grid in e-learning, collective intelligence
sharing, and Grid for mobile e-learning.
4.1 Definition
Many e-Learning platforms and systems have been developed and commercialized. In general, these
platforms are based on Client-Server, on Peer-to-Peer (P2P), or lately on Web Services architectures, with
effectively significant limitations such as scalability, availability, distribution of computing power, and
storage capabilities. Hence, e-Learning is at this time set out in fields (e.g. sciences, medicine, etc.) where
superior requirements concerning those limitations are not essential. Consider this scenario from
[Pankratius&Vossen99] where e-Learning systems can arrive at their frontier: “a medical school where
anatomy students examine the human body and prepare for practical exercise. Up to now, it is vastly
impossible to compute, say, photo-realistic visualizations of a complex body model in real-time and display
the computation result on a remote screen. With the advanced functionality of an e-Learning grid, students
could be provided with the possibility to grab, deform, and cut model elements (e.g. organs) with the click of
a mouse. Basically as before, the e-learning system could support the learner by giving advice on how to cut
or give feedback for the actions, but beyond that virtual reality at local machine would become possible and
improve the understanding of the subject considerably”.
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According to [Nkambou&al05], "the "Learning Grid" refers to the promise of projects that pool together
instructional materials on distant computers. The Grid provides a wide range of available and potential
learning services and resources and does not simply refer to taking advantage of the multiplying effects of
connectivity. It supports the personalized use of the collective intelligence provided by networked computers
and supports the exchange, negotiation and dialogue within and among virtual, evolutionary and pervasive
learning communities" (i.e. collaborative learning that corresponds to human knowledge sharing).
4.2 A General Portal Framework for Learning Grid
An example of a general portal framework for Learning Grid [Yang&Ho05] can be seen in Figure 4.
Considered by the authors as the “Education Grid”, it makes use of the NMI’s Open Grid Computing
Environments (OGCE) Portal framework [OGCE&NMI05] that provides a portal architecture that supports
virtual organizations consisted of scientists and project developers, and also provides the Application
Programming Interface (API)11
for the development of reusable, modular components that may be used to
access the services being developed within the Grid organization. Grid portals enable communication
between grids and the outside world. User portals offer special services to specific members of the public and
researchers.
11 Application Programming Interface (API): is a set of definitions of the ways one piece of computer software communicates with
another. It is a method of achieving abstraction, usually (but not necessarily) between lower-level and higher-level software.
Internet Internet
Remote School A
Remote School B
OGCE Portal
Middleware Globus
Open Grid Services Architecture
Local CAI Platform
Data Web Multimedia (VOD)
Computational Grid Data Grid
Figure 4: A general Learning Grid Architecture [Yang&Ho05].
Computer-Assisted Instruction (CAI)
Video On Demand (VOD)
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This architecture (Figure 4) applies Grid Computing technologies to incorporate inactive computer resources
in schools to reduce costs and make efficient use and sharing of applications and resources. Therefore,
schools with restricted budgets can also acquire better services and huge teaching resources using Grid
technology [Yang&Ho05].
4.3 Grid Learning Services
4.3.1 Dynamic Service Generation
The STROBE Model [Jonquet&Cerri05] introduces a social oriented model that is based on interaction
centred of agent representation and agent communication. It represents how an AA (Artificial Agent) might
“learn” dynamically (at run time) at the Data, Control and Interpreter level, especially focusing on
"learning by being told" mode (i.e. use of AAs that learn (by being told) during conversations with other
AAs, therefore that demonstrate a dynamic behaviour that adapts to the context.
The model depicts how agents might execute the interactive, dynamic generation of services on the Grid.
Services here are constructed interactively between agents depending on a conversation. The approach
consists of integrating selected features from MASs and agent communication, language interpretation in
applicative/functional programming and e-learning/human-learning into a simple view that benefits
interactions, including control. “The main characteristic of STROBE agents is that they develop a language
(environment + interpreter) for each of their interlocutors. The model is inscribed within a global approach,
defending a shift from the classical algorithmic (control based) view to problem solving in computing to an
interaction-based view of Social Informatics” [Jonquet&Cerri05].
