A FLEXIBLE INFRASTRUCTURE FOR P-LEARNING: A FIRST

APPLICATION IN THE FIELD OF PROFESSIONAL TRAINING

Alain Derycke and Vincent Chevrin

Laboratory LIFL/TRIGONE, University of Sciences and Technologies of Lille, Bat 6, Villeneuve d’Ascq, France

Keywords: Pervasive-Learning, Context-Aware environments, Ubiquitous Computing, Dynamic Adaptation of

Services, Multi-channels accesses.

Abstract: With the availability of nomadic computing, and its new interaction user devices connected through wireless

networks, it is obvious that the traditional way of delivering e-Learning will be changed. This paper is

focused on a new mode called pervasive learning which relies on the potential of new IT infrastructures able

to provide dynamic adaptations of information contents and services according to various contexts. Using

our previous experiences in the design and implementation of multi-channel accesses to services (mobile-

Commerce or e-Learning) we are designing a new infrastructure, based on a Multi-Agent Systems, which

satisfies our requirements for future p-Learning systems. Its potential is illustrated through a dedicated

scenario of uses drawn from needs founded in the field of learning on demand, in the framework of a shop,

contextualized for several seller situations and professional activities. The dedicated system, called a

Personal Training Assistant, is supported, in interaction with a Smartspace, through our infrastructure.

1 INTRODUCTION

With the rapid dissemination and uses of mobile

wireless devices, there is an opportunity to enlarge,

or to modify, the scope and the nature of the

traditional e-Learning modes of education. This is

particularly required by the organizations that are

rapidly evolving, under the pressure of their users

and of their markets, whatever the users are students,

clients, citizens or members of a community. Recent

surveys, such as those conducted by (Woodill,

2006), show also that new socio-technological

approaches of the Web, the WEB2.0 umbrella; will

have a great impact on the way we will consider

exchanges of information and co-production of

knowledge in the future. So we can assume, from a

research perspective, that the features of the e-

Learning will be more open, but also more complex,

in order to incorporate all these social and

technological changes, into a co-evolving process,

occurring between the technological pushes and the

real needs of the stakeholders.

Using our previous experiences in the design,

development, deployment of e-Learning services

and technological platforms, both from the research

or real operational perspectives, and also our

experiences in research on multi-channel computer

applications in the field of e-Commerce and mobile-

Commerce, we have start a new research program

focused on the design and experimentation of new

infrastructure for pervasive learning mode. This is

done in the framework of an important collaboration

in a large regional consortium, between Universities

and several big companies from the retail industry,

and from the e-Commerce and the direct marketing

fields, sets to forecast and master evolutions of these

fields. Our present research project is deeply rooted

in the new domains of computer sciences whatever

they are called “Ubiquitous Computing”, “Context-

Aware Computing”, “Pervasive Computing” or even

“Ambient Intelligence”. But it takes also seriously in

consideration the particular needs from these

companies, and their application domains, for the

continuous training of their employees and, if

possible jointly, of their clients and affiliates. This

will be illustrated by a specific scenario of use,

simplified here, that we have kept for presentation in

this paper, among several others that we are

investigating actually.

215

Derycke A. and Chevrin V. (2007).

A FLEXIBLE INFRASTRUCTURE FOR P-LEARNING: A FIRST APPLICATION IN THE FIELD OF PROFESSIONAL TRAINING.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - HCI, pages 215-222

