A COOPERATIVE INFORMATION SYSTEM FOR E-LEARNING

A system based on workflows and Agents

Latifa Mahdaoui, Zaia Alimazighi

University of Sciences and Technologies ‘Houari BOUMEDIENE’ - Algeria

Faculty of Electronic and

Computer Science

Computer System Department; LSI

Laboratory – ISI Team



: El Alia BP n°32, Bab Ezzouar, Alger, Algeria.

Keywords: Process; activities; task; former; formation; role; workflows; multi-agents; e-learning

Abstract: In the E-learning platform, we can consider three principal actors (Teacher, Learner and Administrator)

whose inter

act or cooperate between them among processes, then in context of enterprise the e-learning

process can be seen as a cooperative information system where actors are managers and employees. Many

of these processes can be automated and then we consider this work as a workflow process. The learning

process is naturally flexible because of different levels of learners and the different ways to present a lesson

or training process. We use an oriented object Meta-Model based on UML to describe a process concerning

Tutor and Learner and we propose a Multi-Agent System (MAS) based on ITS architecture to support the

work of the actors roles “tutor” and “learner”.

1 INTRODUCTION

The e-Learning made its appearance following the

technological developments of information and

communication meanings such as the Web and the

Internet. Issued from distant teaching, the first

platforms of e-Learning consisted in providing tools

and convivial interfaces to three principal actors of

the system : Teachers, Students and Administrator,

allowing essentially :

For teacher : creation of courses

, incorporation of

teaching multi-media resources (sound, image,

video) and more or less a follow-up of learners.

For learner: consulting courses on line or

ownloading contents, resolving exercises,

transmitting duties for correction.

For administrator: management and control of

teachers

and learners; management of pedagogical

resources and technical maintenance of the system.

Among platforms, we find GANESHA which is

very sim

ple or others more elaborated like Web-CT

and Web-Tutor (improved tutoring quality). But, in

majority of cases, the recommended solutions are

directed more towards management of the teaching

contents than the teaching itself. In other words, the

question is “how to make sure a good quality of

teaching and an effective follow-up of learning in

absence of direct interaction with teacher ?”. In

traditional classroom, teacher can play several roles.

Among these, the tutor role. In fact, our interest is to

perceive the e-learning with a different vision

regarding the teaching contents built by the teachers

or the specialists as necessary resources for training

processes.

So, the training process is seen as a transfer of

owledge according to a given pedagogical

structure with a planning of work. The learner will

have to carry out the planned work in order to ensure

a good knowledge acquisition.

Consequently, the training process appears as a

ccession of activities that the tutor will define and

the learner will perform throughout his studies.

Hence, the idea is to design e-learning process based

on cooperation between tutor and learner.

Each platform’s actor can have different ro

les

according to objectives of teaching. In one side, the

tutor will have also to perform some activities for

the follow-up of his learners group. Each actor will

need a whole resources in order to achieve suitably

the tasks which fall onto him. In the majority of

cases, these resources are of various document types

such as courses, exercises, multi-media contents, …

etc. Then the concept of route appears. Finally, the

execution of tasks will have to be accomplished

according to a certain number of pre-established

rules.

213

Mahdaoui L. and Alimazighi Z. (2005).

A COOPERATIVE INFORMATION SYSTEM FOR E-LEARNING - A system based on workﬂows and Agents.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 213-225

 SciTePress

Roles, rules and routes are the basic characteristics

of workflow processes (Khoshafian & Al, 1998).

Therefore, we propose to design e-learning process

based on workflow (for the processes corresponding

to this category well-sure) (Cesarini & Al., 2004)

(Lin & Al.1, 2002) (VanTroys & Al., 2002) (Lin &

Al.2, 2002). We are particularly interested to

learner’s following-up by the tutor. To do that, we

use a meta-model approach designed using UML

(Alimazighi & Al., 2002) (Saikali, 2000).

The advantage of using meta-model is to allow the

elaboration of working plans for learners with

different profiles. Each instance of model (workflow

case) will concern a learner and his tutor. Learners

can evolve according to their own speed and the

tutor can perform a personalized tutoring for each

one of them.