The kind of MAS employed by STROBE model is as a matter of fact a Multi Artificial and Human Agents
System (MAHAS), a system where AAs and HAs might interact and exchange information and knowledge
effortlessly, where computers might make suggestions to humans and humans to computers, where
collaboration and cooperation is infinite, where an agent might ask to another one to do a task or help it, a
system which might progress dynamically in time and with a nondeterministic behaviour and finally a system
where queries (i.e. problems to solve) and their solutions might come into view through interactions.
Dynamic Service Generation refers to services constructed on the fly by the provider according to the
conversation it has with the user and implies learning, as we mentioned above, interaction. It is a
nondeterministic process depending on the conversation, interaction between two agents. Dynamically
generated services in fact represent a new concept of service involving a collaborative generation of
knowledge (i.e. learning).
We agree with [Jonquet&Cerri05] that when taking into account grid computing, we bring fundamentally to
mind agents and that the grid, they stress, is an evolution of both Web and agent research. According to their
example, we are not able to shift from a Client/Server model based network (e.g. Web) to a distributed
resource sharing system (e.g. grid) without taking into consideration societies of autonomous interacting
agents providing dynamically delivered services (i.e. dynamic services generation) by means of interaction
among AAs and Human Agents (HAs) existing in the society. As the authors emphasize, Learning Grids (i.e.
societies of learning agents) in fact turn into societies of agents (i.e. HAs and AAs) supporting human
learning.
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A Meta-Level Learning: "Teacher-Student" Dialogue
According to the authors, the goal of education is to change the interlocutor’s state. Actually this shift is
realized after evaluating new elements carried by the communication. The example in Figure 5 demonstrates
that a STROBE agent can alter its way of perceiving things (i.e. of evaluating messages) by "changing" its
dedicated interpreter while communicating.
Let us consider the following scenario:
The goal of education is to change the interlocutor’s state. This change is completed after evaluating new
elements carried by the communication. The example in Figure 5 shows that a “STROBE agent can change
its way of seeing things (i.e. of evaluating messages) by "changing" its dedicated interpreter while
communicating” [Jonquet&Cerri05]. Actually it is a typical "teacher-student" dialogue. An agent
teacher requests to another agent student to broadcast a message to all its correspondents.
Nevertheless, student does not initially know the performative12
used by teacher. As a result,
teacher conveys two messages (assertion and order) explaining to the student the way of
processing this performative by changing the function which interprets the messages (evaluate-
kqmlmsg). In the end, teacher expresses again its query to student and gets then satisfaction. The
dialogue occurring in the experimentation is described Figure 5. After the last message procedure, the
student function devoted to the evaluation of message (evaluate-kqmlmsg) is modified. The
corresponding code in its environment dedicated to this conversation is changed. Then student agent can
process broadcast messages sent by the teacher.
12 A performative states an agent intention, for example, broadcast, assertion, order, etc.).
Figure 5: Learning of the performative broadcast learning teacher-student dialogue.
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The authors emphasize the importance of seeing the learning process as co-construction of knowledge. “An
interaction between two entities is a process that implies an action to occur on the interacting entities. That
means that interactions have some consequence on these entities, i.e. changes of state. However, each entity
may only change state if the change is performed by the entity itself. For HAs, these changes can be
“learning”, the definite purpose of ITS. In e-learning scenarios, it is quite unlikely that human learning occurs
on the simple basis of interacting with a static system. Real interactions modify both entities, including the
artificial one. The learning process should be seen as a co-construction of knowledge (social constructivism).
For this process to be cumulative (i.e. unlimited) in knowledge production, the two entities have to learn from
each other during this process and re-inject what they learn into the loop [Jonquet&Cerri05].
4.3.2 Grid Learning Object
In [Pankratius&Vossen03], e-learning grid architecture is proposed including a Learning Management
System (LMS) and a Core Grid Middleware (CGM). However, what really differentiates it from others e-
learning grid architectures is a new concept of a “Grid Learning Object” (GLOB) for using the grid in e-
learning applications
The LMS and the CGM which are based on Web Services and Grid services are depicted in Figure 6. The
LMS interacts transparently with the CGM hence a learner is not conscious of the grid, and all s/he needs is a
Java-enabled Internet browser to use both the LMS and the CGM.
Core Grid Middleware (CGM)
The CGM in fact implements several layers according to below:
1. The Fabric layer: it is implemented as a Java applet which offers the same interfaces to all
resources in the grid. The user accesses a Web page with her/is Web browser to authenticate in the
grid.