DOI: 10.5220/0002361902150222

 SciTePress

2 FROM E-LEARNING TO

P-LEARNING

2.1 Mobile Learning is more than

e-Learning Adaptation to Mobile

Phone

At a first look it seems that mobile-Learning (or m-

Learning) is just an adaptation of the e-Learning

systems to accesses through wireless networks,

increasing the accessibility (from the learner

location) and the “reachability” (to the learner

location). There is already a strong research interest

in the world for this new domain. It appears that m-

learning is, first, not a mode phenomenon, and

second, that it goes beyond traditional e-Learning

systems. Several research projects (Attewell, 2005)

have shown that in spite of the current limitation of

the mobile devices put in the users’ hands (i.e. a

basic GSM phone), it is possible to envision new

learning activities, less focused on rich document

interactions, but more communication oriented, in

the spirit of the CSCL mode of education. The

novelty in uses of wireless networks and mobile user

devices is the possibility to take into account the

singular context of the learner/user, for example

depending of his/her location. m-Learning is

learning that can take place anytime, anywhere with

the help of a mobile computer device. The device

must be capable of presenting learning content, in a

context sensitive way, and providing wireless two-

way communication between teacher(s) and

student(s). This is why m-Learning is different from

e-Learning due to its ubiquitous nature. However

previous researches have shown that to be effective

the user device must satisfy some requirements:

Highly portable, Individual, Unobtrusive, Available

anywhere, Adaptable to the context of learning and

the learner’s evolving skills and knowledge,

Persistent, Useful, Intuitive to use by people with no

previous experience of the technology (Sharples,

2003).

The researches on m-Learning have taken two

directions:

- The design of systems based on wireless LAN

(WiFi or Bluetooth) that organize the learning

physical space, classroom, theatre, campus, in order

to supplement the mobile user devices, in general

Personal Digital Assistant, with fixed devices

located close to the users. This is a case of what it is

called “SmartSpace”, where there is an automatic

detection of the user location and of the proximity

services. For example the PDA can be used in

conjunction with a large intelligent electronic

whiteboard in a theatre during a lecture.

- The design of specific portals, for regular

Learning Management Systems, LMS, that are able

to support mobile communication and devices on

GSM type of networks in Europe. This means

gateways for SMS supports, sometimes the support

of the WAP, or I-Mode standards, for browsing

information. But until now there are no true multi-

channel accesses to these LMS, in the way already

explored in the field of m-Commerce. Even worse

we have already demonstrated that Learning Objects

standards, such as SCORM compliant ones, can

limit the potential of a dynamic adaptation of the

learning contents to the diversity of channels (X3,

2006). Similarly, the potential of the multimodality

is not used in m-Learning, for example using the

potential of coupling voice and WAP interaction,

thanks to standards such as VoiceXML and X+V,

and the proposal for a true multimodal and device

independence Web in the future, by the W3C

consortium.

2.2 Emergence of the Pervasive

Learning Mode

In the last years a new concept has appeared to

translate the potential of Ubiquitous or Pervasive

Computing in education. This new way to use

technologies to support the learning processes has

been called either “Ubiquitous-Learning” (or u-

Learning) (Jones, Jo, 2004), or Pervasive-Learning.

(Keil-Slawick et al., 2005). In this proposal we have

adopted p-Learning to name this new field of

research. As for m-Learning, most of the authors

mention that p-Learning goes farther than uses of

new technologies provided by recent research in

pervasive computing to support e-Learning

traditional views, but that it enlarges also the view of

the learning process itself. For example for

(Bomsdorf, 2005): “Ubiquitous learning is the next

step in performing e-Learning…. Furthermore, it

enables seamless combination

of virtual

environments and physical space”. And she gives

the main characteristics of u-Learning in terms of:

Permanency, Accessibility, Immediacy,

Interactivity, Situating of Instructional Activities,

and Adaptability. It means than context sensitive

learning environments, learning material semantics,

are not necessary locked to the geographical location

of the learner, but depend of other kinds of contexts

such as his/her present role into the professional

activities and work, the history of the past learning

phases, the technological contexts (characteristic of

the present channels that can be used) and, in the

future, even the affective state of the learner. In

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accordance with Bomsdorf, we state that the central

problem is about dynamic adaptation of the learning

materials and activities proposed for several kinds of

contexts. We also share the reflection done by

(Syvänen et al., 2005) when they said “The

combination of context and adaptivity…can also

prioritize communication channels

, encourage

cooperation, and adjust information modalities”.