On the other side, we are not always teaching in the

same manner and all learners have not the same

skill’s level; so, the learning process is naturally

flexible. For this, using workflow meta-model may

allow to do changes at the instance workflow level

and the model workflow level. Thus, a tutor can

operate modifications on an instance of a particular

learner. In this case we talk about “instance

flexibility”. If changes concern all the model

instances, then we talk about “model adaptability”

(Saikali, 2000). The meta-model cover all the

aspects of the workflow process (organizational,

functional, behavioural and informational aspects).

The behavioural aspect of the system is expressed

using extended UML activity diagrams

(http://www.omg.org).

Workflow processes are asynchronous; so, tutors

and learners are relatively free to perform their work

regarding their needs. This leads to the absence of

direct interaction between the tutor and the learner

and constitutes a lack for good teaching quality as

the learner don’t have any way to obtain immediate

assistance. Therefore, we propose a Multi-Agents

System (MAS) based on cognitive agents to support

tutors and learners (Garro & Al., 2003) (Pesty & Al.,

2001). This paper is structured as follows :

Section 2 : in this part, we locate the e-learning

process regarding to the four aspects of the

workflow meta-model : organizational, functional,

behavioural and informational aspects.

Section 3 : we design an example of e-learning

process and discuss about points which necessitate

assistance for a best tutoring quality.

Section 4 : this section describes MAS’s agents

among their roles and their functionalities. A general

overview of possible interactions between agents

themselves and human actors is also described.

Section 5 : summarizes the obtained results and

future works.

2 WORKFLOW META-MODEL

FOR THE E-LEARNING

Within the framework of our research’s team, a

meta-model of workflow process was developed

(Alimazighi & Al., 2002) in accordance with the

standards of the WFMC (http://www.wfmc.org)

which is a consortium of workflow standards. The

meta-model covers four aspects :

Organizational aspect : describes the organizational

structures, the actors of the system and their roles.

Functional aspect : shows system’s functionalities

by splitting of a process into sub-processes,

activities and tasks.

Behavioural aspect : enhances the control flows,

conditions and events attached to the activities.

Informational aspect : presents the part of

information system necessary to the achievement of

work.

Initially, we locate the e-learning process relatively

to these four aspects.

2.1 Organizational aspect

Many organizations of training and education use

today Internet for distant formation. In our point of

view, a platform of e-learning can be seen as an

additive support to university’s campus (Mahdaoui

& Al.1, 2004). This could contributes to reduce the

loads endured in term of capital costs. In fact, the

installation of an e-learning platform (equipments,

networks, software tools,…etc), could not be higher

than costs of real infrastructures (construction of

buildings, classrooms, human resources, …etc). We

consider then e-learning platforms like a virtual

institution of teaching whose actors and roles are :

Table 1: Actors and roles in e-learning platform

Actors Roles

Teacher Tutor

Author of contents

Examiner

Student Learner

Administrator System Manager

Manager of Teachers and

Students

Manager of pedagogical contents

Let’s note that Administrator roles and examiners

are not concerned by this study. The Administrator

is assumed to be the system. For considered

organisational structures, we propose to dispatch

learners which adhere in e- learning formation into

groups. Each group will have a tutor by module or

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topic taught within the framework of the followed

formation. Parameters such as the number of learner

per group or a number of groups per tutor are fixed

by the administrator (the tutor is concerted). For

example, one tutor can propose an evaluation test to

detect learner’s profile in order to form groups.

The roles described for the teacher can be ensured

by the same or different persons. We consider that

the role concept is independent from the person who

plays it. The following figure shows the organisation

of actors and roles (fig. 1) :

Figure 1: Organization of actors in e-learning process

The following diagram presents the part of the

meta-model covering the organisational aspect (fig.

2):

Figure 2: Organizational Meta-Model

2.2 Functional aspect

The functional aspect describes processes in term of

sub-processes, activities and tasks independently of

rules, events and constraints to which they are

subjected. Thus, by regarding to the planning of

work established by tutor as a process, we notice

that it can be broken up into activities and each one

decomposed into tasks to be achieved by the learner.