2. The Connectivity layer: it refers to the Grid Login service that performs all access
control operations to the grid middleware.
3. The Resource layer includes an Information service which is aware of the status and type
of all resources in the grid.
4. The Collective layer contains a Broker which implements a grid scheduling algorithm and
also is in charge of distributing computations and data across the grid.
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Figure 6: Architecture of an E-Learning Grid including the Core Grid
Middleware and the Learning Management System [Pankratius&Vossen03].
Link CGM/LMS Link CGM/LMS Link CGM/LMS
GLOB
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Learning Management System (LMS)
The LMS in general manages all learning activities. A learner who uses a PC for a learning session interacts
only with the LMS and as we have already mentioned is not conscious of a grid in the background. The LMS
provides grid-content or not grid-content functionality related. It is important to mention that all Web
Services of the LMS are accessed via Web pages, in this way the learner only needs a Web browser to use the
LMS.
Since the learner is logged in and authenticated, s/he can access a Web page for course management (i.e.
functionality implemented in a Course Mgmt service). An Ontology service supports the semantic
search for courses. After that the authoring service offers an environment to create, edit, and publish e-
learning content in the ContentRegistry, in this way they can be discovered by the LMS.
The Web services that provide e-learning content is comprised of three essential components: the
learning object (e.g. usually a lesson) or a course comprising of many learning objects; the
assessment element which defines online tests and finally the metadata for search engines that details
the content in a standardized mode.
Other services also are provided by the LMS such as Discussion boards, Chat rooms where learners can
interact with instructors or other learners and ask questions, a Progress Monitor and an Accounting
service.
Integration of Grid Middleware and LMS
The LMS Login Service allows the e-learning PC to become a resource in the grid. As soon as the
learner authenticates her/him on the Web page which in turn is connected with the Login Service of the
LMS, the Fabric layer applet of the grid can be transported as mobile code and be set off locally on the
e-learning PC. So this makes possible the communication with the grid.
Grid Learning Object (GLOB)
A Grid Learning Object (GLOB) is an advanced version of the conventional learning object with grid
functionalities. In Figure 7, we can examine the structure of a GLOB which was designed by the authors in
order to include both traditional e-learning content and content that makes use of grid functionalities. The
GLOB is wrapped by a Web Service which enables it be effortlessly integrated into the LMS (Figure 6).
Moreover, the Web Service offers procedures to access specific parts of the GLOB, to convert content (e.g.
from XML to HTML), or to produce online tests. The GLOB is compounded of several parts: An
Overview (a lesson), metadata (used to find GLOBs), many reusable information objects
(RIOs), and a summary. The User Interface may be implemented as a Java Applet that coverts user
input (e.g. mouse clicks) into tasks for the grid service in the application layer (e.g. a query to recalculate a
3D model).
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Figure 7: The structure of a Grid Learning Object (GLOB) [Pankratius&Vossen03].
According to the authors, e-learning management systems and Grid computing can effectively work together
especially for applications or learning scenarios where superior computational power is demanded or even
the tool sets on which learning should be carried out are too high-priced to be granted to each and every
learner. Future issues to be investigated consist of for example transactional guarantees for service executions
over a grid (e.g. atomicity or recovery protocols that assist re-establish an operational state after a grid
breakdown).
4.3.3 Learning Grid Infrastructure
This sub-section presents the Learning grid services and also the functional requirements of a Learning Grid
infrastructure.
Learning Grid Services
In the Proceedings of the First Workshop on Grid Learning Services [GLS04], several approaches were
presented in order to develop a technological infrastructure for the Learning Grid. Contributions realized by
participants were grouped into three categories which describe the participants’ standpoints:
4.3.3.1 Semantic and Ontological View of the Grid
The first standpoint into this category refers to a Service oriented model which requires the use of semantic
tagging for the recognition of and service to individual users (personalization) [Allison&al04].
The actual content of a unity of study.
Generate online exercises for the learners.
Generate online tests for final exams.
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The second one refers to the vision of the Grid to Semantic Web technologies which presented a human
centered approach to e-learning. One of the strategies of this approach is the use of hypertext and knowledge-
based tools to augment the capacities of collaborative mediated spaces such as (e-Science) [CoAKTing05]: i)
Ontologies to enhance group discussions; ii) Knowledge-based planning and task support to enhance
process/activity discussions; iii) Scholarly discourse and argumentation to enhance collaborative meeting
activities; iv) Presence and visualization to enhance group peripheral at a distance.