It is the challenges that we want to support into the

open intermediation infrastructure we are designing

in our present project. And for that purpose we will

reuse also previous researches that we have

conducted in the field of multi-channel e-Commerce

(Chevrin et al., 2005a).

3 NEW NEEDS AND NEW

LEARNING SCENARIOS FOR

EVOLVING ORGANISATIONS

3.1 Some General Hypotheses for the

Design of Future p-Learning

Infrastructures

Just to summarize we want to put emphasis on some

of our main hypotheses, which result of our previous

investigations and experiences in the field of e-

Learning, or in the field of the mobile-Commerce:

H1: New learning modes, in this case, the p-

Learning mode, and new organization approaches

will appear that enlarge or modify the traditional e-

Learning mode. This is especially true due to the

ubiquitous nature of the interaction with the learner

on the one hand, and in the other hand to the new

needs of the organization for the training of both,

and jointly, of their employees, clients, providers

and partners in accordance with the related

organizational workflows. There is also a

tremendous potential for some learning

environments and contents that are more or less

auto-produced by the learners themselves, in

collaboration with others pedagogical agents (human

or synthetic one). This leaves place for a better

involvement and an active participation of the

person to his/her learning and collaborating process;

H2: There are various “contexts” that need to be

considered for a real p-Learning system, some are

independent of the domain of application such as

technological, physical contexts, and some are

specific to the learning process and to the present

status of the learner (worker or client). An effort

must be put on the way to “contextualize” a

pedagogical scenario and to support an easy

development of the deployment of the infrastructure

needed for these p-Learning settings;

H3: The first generation of technological e-

Learning platforms, the LMS, are not suitable for the

development of p-Learning, Learning on Demand

across the value-chains, and in the context of future

agile and virtual organizations. The future will be a

collection of dedicated learning and other e-

Services, some provided outside the concerned

organization, similar to the Application Service

Provider model;

H4: The first investigations about mobile-

Learning have shown that the mobile phone user

platform can be useful if proper adaptation of both

the learning material and of the learning activities is

done. The rise of the Smartphone, supporting

universal roaming (from GSM to Wifi and local

Internet accesses for example) and different

communication medium, from Voice, Podcasting,

WAP, Web 2.0, Rich Media, to Digital TV, should

be a booster for the new learning environments;

H5: Importance of the dynamic adaptations of

materials, e-Services and activities for learning, and

Contributions

Situations, Contexts

Equipment

designers

Products data

sheets

workflow

Activity

E-Learning

services

Channels

information

User devices

informations

Ont ol ogi ca l

Model Domai n

Kno wl edge

Hyper medi a

Document s

Shop

context ual i sed

documents

Rol e

context ual i sed

documents &

Cont ext uaki sed

e- Learnni g

servi ces

Mul t i èc ha nne l s

accesses

Concret e user

i nte rf aces

Shop stocks

& marketing

Levels of

expertises

Shop local

parameters

-Initial

training

-Recurrent

training

-In office

-In the shop

-In front of

the client(s)

User profiles

e-services

available

-Local or

remote?

-Wifi or

mobile

phone

-Earphone or

not

-Smartspace

-Geolocalisat

ion

-Type of

user

devices?

-Colocation

or not

-Multiusers

or not (SDG)

Figure 1: Chain of contextualized transformation for the scenario Personal Training Assistant.

A FLEXIBLE INFRASTRUCTURE FOR P-LEARNING: A FIRST APPLICATION IN THE FIELD OF

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217

of the delivery systems and their channels or

modalities mobilized for the user interaction, in a

great diversity of contexts and of rules and strategies

of transformation, that maintain nevertheless the

pedagogical and didactical intentions.

3.2 An Example of Rich Generic

Scenario with the Associated

Transformation Chain

In the framework of our present project to design a

p-Learning infrastructure we have partnerships with

companies in the field of retail, ranging from small

retail shops to hypermarkets and huge online stores.