We consider the task as the smallest entity of work,

i-e, not decomposable. The functional part of the

meta-model is presented in the figure below (fig. 3):

Figure 3: Functional Meta-Model

2.3 Behavioral aspect

This aspect focuses on intrinsic flow of control to a

process and allows to show the state of activities and

tasks. We can also describe conditions and events

stream controlling the process execution. We

suppose that the necessary pedagogical resources

(tools and support documents) are disposable for the

work of learner at each moment.

The activity diagrams of extended-UML

(http://www.omg.org) allow to describe these

properties. The description of a process always

begins with the word “START” and finishes with

the word “END”. We use swimlane’s activity

diagrams where each swimlane represents a role

(tutor or learner) and contains the set of activities or

tasks to perform. A complete example will be shown

in section3. Figure 4 illustrates the behavioural

aspect :

Figure 4: Behavioral Meta-Model.

Transitions express the activities and tasks. An

activity or a task is characterized by one or several

possible states. An activity is described by a

succession of executable tasks. If all activity tasks

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are correctly performed then the activity is correctly

accomplished. The links between transitions

describes relations of precedence, parallelism,

choice, … etc. A transition may be triggered by one

or several events and submitted to simple or

complex conditions. This can be described by

following operators : OR-SP, OR-JO, AND-SP,

AND-JO, and XOR-SP as shown below (fig. 5, 6

and 7).

Figure 8: Iterative Structure

2.4 Informational aspect

Finally, the informational aspect presents the

information system part in order to extract necessary

data for operation of learning and then the execution

of e-learning workflow processes. Information can

be portions of databases or any other documents

(text, multi-media, formulary, …etc). Meta-model

part covering this aspect is represented in fig. 9 :

Figure 5: OR Operator

Figure 9: Informational Meta-Model

To extract pertinent information for this part, we

carry out a conceptual study based on UML and

RUP (Rational Unified Process) (Jacobson & Al.,

2000) (http://www.omg.org). We obtain a class

diagram covering all the information of e-learning

platform. From this diagram, we present only

information concerning this work, like showed in

figure 10 by the dotted framework:

Figure 6: AND Operator

Figure 10: Classes Diagram for the Informational aspect

Figure 7: XOR Operator

2.5 Integration of the four aspects

Let‘s note however that the XOR-JO operator is

similar to the OR-JO. Activity can executed

iteratively as showed in (fig. 8):

The diagram of figure 11 shows the integration of

the four aspects previously introduced. For

clearness, the information system part is presented

without details.

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Figure 11: Workflow meta-model for e-learning

3 EXAMPLE OF E-LEARNING

PROCESS

3.1 Presentation of the example

In this section, we will present an example of e-

learning process model by designing the

correspondent activity diagram according to the

behavioral meta-model. We consider that a learner’s

work plan is prepared by the tutor. To do that, he

retrieves structured contents (courses, exercises, …

etc) established by the author. Tutor can also insert

his own exercises. Furthermore, he can consult

domain’s specialists and pedagogues to improve

learner’s work plans.

Let’s consider a module of “Information System”

(a process) where a part of program is described in

(fig. 12):

Figure 12: Decomposition of a process into sub-processes activities

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The granularity choice in terms of processes, sub-

processes, activities and tasks is delegated to the

tutor. In this example, “Information System”

represents the whole process subdivided into courses

represented by sub-processes which are split into

multiple activities corresponding to lessons. Each

activity contains a succession of tasks to be

performed by a learner. Let’s note that nothing

prevents to see a process differently. For scheme’s

legibility, we use zoom on sub-processes and

activities as illustrated in figure 13:

Figure 13: Zooming of a sub-process and activities

In correspondence with learner’s work plan, the

tutor will define a set of tutoring activities. This

depend on his judgment of critical points (for

example, need of learner’s feed-back) in the work

plan. Figure 14 shows an example of tutoring plan

for sub-process1 (see fig. 13) :

Figure 14: Example of tutoring process

3.2 Activity diagram describing the

process

An activity diagram specifies the global process

behavior and the work part of both actors learner and

tutor. Events and control flows are detailed and

learner-tutor interaction is enhanced. Tasks due to

interactions are added like “Send a course” for tutor

and “Connect to learning session” for learner. See

figure 15 for the enrolling of sub-process1 (of fig.

