Creative Education
2013. Vol.4, No.7A2, 181-190
Published Online July 2013 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2013.47A2024
A Multi-Agent Intelligent Learning System: An Application
with a Pedagogical Agent and Learning Objects
Sánchez-Guerrero Lourdes1, Laureano-Cruces Ana Lilia1,2,3, Mora-Torres Martha3,
Ramírez-Rodríguez Javier1,2, Silva-López Rafaela Blanca1
1Departamento de Sistemas, Universidad Autónoma Metropolitana Azcapotzalco,
Distrito Federal, México
2Laboratoire I nformatique d’Avignon, Université d’Avignon et des Pays de Vaucluse,
Avignon, France
3Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México,
Distrito Federal, México
[e-mail], [e-mail]
Received May 29th, 2013; revised June 29th, 2013; accepted July 6th, 2013
Copyright © 2013 Sánchez-Guerrero Lourdes et al. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the origina l w o rk is properly cited.
This article describes the analysis, design and development of an Intelligent Learning System (ILS). The
design of the ILS is based on a multi-agent architecture. This architecture includes reactive agents which
represent the expertise of each of the necessary sub-skills in learning the application domain, which in the
study case is structured programming. The ILS utilizes artificial intelligence techniques to implement the
teaching-learning process using an inference engine based on a general didactic model. As a result, this
system is termed as Intelligent Learning System with Learning Objects (ProgEst). ProgEst is carried out
with the objective of providing the user with self-regulated learning strategies in addition to the knowl-
edge of a determined domain. The case study includes situations related to: learning styles, knowledge
domain (errors made) and affective-motivational state. The assessments shall determine: 1) what is to be
explained, 2) level of detail and timing, 3) how and when to interrupt the student, and 4) the information
to provide during the interaction.
Keywords: Intelligent Learning System; Educational Objective; Learning Object
Introduction
The information and communication technologies of today
have evolved rapidly, affecting their application in distinct dis-
ciplines. One of the sciences in which they are more frequently
being applied is education. At present, a wide variety of elec-
tronic media are utilized to send or receive support materials,
for the purposes of Distance Education (DE), which has given
rise to the e-learning modality. This term refers to the use of
new information and communication technologies with a learn-
ing objective which involves: 1) the way of organizing educa-
tional content; 2) the mode of accessing them; and 3) their use
in the teaching and learning process. Intelligent learning system
(ILS) with learning objects (LO) (ProgEst) was designed to
support students in understanding and learning the skill of pro-
gramming.
The (LO) model offers a way to build educational content by
composition from parts of elements which are located in the
lower levels of learning.
According to Chan-Núñez, Galeana-de la, Ramírez-Montoya
(2006), one of the reasons for which the notion of the LO has
gained such strength in the field of information and communi-
cations technology (ICT)-based education is the fact that it can
be considered as a bridge concept between: education, commu-
nication, design and computer sciences, among others.
Learning environments are changing rapidly, implying new
scenarios that pose technical and pedagogical challenges which
higher education institutions must consider in their educational
model, and professors, students and support staff must quickly
adapt to the use of these new environments. The primary objec-
tive of this work lies in combining the new technologies with
artificial intelligence (AI) and LO. The following elements
were analyzed for the purpose of accomplishing this objective:
1) the pedagogical model used in the didactic materials which
are developed, 2) the publication for which the material is cre-
ated, and 3) the use of LO.
The ILS bases its teaching and learning process in an infer-
ence engine inspired by the human tutorial process and is com-
prised of the following elements: 1) interest, 2) desire, 3) help,
4) cognitive and operational strategies, 5) interruption, 6) quit-
ting, 7) learning, 8) idle time, 9) error 10) student’s perceived
tendencies (Laureano-Cruces, Mora-Torres, Ramirez-Rodriguez,
de Arriaga-Gómez, & Escarela-Perez, 2010a). These elements
are connected in a causal matrix which allows us to see the
interconnection of each element with the others. In addition to
these elements, learning style is included, which will have a
direct impact on the interface and the internal motivation of the
object of study. All of these allow selection of the operational

S.-G. LOURDES ET AL.
strategies at the appropriate time. The inference engine is rep-
resented by extensive cognitive maps, one of the AI techniques
used for representation of knowledge with uncertainty and de-
sign of the inference engine. The latter is not included in the
development of this project (For more information, see Lau-
reano-Cruces, Sánchez-Guerrero, Ramirez-Rodriguez, & Mora-
Torres, 2008a; Mora-Torres, Laureano-Cruces, & Velasco-San-
tos, 2011).
On the other hand, the curriculum is designed using a genetic
graph, which is based on a multi-agent architecture, implying
three expert agents, each one in a sub-domain (Laureano-Cru-
ces & de Arriaga-Gómez, 1998; Laureano-Cruces & de Arri-
aga-Gómez, 2000; Sanchez-Guerrero, Laureano-Cruces, Mora-
Torres, & Ramírez-Rodríguez, 2010). The sub-domains are re-
presented by instructional objectives. The aforementioned per-
mits the elaboration of a teaching- learning process based on
scenarios linked to each one of these sub-domains, and thus
allows for detailed error management. For further details, see
Reilly & Lewis (1991).