The following generic scenario is extracted from the

needs in matter of p-Learning coming from one of

these partners. However it will be simplified here for

reasons of clarity and of confidentiality.

The main aim is to develop a Personal Training

Assistant (PTA) for sellers working in the field of

Hifi/Video equipments. This domain is becoming

complex due to the rapid changes in the underlying

technologies with the appearance of High Definition

TV, sophisticated home video, media-centers,

Terrestrial and satellites digital TV… So an effort

for continuous education of the sellers is needed in

order to maintain quality of the relations with the

clients and his/her efficiency. The main idea is to

use hand held computer, which can be used for

several others purposes also (stocks management for

example), in order to support both the learning and

the coaching of the seller/learner in various contexts.

It could be for example a Personal Assistant

connected to the information infrastructure of the

shop. Because the development of such a training

system is costly, it is envisioned that the cost will be

shared by a cooperation across, not only all the

shops of the distributor, but also across the value

chain. This means that the learning contents and

activities start from general and more abstract levels,

not contextualized, are transformed through a chain

of transformation, in order to generated, mostly at

run-time, a dedicated, contextualized support for a

particular seller/learner interaction situation. It must

be noted that the whole system has also intents to be

adapted automatically to the training of the potential

clients, in order to facilitate her/his choice of

particular set of equipments, or for after a purchase

for its operations.

Figure 1 shows that many transformations must

be supported in order to deliver a pertinent learning

or coaching support to a particular user that can be

mobile, across the shop and connected through

wireless networks. Various situations (or contexts)

can be handled by the system depending of the

place, work activities, presence of the potential

clients or not, etc. Several sources on contribution

feed the chain with knowledge and contents. It must

be noted that the other e-Learning services such as

tutoring, assessment, access to dedicated forum,

management of the learning curriculum, are

introduced on the fly, depending of the situations

and needs. Transformations will be done at run-time

following rules and inferences about knowledge

modeling at design time.

There are two main situations for the

seller/learner:

• The seller, or client, is outside the shop

counters: seller in the back office or storage

areas, client at home or another places;

• The seller is in middle of his/her department,

alone or in front of potential client with the

possibility to use resources from the Smart

Space surrounding them.

We can precise here the second situation which

is richer in terms of interactions and importance of

contexts. In this situation, from the learning

perspective, the seller used the mobile information

system for both revising his/her knowledge about

products and selling characteristics or as a coach to

help him/her, in front of the potential client, in the

selling process especially in the selection/decision

phase. For this purpose he/she is equipped with a

new Ultra Mobile Personal Computer (UMPC) such

as the Q1 of Samsung, operating with WindowsXP

OS system and having a WiFi connection to the shop

network, and a Bluetooth to an earphone or

eventually to the mobile client’s phone (in order to

download information). From the earphone the seller

can receive calls from the call-centre of the shop or

enterprise, and computer generated voice message

with the help of the VoiceXML standard. Future

investigations will also support multimodal

interaction both using the digital pen or the touch

screen of the personal assistant and the microphone

included in the Bluetooth earphone. There are two

main variants:

• One where the seller is also equipped with a

portable of a code-bar of RFID tag reader. This

enables the potential for real objects, such as the

product (DVD player for example), to play a

role into the interaction and the learning

process, because, they have their counterpart

into the virtual world;

•

The second where the seller can also

supplement his/her PTA with a larger LCD

screen, located in the department and connected

to the shop network. This is useful for the

sharing of information with the potential client

forming a Single Display Groupware (SDG)

with the PTA used as the coordination

supervisor. This is an example of SmartSpace

where some pervasive technologies augment the

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potential of places with services and dedicated

apparatus shared by the user owners of mobile

devices.

This is a way to follow the vision about

Pervasive Computing as advocated by

(Satyanraynan, 2001).