The third standpoint propose a Grid Learning which can be personalized13
to an individual learner using
Semantic Web techniques applied to resources, learners’ characteristics and content categorization that are
indispensable to learners and teachers [Razmerita&Gouardères04]; this approach defines Semantic Web as “a
mesh of instructional resources linked in such a way as to be easily computable by machines on a global
scale” [Woolf&Eliot04).The semantic Grid is considered as a means to assist user-centered, personalized,
contextualized and experiential approaches as well [Gouardères&al02].
4.3.3.2 The Role of the Agents and Networking
Intelligent and autonomous agents are a kind of agents which might perform complex tasks for the learners
such as identify errors and misconceptions, recommend a diverse range of learning objects with also a
diverse spectrum of features, support learners to obtain new concepts, accomplish different goals (e.g. as in a
MAS [Woolf&Eliot04], [Roda&al03]), and respond to dynamic aspects of the environment.
The first standpoint refers to a MAS carrying out training and cognitive supervision through a network
distributed training system ASIMIL [Gouardères&al00]. Another MAS called Actor Specification for
Intelligent Tutoring Systems (ASITS) makes use of agents interacting individually with actors (e.g. human,
intelligent agents, etc.) through a common flow of messages (i.e. agents offering diagnoses, advice and
support to users such as learners, instructors, etc.).
Another standpoint concerns an agent representation and communication model derived from a social
approach to accomplish the dynamic generation of services through the interaction of Artificial Agents (AAs)
and Human Agents (HAs) (STROBE Model [Jonquet&Cerri04]). The objective is in fact to enhance HA-
learning (e-learning) by using AA-learning.
Finally, the third standpoint refers to a set of agents that handle computer-grid communication through
devices (Grid-e-Card) [Gouardères&al04].
4.3.3.3 Real-World Content-Rich Environments
The focus here is on a real world content-rich environment where services should be created according to
teachers and learners’ needs providing if possible (and strongly recommended!) real-world content-rich
environments [Allison&al04]. A diverse spectrum of Grid learning projects have been developed such as: i)
e-Qualification process (dynamic classification of users who enter the grid according to their need in their
activity domain [Yatchou&al04]); ii) Project:EnCOrE (building and using an Encyclopaedia of Organic
Chemistry by virtual communities communicating on the Web); iii) Collaborative Advanced Knowledge
Technologies in the Grid project (CoAKTinG Project) that seeks to advance the state of the art in
collaborative mediated spaces for distributed e-Science; iv) CombeChem project [Bachler&al04); v) Live
communication with remote scientists using mobile sensing equipment in the Antarctic (Antarctic Remote
Sensing Project and the Urban CO Monitoring project) [Underwood&al04].
13 On one hand, Personalization refers to make a system suitable for what a particular user needs (teacher or system demand). On the
other hand, customization refers to changing something to make it just right for you (learner).
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Functional Requirements for Grid Learning Resources Services
The goals here represent that learning services enhance classical classroom activities and make changes in
teacher practices. The functional requirements are comprised of two basic functionalities that need to be
provided: pedagogical considerations and identification of services. Pedagogical considerations refer to a set
of characteristics that should accomplish those goals according to below:
• Focus on the learner, not the teacher (student-centered methods).
• Computer-mediated teaching is well suited to support and promote constructivism teaching.
• Personalization and customization in order to improve the efficiency of computer interaction with
users and make complex systems more usable.
• Rich-environments to provide closed interactions between entities, humans and computers for
knowledge construction during e-learning sessions.
• Promote more constructivist and learner-centred learning (simulations, multimedia, virtual reality).
Identification of Services
As we have noticed above, with a diverse range of pedagogical factors involved, Grid services must be
provided in the Learning Grid such as Collaboration services (members of a community sharing and
executing tasks to reach a common goal), Communication services (services offered by the OGSA),
Customization services (pertinent curriculum for each learner), Personalization services, Support services,
Learning styles services, Searching services, and finally Qualification services (qualify a resource for a
curriculum, assess the quality of resources (e.g. user comment and rating) and identify learner capabilities
[Vassileva&al99)].