13):

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Figure 15: Activity diagram for a sub-process1 learning session

For clearness, we have reduced our example to

a normal scenario, but in fact, the procedure can be

more complex. We have said in introduction that e-

learning is flexible, so when the tutor conceives a

work plan, he can prevents possible assistance

points besides pertinent points (necessary for

tutoring). Here, process modeling becomes more

complex and if he (the tutor) don’t want to do it, he

can ignore exceptional cases and treat them during

workflow case (flexibility). If the tutor detects that

the same exception arises at the same point in all

workflow cases then he can decide to modify the

process model (adaptability). That is possible by

using of meta-model (Saikali, 2000)

(http://www.wfmc.org). Figure 16 presents an

example of learner’s assistance for a task.

Figure 16: Learner’s assistance for the task “Study the

examples and case of study

: Tas

: Activity : Even

[…] : Condition

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We differentiate between learner’s connection to

platform and the execution of e-learning instance

process. Therefore, when a learner is connected to

the system, he must identify himself as adherent to

the platform (administrative side). Once the

connection is established, he selects the module he

wants to study and then accedes to activity list (sub-

process). Interactions between tutor and learners are

asynchronous, hence it’s possible that a learner

stands until his tutor sends the results of tutoring

before to continue his work (for instance, he wait

for an answer to his sent question). Finally, a

workflow case concerning one matter (module) can

be performed in several sessions of connection, as

it’s possible that different sub-processes instances

for the same module are executed in one connection,

depending on learner’s rhythm, skills and the

process model.

From there, the using of workflow system is

benefic in sense that it makes possible to plan both

works of tutor and learners. Asynchronous

communication allows each one to evolve

accordingly to his disposal. But the learning quality

may be altered because some types of problems that

the learner can meet need immediate response (this

is not possible when the tutor is not connected).

In the other side, a tutor have one or several

groups of learners. They can solicit him at the same

time for different problems from various degrees of

importance, so tutoring can become difficult.

Thus, to improve the quality of learning and

tutoring, both roles need assistance. We propose to

introduce artificial actors in the system. These actors

are organized as a Multi-Agents System (MAS)

(Garro & Al., 2003) (Ouahrani & Al., 2003) (Pesty

& Al., 2001).

4 A MULTI-AGENTS SYSTEM

(MAS) FOR THE E-LEARNING

ASSISTANCE

4.1 Needs of assistance for tutors and

learners

As previously described, tutors and learners need

assistance during the accomplishment of their

respective tasks. These can be summarized in

• For learner, the primordial thing is to provide

an assistance or an artificial tutoring (as much

as possible) during his work’s session.

• For tutor, the essential is to present and

organize all critical information concerning

his groups in such way he can takes a good

and efficient tutoring decisions.

To realize that, we propose the intervention of

artificial actors in the system. These actors are

cognitive agents having abilities of reasoning and

taking decisions (Garro & Al., 2003) (Ouahrani &

Al., 2003). They can communicate between them

and with human actors by messages. Agents can also

execute complex tasks alone or in

cooperation(Ouahrani & Al., 2003). Each agent play

a specific role in the system. The MAS is organized

accordingly to Intelligent Tutoring Systems (ITS)

(Mahdaoui, 2002). Advantages of using MAS in this

context are :

• To make tutoring so easy by delegating all

automatisable tasks to agents. This will

allows tutor to focus on real problems which

can not be resolved without him.

• To offer a certain necessary interactivity for

learner to evolve, and to allow more rapid

progress in training by submission to

elaborated learning strategies (Aimeur & Al.,

1999) (Mahdaoui, 2002).

• The MAS can collect lot of knowledge and

this may considerably contributes to the

improvement of tutoring and learning

strategies. Tutoring strategy is the “tutoring

way” of tutor and the learning strategy is the

manner of learner’s learning.