4 THE DESIGN OF A FLEXIBLE

INFRASTRUCTURE FOR

P-LEARNING

4.1 Dynamic Composition of e-Services

for p-Learning with Multi-channel

Interactions

Following the movement already started in the field

of software engineering, with the Service-Oriented

Architecture (SOA) and in the business field, we can

show that SOA is also a good framework to reframe

the Learning and Content Management Systems

(LCMS). This is confirmed by proposals for the

future learning systems, seen as a collection of e-

Services, such as those of (Wilson et al., 2004) who

proposed a framework of e-Learning services for a

consensus at the international level. If we analyze

this proposal, we can see that the architecture, made

of numerous services, can be decomposed into three

layers from bottom to top: the underlying layer

concerns the data and their persistence across the

services relying on some databases. The

intermediate layer provides application services (for

education it is for example LearningFlow, e-

portfolio, authoring, etc.) and common services (AV

conferencing, role management, DRM…). The

upper layer not precisely described in (Wilson et al.,

2004) is called User Agents and is in charge of

managing the interaction between the user (through

his/her DUs) and a collection of services that

composed his/her applications.

This hypothesis about the importance of these

future open frameworks of learning services is one

of the main assumptions behind the design of our

infrastructure, with a focus on the upper layer. But,

because at this stage the whole framework of e-

Services is still under development, we have

decided, pragmatically, to “wrap” already available

services derived from existing e-Learning platforms

(LCMS existing in open source) providing learning

documents; a dedicated e-portfolio (http://elgg.org);

And from Wiki like one, in order to be compatible

with our specification of the e-Services and with the

Web services technology standards (I.e. the SOAP,

WSDL, UDDI trilogy). This is the left part of the

schema given in figure 2 about an overview of the

software architecture.

4.2 About the Modeling of Different

Contexts and Dynamic Adaptations

The e-Services proposed approach allows us to

concentrate our effort on the management of the

dynamic composition of e-Services directly in

interaction with the learners, that we have called

Interactive e-Services, IeS, in figure 2. We have

already shown (Chevrin et al., 2005a) that this

composition is not independent of the characteristics

of channel used, as usually think. For us, a channel

is the composition of a particular User Devices (i.e.

a personal assistant) with a particular

communication network, which proposes one or

more human communication modalities: textual,

direct manipulation through graphical interface,

voice….

Nevertheless, we have already done an important

research effort for the IeS composition and multi-

channel adaptation (Chevrin et al., 2005b). In order

to support more general mechanisms for the

adaptation of the flow of documents and interaction

elements to the different contexts, we have built a

meta-model of the context-aware process, following

for that purpose the proposals of (Henricksen, 2003).

Figure 2: Global view of our infrastructure.

A FLEXIBLE INFRASTRUCTURE FOR P-LEARNING: A FIRST APPLICATION IN THE FIELD OF

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219

This allows the description of the relationship

natures and the constraint associations, between the

different contexts and their effects on the main

elements used in this ontological model. This leads

to the design of our own adaptation metamodel that

we cannot present in detail here. In this metamodel

there are four main entities around the user/learner,

forming the core of the model, which are: the

Organization (responsible of the intention: training)

the Context, the Activity and the Channel. The

activity is linked to the learn-flow model that can be

expressed with the concepts of IMS-LD, our other

workflow metamodels.

In our collaboration with a Tunisian research

team, we have made a new step by considering the

concept of activity as part on the context. In fact, we

have proposed a new approach, which is the

bijective adaptation between contexts and user

activities (Malek et al., 2006). The next step of our

works is now to plug this “bijective adaptation

system” (symbolize by the bi-directional links in

figure 2) on our intermediation middleware for

supporting more pertinent ubiquitous interactions.

Up to now, it was the activities that were adapted to

the context. From now, from this point of view, the

context may be also adapted to the activities if it is

possible and pertinent.