4.4 Collective Intelligence Sharing
The extensive augmentation of information nodes, the diversity of computers in complex networks, the
cognitive overload, and the transactional distance14
[Moore73,93] demand for an appropriate set of learning
services and devices for the Grid. Consequently, find the Virtual Learning Community (VLC) that shares
learner’s centers of interests is not at all an easy task (Figure 8). In [Gouardères&al04], an approach to reduce
cognitive overload and transactional distance for VLC on the grid trough a computer-grid communication
device called “Grid-e-Card” is proposed.
14 Transactional distance refers to the psychological space created between the teacher and the learner. It is a function of two
variables: dialogue and structure. Dialogue refers to the nature and the quality of communications between the teacher and the learner
while structure relates to the rigidity of the course, the organization of the instruction and the teachings' strategies [Gouardères&al04].
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The Grid-e-Card, defined by the authors as a VLC auto-organizer device for collective intelligence sharing
on the grid, intends to bring together users according to their signature for collective intelligence sharing in a
social context: knowledge they have acquired, objectives they wish to attain or learning services
corresponding to their requirements [Gouardères&al04]. The methodology employed is based on P2P-agents
that handle user’s electronic portfolio (e-Portfolio) as “knowledge prosthesis” and exploit e-Learning
qualification (e-Qualification) processes as aggregation methods to dynamically assemble people in pertinent
VLCs.
Figure 8: How to find the VLC coping with my interests? [Gouardères&al04].
Generally speaking, e-Qualification refers to a context where an individual or global assessment of human
actors, of architectures or devices takes place. In [Gouardères&al04], that notion is expanded to the Grid to
indicate the following: i) the exploration that is realized to find the best VLC for the user; ii) the iterative
construction of knowledge from an early state of knowledge to an expert knowledge state; iii) the assessment
of the trainee when progressing inside her/is community during debriefing. “The e-Qualification process
helps the self-organization of nodes by the dynamic classification of people who enter the grid
according to their need in their activity domain” [Gouardères&al04].
Figure 9: e-Qualification loops to a user to integrate a VLC [Gouardères&al04].
The Figure 9 shows some of the e-Qualification process that takes place when the Grid-e-Card is plugged
(See Figure 9 for a general overview of the system). From the trainee e-Portfolio, the learner human agent (L)
finds the corresponding virtual learning community (V1, V2, V3 or V4).
LEGEND L Learner human agent V Virtual Learning Community
???
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Grid-E-Card and P2P Communication
In the learning process in Figure 10, every user that necessarily belongs to a community, is connected to the
learning grid through her/is Grid-e-Card. Each trainee is characterized by an agent in her/is community. It is
important to highlight that all the members of a community have a piece of their e-Portfolio that is
comparable to other’s one. In fact, that mutual piece is the signature of the VLC. In order to consent to a
trainee integrate a specific community, rules are activated by agents where those rules are settled on the
virtual learning community signature and the trainee e-Portfolio features. As described by the authors, in this
process, several agents are activated according to the basic processes below:
• A user agent is associated to every user Grid-e-Card. It communicates with her/is community and
automatically triggers the e-Qualification process. It will answer to identification and authentication
requirements of the system through a matchmaking dialog which at the end accept a member in the
community or not.
• The process called “matchmaking” evaluates the content of the messages with the user agent and
enables the categorization of agents in VLCs according to the pertinence of their knowledge in the e-
Portfolio or the goal of the new member in the loop. As the agents adopt a social behaviour so that
they are able to reason on the knowledge states of the other members to which her/is is linked, and
take into consideration her/is own knowledge that other agents will would like to share. As the
authors mention, “the basic loop of the e-Qualification process: mapping of peers into a common
VLC tacitly qualify each one in a shared competence group which is the Virtual Learning Group
Communities (VLGC). From a technical point of view agents need to processes in P2P mode in order
to be organized in groups and interact in pairs, and should be mobile due to the exigency of the grid
environment” [Gouardères&al04].
Figure 10: Grid-e-Card: Basic Dynamic View of the system [Gouardères&al04].
LEGEND LA Learning Agent GeC Grid-e-Card S Service K Knowledge B Broadcast
User
Mirroring: Display the state of the newcomers in relation to others.
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A Learning Grid scenario can be seen in ASIMIL, an e-Qualification process (Figure 11) application from
the aerospace domain that stands for “A Network Distributed Simulator Training System” [ASIMIL05]. The
experimental framework is simulation-based intelligent. A P2P review process which is executed by
autonomous agents (i.e. knowledge, ergonomic, psychological). Each agent scans separately a common
stream of messages coming from other actors (Human, intelligent agents, physical disposals) (see Table 5).