Finally, let’s note that when adding a MAS, our

goal is not the elimination of human tutoring but its

improvement with best level of assistance.

Organizational meta-model of workflow is extended

to agents like showed in figure 17:

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Figure 17: Organisational Meta-Model extended to agents

4.2 MAS’s Agents description

Agents are defined by the role they can play in the

system. The MAS is constituted by seven cognitive

agents : “Learner’s Assistant Agent” (LAA),

“Personal Tutor Agent” (PTA) and “Pedagogical

Agent” (PA) are dedicated to learner. “Tutor’s

Assistant Agent” (TAA) and “Group’s Assistant

Agent” (GTA) concern tutor. “Super Tutor Agent”

(STA) and “Session Manager Agent” (SMA) are for

both tutor and learner. We describe below, each

agent and its functionalities.

4.2.1 Learner’s Assistant Agent (LAA)

The role of LAA consists to assist each learning

session. It’s a learner’s representative towards the

other agents concerning him. One LAA agent is

associated to one learner.

Functionalities :

9 LAA is created when a learner is connected

for the first time to the e-learning platform.

At each learner’s connection, it’s activated by

the system and inactivated at each

disconnection.

9 LAA helps the learner in the choice of the

module to study. After, it informs PTA agent

that the learner is on-line.

9 When learner wants to obtain help from

others, LAA make in his disposal a special

buffer where he can write his request.

Moreover, the learner can precise the receiver

of message which can be PTA agent, the tutor

or other learners in his group. If the receiver

is human then LAA demand to SMA if he

(human) is connected.

9 If SMA’s reply is well acknowledged then

LAA negotiates with the corresponding agent

of the receiver (other LAA or TAA for the

tutor) to know if he wants to converse. After

answer reception, LAA inform the learner.

9 If the learner have not precise the receiver

(for example, he search for any connected

learner in his group), then LAA demands a

list of connected learners from SMA and

after, LAA negotiates to find favorable

persons to dialogue. It transmits results to the

learner.

9 Furthermore, LAA can accede to learner’s e-

mail once it is activated and classify arrived

and non treated messages. Tutor’s messages

are always placed at the top of the list.

Generally, LAA can use the message list to

council the learner for choice module.

4.2.2 Personal Tutor Agent (PTA)

PTA is an artificial personal tutor for the learner and

assists him for each task he performs. For each

learner, one PTA is associated. PTA knows the

nature and pedagogical objectives of activities and

tasks. We distinguish between two categories of

tasks : passive task like reading text or visualizing

multi-media and active task like doing exercises,

duties or practical works.

For passive tasks, PTA will inspect parameters

like estimated duration for example. This kind of

task remains very hard to inspect, so, PTA will just

try to attract learner’s notice that it must be

performed.

Generally, an active task always terminates with

learner’s feed-back. Activities like exercises, for

example, are particularly important for a good

teaching and presents pedagogical goals as :

• Test of acquired knowledge

• Increase learner’s capabilities to analyze and

proof

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• Evaluate learner’s skills and lacks

Functionalities:

9 PTA receives a sub-process (work plan)

from GTA and loads learner’s work-list

with the activity to execute. Each task of an

activity corresponds to a work-item and

PTA will assist its achievement.

9 If the task is passive, the learner can decide

to jump it (for example, because he knows

the content) and so PTA will note this fact.

After, PTA can use it to direct his tutoring

decisions.

9 If the task is active, PTA will stand for

learner’s feed-back. If necessary, PTA can

activate his subordinated agent PA for

applying an appropriate pedagogical

strategy to the learner. When terminated,

PTA will receive a report from PA.

9 PTA knowledge allows it to judge if a task

is correctly performed or no (if learner’s

results are satisfactory or not). Then PTA

can take one of the following decisions :

• If results are good then PTA will pass to the

next step of work. It can be the next task or

activity to do. If the concerned sub-process

is correctly finished then PTA will request

from GTA the next work to be done.

Depending on GTA’s answer, PTA may

continue tutoring or arrest the work (answer

depends on what the tutor have arranged).