We retain from the previous works on context-

aware computing that there is a need for better

models of contexts and models of adaptation of

services which are context-sensitive. At this stage

we reuse some results and technological solutions of

previous researches, for the parts of the context that

is not directly related to the learning situation. It is

the user profile, the technological context with an

emphasis on the channel context, and some parts of

the physical contexts, such as the detection of

position in front of a Single Display Groupware for

the seller of our proposed scenario in section 3.2.

Our effort is put mainly on the parts of the context

that is directly related to the learner activities.

Effectively the activity itself is also a context,

especially through the history of the past episodes of

learning and also the work activities. In the proposed

scenario this means that we must adapt the p-

Learning environment to the professional situation,

for example the nature of the present activities of the

sellers, and the presence or not of a potential buyer.

4.3 The Software Architecture for

Intermediation based on a

Multi-Agents System (MAS)

We present now our overall software architecture

based on a Multi-agents System. This is an evolution

of our previous infrastructure developed for research

in multi-channel and multimodal interaction in

mobile-Commerce (Chevrin et al., 2005b). This

architecture is dedicated to the management of the

intermediation between the IeS and the channels

used during an interaction. The central principle of

our solution is that the data, document, flows are

going from an abstract form (elaborated into the IeS)

to a concrete form, in the Application Server that

manages the different channels accesses, through the

chain of transformations, close to those given in

figure 1. The heart of our architecture, as given in

Figure 2, is implemented using the MAS technology.

The MAS role is to coordinate the use of the other

parts of the prototype, i.e. the context, the

organization policies, the intermediation between the

IeS and the channels used (synchronously or not)

during the interaction with the user, etc. The

adaptation of the IeS is done through the

intermediation middleware already presented in

details in (Chevrin et al., 2005b).

The interest, of using a MAS as a substrate for

the design and implementation of our infrastructure

for p-Learning, is of course due to the already well

known flexibility, and potential to support evolving

systems, of MAS as dedicated middleware for

pervasive computing. But less known the multi-

agents paradigm is also useful for some complex and

dynamic mechanisms required in multi-channel and

multimodal interactions with the end users, the

learners. The two main mechanisms are:

• The fission mechanism: where the abstract

document can be split into contextual sub-

elements that are adapted for, and routed to, the

right channel. This form the multimedia output

in a multi-device context (more in section 5);

• The fusion mechanism: where two input

channels (i.e. voice and graphical selection)

used during an interaction with the user, a

session, are combined, in order to form a

request: understandable by the middleware

agents and directed to the right service. This is

the multimodal mode of interaction.

For the MAS we have used a technological

platform that is relatively mature now, JADE

(http://jade.tilab.com/), in operation in several

projects on mobile communications supported by

telecommunication providers, and that offers the

possibility to deploy software agent even inside the

User Device, due to its implementation in JAVA,

and its respects of technological standards for

interoperability.

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5 EXAMPLES OF ADAPTATION

FOR THE PTA SCENARIO

In the proposed scenario, the seller as a learner can

do three types of activities, which are

contextualized:

• Access to the learning contents by navigation

guided by a network of concepts relative to the

selling domain (ontological model and adaptive

hypermedia);

• Access to the learning contents through the

product datasheets or frequently ask questions;

• After a dialogue with the client, selection of a

similar case already solved (for example

problem of a home studio configuration), which

help her/him to give a better advice to this client

(Case-Based Reasoning);

In one of the phase of the scenario, the seller

asks the client to follow him/her to a specific

apparatus placed inside the store department in order

to augment the potential of his/her PTA. In this

pervasive learning situation, the fusion and fission

mechanisms are used to support the collaboration of

the user device, his/her PTA, with the SDG (see

figure 3 for the setting), that requires a fission

operation, or even with the mobile communication

system owns by the potential client (i.e.

downloading of commercial information). The data

and documents provided by the IeS of the left part of

the architecture as described into figure 2, must be

dynamically split into different streams depending of

the contexts, for example the user preference and the

present characteristics of the two or three channels

in action. Same the use of a voice channel in input

jointly with user action on his/her tactile screen of

the UMPC required a fusion operation in input in

order to analyze the request in accordance, also, with

the present contexts of interaction. In this case, we

will have several particularities such as using multi-

devices and “physical objects” of the environment

such as the shared display or RFID tags on products.