They perform coalitions to supply a given community of users (instructors, learners, moderators, etc.) with
diagnoses, advice and help among actors in the community. A dedicated P2P Agents architecture for
perception and qualification of erratic user’s behaviours has been constructed which consists of a cognitive
monitoring based on intelligent agents.
Figure 11: The e-Qualification process in ASIMIL [ASIMIL05].
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4.5 Semantic Grid in E-Learning
The Semantic Grid refers to applying Semantic Web technologies to the Grid therefore setting off new
avenues for automation. Semantic Web is “a mesh of instructional resources linked in such a way as to be
easily computable by machines on a global scale” [Gouardères&al02]. Let us consider the proposed E-
Learning Grid Infrastructure [ELeGI05] for example in a widespread Grid environment, so that it would be
viable to automatically integrate new services into a local learning environment.
Independently by offering a distributed system of superior performance of computational resources, the Grid
must also enable structured access to the generated data and also an environment within the collaboration can
take place (e.g. meetings between researchers, shared access to experiments, etc.). The Grid nowadays can be
seen as a “composite of computational grid, data grid and collaborative grid functionalities” [Page&al05].
The CoAKTinG (Collaborative Advanced Knowledge Technologies in the Grid) Project [CoAKTing05]
seeks to enhance the state of the art in collaborative mediated environments for distributed e-Science. It
encompasses four tools: instant messaging and presence notification (BuddySpace), graphical meeting and
group memory capture (Compendium), intelligent “to-do” lists (Process Panels) and meeting capture and
replay. These tools in fact are incorporated into existing collaborative environments and via shared ontology
in order to exchange structure, promote improved process tracking and navigation of resources before, after,
and while a meeting occurs.
BuddySpace
BuddySpace [Eisenstadt&al03; Vogiazou&al05] is an Instant Messaging environment with both client and
server functionality lengthened to improve presence awareness. It presents automatic list construction and
intelligent service discovery on the server, and also the graphical visualization of users and their presence
states in an image, geographical or conceptual map according to Figure 12.
Figure 12: BuddySpace showing a virtual organisation and presence indicators, (a) with live/clickable
presence dots superimposed on geographical and office locations, and (b) with the office dots superimposed
on a conceptual map depicting KMi’s research themes as generated from an underlying ontology
[Eisenstadt&al03; Vogiazou&al05].
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Compendium
Compendium [CoAKTing05] is a hypermedia software tool for publishing Dialogue Maps (Figure 13) which
are concept networks which structure Issues, Ideas and Arguments in a dialogue, linked to background
multimedia documents and internet resources. It is better defined as a knowledge management environment
for supporting personal/group discussions and memory, merging hypermedia, modelling and mapping skills
[Conklin&al01].
Issue-maps can be used in learning perspectives to for example sum up: background information about a
difficult issue to be tackled, evidence as it is collected and how it is appropriate to issues under debate,
contributions to online discussions forums.
Compendium is usually used as a means of assembling together diverse resources into a common place for
organization and analysis. For example, students, teachers and researchers can make use of Compendium’s
maps to drag and drop multimedia resources onto a map. Also, Open University PhD students are making use
of Compendium as a visual database for managing their literature reviews, as a manner to improve their
research questions, and to support virtual supervision of e-PhD students as well.
Figure 13: Example use of Compendium by an instructional designer to organise issues, ideas and resources
from diverse sources: (1) The key problem to be addressed is framed as a question; (2) open courseware
resources are dropped from a web browser onto the map; (3) an existing course Unit 3 is added in response to
the issue about one of the web resources; (4) a catalogue of resources is created; (5) a relevant email is linked
to as a response to two different questions [CoAKTing05].
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I-X Process Panels for Task-oriented e-Learning
The purpose of I-X research project [Tate&al02] is to create a propitious environment for mixed-initiative
(i.e. engaging human and computer agents) synthesis tasks. In the point of view of a user, the main interface
to the I-X is the Process Panel. A Panel introduces to users the present state of the collaboration from their
individual standpoints, and enables them to dissociate activities, improve elements of the plan, delegate
issues, and invoke the automated agents, etc. all these characteristics supporting to shift the whole task
towards a finishing point.