In arrest case, PTA sends a detailed report

to GTA about the learner.

• If results are not good, PTA will try to

resolve the problem according to the

following steps :

¾ It proposes for learner to re-execute the task

in the step which causes the problem.

¾ If problem persists (not conclusive results),

PTA will use his own experience and

propose other works (complementary tasks)

to reinforce learner’s knowledge.

¾ If PTA have not required competences or if

results remain not good, GTA will be

solicited for help.

¾ GTA’s reply can be : a new tutoring

strategy or information that the problem

will be transmitted to the tutor. In this case,

PTA stops tutoring until reception of new

directives.

Moreover to that, PTA can cooperate to resolve

GTA’s requests for other PTA’s count. If PTA have

no response to send, it ignore the request.

4.2.3 Pedagogical Agent (PA)

PA’s role is to submit a learner to different learning

strategies in order to ensure that he had (the learner)

understand and for filling his gaps. PA is

subordinated to PTA which solicits it when

necessary. According to pedagogical objectives, PA

can become “companion”, “troublemaker” or

“learner’s pupil” (Aimeur & Al., 1999) (Mahdaoui,

2002).

Functionalities:

9 PA can apply one of these strategies :

• Learning with companion : PA

becomes a co-learner (a friend) with

slightly high level of knowledge than

learner. PA guides the learner during

his work by counsels and suggestions

(according to what it knows). The

learner is free to choice and to decide.

• Learning by disturbing : PA becomes a

troublemaker. With this strategy, the

level of exercises is more elaborated

and aims to provoke conflict situations

(cognitive dissonance) between PA and

the knowledge and the convictions of

the learner (Aimeur & Al., 1999). So,

PA can detect learner’s gaps and his

performances in analyze and proof.

• Learning by teaching : it’s the ultimate

step where PA becomes a learner’s

pupil, i-e, learner have a very high

level of knowledge and capacities.

Therefore, he must teach to PA.

9 Once terminated, PA transmits a report

containing evaluations, marks and

observations to PTA.

4.2.4 Tutor’s Assistant Agent (TAA)

Similarly to LAA, TAA represents the tutor over

against other concerned agents and assist him at

each tutoring session. For one tutor corresponds one

TAA.

Functionalities:

9 TAA is created when a tutor is connected

for the first time to e-learning platform.

TAA is activated/inactivated according to

tutor’s connection/disconnection.

9 Tutor can have several associated groups

for different modules. Then TAA assists

him for choice. After, TAA informs

concerned GTA that the tutor is on-line.

9 When tutor wishes to communicate with

agents or his learners, he describes his

request in a special area provided by TAA

with possibilities to choose receivers.

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9 When tutor want to send his reply

concerning a problem received from GTA,

he also use TAA.

9 TAA treats the request like described for

LAA.

9 TAA can accede to tutor’s e-mail once it is

activated and classify arrived and non

treated messages. Learner’s messages are

always placed at the top of the list and

generally, TAA can uses the message list to

council tutor for the group and the matter

choice.

4.2.5 Group Tutor Agent

GTA is associated for one group of PTA

representing a group of learners and one tutor (for

one matter). GTA is an artificial tutor for all group

members in sense that if any problem PTA(s) cannot

be resolved, GTA will try to do it. GTA cooperates

with PTA and STA and informs regularly the tutor

about group’s evolution.

Functionalities :

9 When GTA receives request from PTA, he

attempts to answer according to its own

experience (knowledge previously collected

from others PTA, STA or tutor).

9 If GTA don’t find the solution, it requests

SMA to obtain a list of active PTA(s)

(inactivated PTA(s) have been previously

sent all the information to GTA).

9 Based on the received list, GTA diffuses a

request for tutoring assistance. If no result

is obtained then GTA asks STA for help.

9 Once answers received, GTA analyses and

organizes results in a report and sends it to

concerned PTA. If no solution, GTA

informs PTA that a problem will be

submitted to the tutor.

9 GTA can receive request for assistance

from STA, thus if it cannot treats the

problem, GTA ignore the request.