The Figure 3 shows an example of SDG

arrangement. This SDG is composed of four distinct

channels: two are visual, one small and private

viewed by the seller, one large and public viewable

by several persons; the voice channel directed to the

seller, and private, thanks to the Bluetooth auricle;

and possibly the data channel of the client mobile

system if connected, for example by Bluetooth link,

to download commercial documents and guidelines.

The heart of this scenario implementation is the

dynamical fission of the different information on the

different channels. Projects like (Han et al., 2000)

deal also with this subject.

The Figure 2 shows the different software agents

implemented to manage the fission mechanisms. The

Session Agent keeps a state of the different channels

used, and in this way, the data fission. For the

moment, only one software agent (Proxy Agent),

manages the routing to the dedicated channels.

Figure 3: Example of SDG arrangement in the PTA

scenario.

6 CONCLUSION

The application of advanced technologies and

models introduces the possibility to dynamically

adapt the contents and the services composition to a

particular learning and learner situation, in

accordance with various contexts, including his/her

past experiences as learner and the present situation

as worker, and the impact of his/her mobility on

location, proximal resources… The new step, called

pervasive learning environment, is to realize the

potential of the pervasive computing vision

(Satyanraynan, 2001), which proposes the extension

of mobile computing with the potential of

Smartspace, where the user interaction and

experience can be enriched with concrete objects,

having their computerized counterparts, and by e-

Services embedded into the surrounding (for

example the office, the shop…) and supported by an

invisible and very proactive, attentive, infrastructure.

However the potential of designing and using

pervasive learning environments is hampered by two

interrelated problems: First to find real needs and

significant situations for this proposition; And

second by the lacks of open infrastructures, and

equivalent of the past LMS, adapted to context-

aware dynamic adaptations of contents and services,

and the multi-channel and multimodal nature of the

user interactions that are inherent to the use of small,

limited, embodied, mobile devices.

We think that professional world with the

demand for continuous training and learning on

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221

demand of workers in real situations (professional

activities and contextual physical settings) is a good

candidate for an application of the potential of p-

Learning. For that purpose we are developing a first

solution, called the Personal Training Assistant,

supporting the counseling and selling of products

that are complex to master and in continuous

evolution, the sector of TV HD in our case. This is a

new learning scenario that has a great interest for the

retail industry and its evolution to e-Retail.

Our contribution, to the second problem solving,

is to propose an infrastructure sufficiently open and

flexible to support a wide range of p-Learning

settings. However the difficult problem is about the

reuse of the previous different contexts models and

the standardization of the information they can

provided. In p-Learning we think that this must be

done pragmatically by reusing Context Provider

Services when they are available, for example as

results of more general researches in pervasive

computing, or even proposal from international

consortium such as Liberty Alliance for the

management of the digital identity of the learner

including his/her profiles as users of e-Services.

As researchers in the field of Technology

Enhanced Learning, we must concentrate our

collective effort on the definition and modeling of

the part of the contexts that are particular to the

learning activities and processes. This elicits the

weakness of currents standards for e-Learning, such

as SCORM for the learning objects or IMS-LD for

the pedagogical scenario and learning activities,

because they offer no possibility to specify and

support dynamic adaptations that are context-aware.

ACKNOWLEDGEMENTS

The present research work has been supported by the

“Ministère de l'Education Nationale, de la Recherche

et de la Technologie», the «Région Nord Pas-de-

Calais» and the FEDER (Fonds Européen de

Développement Régional) during the projects

MIAOU and EUCUE. The authors gratefully

acknowledge the support of these institutions. This

has been also supported partially by the p-LearNet

project funded by the Agence National de la

Recherche.

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