All features of I-X seek to give confidence novice users to develop their own expertise whereas executing
tasks within the context of a distributed virtual environment of shared resources, aid agents and other users of
several levels of expertise.
Meeting Replay
The meeting replay tool [CoAKTing05] enables individuals to rethink the ideas and topics discussed after a
meeting has occurred. Some features implemented are the meeting time, location, attendees, audio/video
recordings, any presentations given (and related Web versions), and argumentation annotation from
Compendium (Figure 14).
Figure 14: The meeting replay tool [CoAKTing05].
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Finally, we describe below where the CoAKTing tools can be used in learning grid scenarios:
• BuddySpace (enhanced presence/communication): to create a Virtual Community consisting the
individual learners & teachers and to provide the “social affordance scaffolding”.
• Compendium: to capture collective thinking within a group who are physically distributed and used
to plan, structure, and access other learning resources providing significant interactive and reflective apparatus for the learner.
• I-X Process Panels: to plan and structure learning tasks, goals, and experiments and to provide
mechanism for tracking issues and tasks when part of collaboration in this manner providing critical
task-level support.
• The Shared semantic ontology -> structured metadata from the various tools can be merged with new
material to generate additional services.
4.6 Grid for Mobile E-Learning (m-Learning)
Grid services can be suitable in a mobile context as mobile electronic devices (e.g. PDAs, cellular phones,
laptops, tablets PC, etc.) change their networks (i.e. instable due to discontinuous connectivity and poor
bandwidth) more regularly than desktop installations. In this way, these mobile devices can take advantage
from being able to discover and utilize services that are local to the device (e.g. to use a projector screen to
show information that couldn’t be displayed onto a palmtop screen). Another factor is that mobile devices
also typically possess currently limited resources (i.e. less computing power) than wired devices, and can
take advantage also from the Grid’s characteristic that is to shift computation to a more powerful system.
Moreover, there is a need to deal with the transparency of the service, and the mobility of users demands
huge efforts in the design of a proper middleware [Bruneo&al03].
In the same way mobility can be very useful to e-learning in order to facilitate and enhance the processes of
learning. The former, it would enable learning resources more straightforwardly available to learners and also
teachers. The latter, it would augment learning experience through the use of mobile devices in, for example,
laboratory work and field trips [Braz04]. In this context, mobile devices can be used in place of classical
paper and pen to organize these experiences or gather learner information [Millard&al05b].
Mobile Grid computing in turn consists of providing Grid services anytime, anywhere from mobile devices.
As we mentioned above, mobile devices have low processing power, in addition its battery life is very short
and its screen is very limited in size and quality. All these restrictions bring as a matter of fact significant
advantages of using mobile Grid technology such as “mobile-to-mobile and mobile-to-desktop collaboration
for resource sharing, improving user experience, convenience and contextual relevance and novel application
scenarios. A grid-based mobile environment would allow mobile devices to become more efficient by off-
loading resource-demanding work to more powerful devices or computers [Millard&al05a]”.
In [Millard&al05a], a mobile e-learning client is proposed, “Finesse e-Learning System”, using Grid
technologies, that is, a mobile learning Grid [ELeGI05]. Finesse (Finance Education in a Scalable Software
Environment) is a Web-based collaborative learning and teaching environment for the finance domain.
Learners are able to manage on-line portfolios and buy and sell shares making use of real-time market data.
The objectives of this project were to conceive a set of Grid services that reproduced the functionality of the
initial Finesse, that is, the Finesse Grid Services (FIGS), and also to create a mobile interface for FIGS which
would enable the portfolios accessible via a PDA.
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Unfortunately, conclusions from the authors related to the examination of Grid technologies stated that none
of the technologies presently available (white color) or those still in development or may only have partial
releases (grey color) shown in Figure 15 can effectively support Grid clients on a mobile device. According
to the authors the reason why is that “…they make assumptions about the capabilities of their host
environment, for example OGSI.Net will not work with .NET CF and Globus Toolkit 3 assumes too
high a level of Java support”.
Figure 15: Grid Technologies [Millard&al05b].
The authors argue that lightweight implementations such as Mobile OGSI.NET can provide s subset of the
enterprise services, rather than a new lightweight view; even though these lightweight implementations may
be possible to run on a PDA, it is a wholly different interface onto OGSA (the “?” layer in Figure 15), that is
required to support the type of e-learning mobile Grid applications that was just described.