4.2.6 Super Tutor Agent

STA capitalizes all the experiences concerning

GTA(s) that tutors the same module (for the same

profile of learners) and experiences concerning the

same group of learners for different modules. At

present, we consider that only one STA exists for all

the groups.

Functionalities :

9 When STA receives a help demand from

GTA, it try to treat it according to its own

knowledge (results from other GTA(s)

experiences).

9 If no results, STA diffuses a request for

assistance towards actives GTA(s) (list of

actives GTA(s) is obtained from SMA).

The rest of the procedure is similar to

GTA/PTA scenario.

9 When help’s demand is received from the

tutor, depending on what is requested, STA

treats question under one of the two aspects

described in the beginning of this

paragraph.

4.2.7 Session Manager Agent

SMA has the responsibility to save all the

information concerning connection/disconnection of

learners and tutors (and activated/inactivated

agents). Actually, we consider one SMA for all the

system. SMA is automatically launched by the

system when start-up.

Functionalities :

9 Receives and treats demands issued from

all other agents of the MAS and tutors.

4.3 General Overview of the MAS

behaviour

As previously described, figure 18 summarize the

global behaviour of the MAS :

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Communication Communication / Cooperation

Communication between agents of the same type Requests for services

Figure 18: Agents Interactions

The link between workflow system and MAS’s

agents is activities and tasks received in the learner’s

work-list and work-items. Agents may communicate

and cooperate between them. Communication with

human actors is also possible. To realize this system,

we are working to specify the MAS with AUML

diagrams suitably to the FIPA standards

(http://www.fipa.org). AUML allows specification

of agents (competences, knowledge, intentions,

plans …etc). Moreover, we aim to enhance the

conversation between agents and to define the

appropriate cooperation/ communication protocols

and to avoid conflict problems.

5 CONCLUSION AND

PERSPECTIVES

In this paper, we are interested to design an e-

learning process as a workflow process. Some

related works have considered workflow aspect in

the e-learning in different ways. (Cesarini & Al.,

2004) (Lin & Al.1, 2002) propose models for

teaching staff who interact between them.

(VanTroys & Al., 2002) (Lin & Al.2, 2002) propose

a workflow engine for e-learning based on

Workflow Management Facility (WMF)

(http://www.wfmc.org). (Pesty & Al., 2001) (Garro

& Al., 2003) talk about using MAS for teacher’s and

student’s partner system.

Our approach, proposes to combine the

using of workflow process with a MAS to improve

the quality of assistance for tutors and learners. We

propose to design both works of learner and tutor,

and the interaction between them by workflow

processes. We have established an UML meta-model

of Workflow process for e-learning. For the

behavioural aspect, extended UML activity diagrams

are used. These constitute a powerful formalism

allowing the expression of control flow using

specific operators.

We define a MAS containing seven cognitive and

cooperative agents. Like Intelligent Tutoring

Systems (ITS), some agents have the ability to

replace human tutor in determined tasks and help the

learner during his work. Knowledge capitalized in

the MAS can be used to improve the quality of

teaching in a platform.

As first result of implementation, we have

realized an author’s tool to prepare pedagogical

contents and exercises making it in disposal of tutor

for preparing work plans of learners. A tool for

definition of pedagogical workflow process is under

development.

As perspectives, we are actually working on

specification of the behavioural aspect of workflow

with Workflow-Nets (issued from PETRI-NETS)

(Mahdaoui & Al.3, 2004). In fact, e-learning process

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might be very complex and then the need of

properties verification is important before any

deployment on e-learning platform. Parallely to this

we have proposed in (Mahdaoui & Al.2, 2004) an

assistance system based on holonic agents an we

give a more detailed description of the MAS agents

using J. Ferber’s Grill and petri-nets in (Mahdaoui &

Al.4, 2005). Our future gool is to compare the two

approaches respectively to certain properties of

efficiency we will define.

In addition to what we said in section 4, we think

that the role of some agents can be extended in order

to cooperate with other actors like the administrator.

Finally, we want to consider other human roles like

examiner which represents a great importance in e-

learning platform as virtual structure of teaching.

This role can have important incidence in tutoring.

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