Open Grid Service Infrastructure
Open Grid Service Architecture
[4] [1] [5] [2] [3] [6] [3]
Web Service Resource Framework
Detailed Legend:
[1] Web Services Resource Framework (a PERL implementation of the current WSRF
definition).
[2] Web Services Resource Framework (an initial implementation of WSRF on .NET1).
[3] Globus Toolkit (a collection of services, written in Java, which can be used to
deploy and discover other services).
[4] Open Middleware Infrastructure Institute (an open source and secure Web services
platform for building Grid applications).
[5] Open Grid Service Infrastructure .NET (an implementation of the OGSI.NET
implementation for mobile devices).
[6] OGSI.NET (a container framework that allows .Net applications to access Grid
Services).
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Consequently, the only solution found by the authors was to implement the mobile Grid client using
a proxy (i.e. a browser on the PDA) that accessed Grid services running on a Web server as shown
in Figure 16.
Figure 16: Finesse proxy architecture for mobile clients [Millard&al05a].
The authors used Java Server Pages (JSPs) to invoke Java code on a remote Web server. They implement
their mobile client using JSPs. The Finesse Grid Services are deployed on the Grid, and clients are
implemented as a set of Java Beans talking to the Grid and providing suitable responses as JSP pages to
mobile devices. Requests from mobile users are input via the mobile device’s Web interface, these requests
are first handled on the proxy by client beans which then issue proper Grid Services requests to the Grid.
Once the proxy receives responses from the Grid, it generates and serves appropriate pages to the mobile
client [Millard&al05a].
The authors conclude that “...the current set of Grid technologies does not fit well with the loosely coupled
requirements of mobile e-learning and are often too heavy-weight to fit on a mobile device. Unless this is
addressed it will make the emerging e-learning Grid infrastructures inaccessible to mobile devices, and stunt
the development of novel mobile e-learning applications [Millard&al05b]”.
5 CONCLUSION
In this report, we presented the state-of–the-art regarding the converging field of Grid Computing technology
and e-learning. It addressed how Grid Computing has been employed in wired and mobile (wireless)
E-Learning illustrated here by a diverse spectrum of domains such as Grid Learning Services, Collective
Intelligence Sharing, Semantic Web, and Grid Clients for Mobile Devices.
Recently, there have been some very important developments in the Grid and e-learning coming from
research communities such as an increase of a multitude of collaborative e-learning environments and
components, the amalgamation of different technologies and learning theories (e.g. leading toward fusion of
the Grid with P2P networks and at the same time providing a co-construction of knowledge (social
constructivism), the augmentation of the capacities of collaborative learning through Semantic Web
technologies, the arrival of numerous e-learning objects (e.g. grid-enabled learning objects, learning objects,
etc.), and finally huge efforts has been developed to realize effective Mobile Grid Learning.
Deployed Finesse Grid Services (FIGS): Interest, Notebook, Portfolio,
Sharedata, Userdata.
GRID
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In our opinion, Grid computing, semantic Grid, Web services technologies and mobility are crucial factors to
be considered when providing learning resources to learners and teachers in an e-learning environment due to
the following:
• Modularity: where services are dynamically coupled at runtime.
• Interoperability: where we notice the standardization of the service interfaces.
• Extensibility: where services can be automatically and easily integrated into a local learning
environment.
• Distributed Knowledge Management: where further functionalities further than a regular SOA can
be provided such as security and state awareness.
• Suitable Trust Services: where the “intelligence of the services” [Nkambou&al04b] in relation to
the Semantics in the description, the discovery, the selection and the composition of services.
• Communication: where new possibilities are offered to users in order to effectively communicate
more easily without obstacles.
• Automatism: where conversational agents dynamically generated Learning Grid Services.
• Compatibility and expandability: where we can build learning virtual organizations for collective
intelligence sharing through the use of an e-Portfolio as an entry point for e-Qualification of a grid
learning service.
• Accessibility: where users can access from simple to complex e-learning resources anytime,
anywhere.
In a nutshell, the use of Grid Computing in conjunction with wired and wireless e-learning will provide
basically an end-to-end high-bandwidth access and a vast range of distributed computing resources to users
such as learners, teachers, instructional designers, etc.
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6 REFERENCES
It is important to highlight that several references cited in this report, “RGridE-Learning”, are included in the
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