The structured programming content was organized based on
the LO model, as these offer a way of building educational
content by composition based on parts of elements which are
located in the lower levels. Likewise, it is a way to search for
objects and content, locate, recover and integrate them through
a collection of specifications and standards for web based
e-learning, or SCORM (Shareable Content Object Reference
Model), for their cataloguing, requisition, export, transport and
import. Finally, it offers the possibility to build a personalized
selection of educational content for each student and moment
which offers the optimal context for his or her learning. For
greater detail see references (Muñoz-Arteaga, Osorio-Urrutia,
Álvarez Rodríguez, & Cardona-Salas, 2008). Chapter 2 ex-
plains how the ProgEst system is integrated, chapter 3 explains
the example of the use of ProgEst and, finally, we find the con-
clusions.
ProgEst System
ILS’s pose the learning process as cooperation between an
intelligent system and humans (Laureano-Cruces, 2000a; Lau-
reano-Cruces & de Arriaga-Gómez, 2000b). The tutor, based on
the evaluation of the user’s performance, is found within a con-
stant decision-making process for the purpose of selecting the
most appropriate teaching strategy. These strategies are elabo-
rated based on the perception of the user’s performance taking a
series of evidence as parameters, such as: errors made, learning
style, knowledge domain and affective-motivational state,
among others (Pintrich, Smith, García, & Mckeachie, 1991).
These assessments determine: what is to be explained, the level
of detail and timing, when to interrupt the student and which
information to provide during the interaction.
An ILS has been designed and implemented together with
the learning objects, called ProgEst. An inference engine was
utilized to achieve this, based on a general didactic tutor (Lau-
reano-Cruces, Teran-Gilmore, & Rodriguez-Aguilar, 2005; Lau-
reano-Cruces, Mora-Torres, Ramírez-Rodríguez, & de Arriaga-
Gómez, 2011). This is linked to the case study tutor module and
to the perceived user performance (student model). The user
performance is represented by the student model. The relation-
ship between the tutor module and the student model permits
creation of the distinct didactic strategies.
In the ProgEst system, the cognitive didactic is designed in
accordance with the instructional objectives (IO), which re-
present the cognitive sub-skills and abilities the professor (in
this case, the tutor module) wants to convey to the student
(Laureano-Cruces et al., 2000b; Laureano-Cruces, Teran-Gil-
more, De Arriaga Gomez, & El Alami, 2003; Laureano-Cruces,
et al., 2005). These abilities are activated together with opera-
tional didactics (Figure 1).
Design of the Agents
The mental model generally implies use of the distinct con-
trol structures. This point is very important and is part of the
detailed logic stage of each module (descending modular pro-
gramming and structured programming). Taking this into ac-
count and based on the instructional objectives (defined in Sec-
tion 3.1), three agents are included: 1) types of data, variables
and constants, 2) control structures: (sequence, iteration: condi-
tional (during and repeated) and non-conditional (arithmetic
progression), selection: simple (if, then, except) and multiple
(case dependent)) and 3) abstractions: (procedures, functions)
Figure 1.
Intelligent learning system (ILS) with learning objects (ProgEst).
182

S.-G. LOURDES ET AL.
(Figure 2).
The teaching-learning process is based on the previous Ge-
netical Graph (Figure 2), the nodes of which represent the in-
structional objectives. These objectives are immersed in a mul-
tiagent structure (Laureano-Cruces et al., 2000b).
Multiagent Model
The MultiAgent structure and its anatomy are made up of
three parts: a presentation (P), an abstraction (A) and a dialogue
control (DC). The presentation is the part of the agent which is
viewed by the world and is related to a presentation technique.
Didactical Design
The abstraction represents the local status of the agent. It is the
part which contains the conceptual objectives for accessing the
domain and is where the competence of the agent is imple-
mented. The coordination between the abstraction and the
presentation and coordination with other agents is carried out, if
necessary, in the dialogue control. It is worth mentioning that
agents may exist which contain solely the presentation part or
the abstraction part or neither.
The agents performance is divided into two subagents, the
first being the diagnostic, of which the objective is to address
the environment (the student’s progress). It acquires evidence
Figure 2.
Domain of each agent.
of the agent’s perceptual abilities using the obtained informa-
tion in order to know if the student employs, does not employ
or incorrectly employs the skill which is monitored and con-
trolled exclusively by said agent. Based on the findings it de-
tects the committed error(s), thus activating the second sub-
agent, represented by a MicroWorld with the mission of creat-
ing an environment which assists the student in clarifying his or
her doubts. The aforementioned is carried out by means of di-
dactical methods which guide the intervention throughout com-
pletion of the process, reestablishing the student in the principal
environment where the error occurred.
In ProgEst, the intervention of the MicroWorld consists of
presenting an explanation of the subject followed by complete
examples which show the correct use of the skill. The ProgEst
system architecture is a multi-agent architecture and its anat-
omy is composed of three independent agents: 1) types of data,
variables and constants, 2) control structures and 3) abstractions.
The task which they develop is hierarchical, resolving the
problem of intervention during development of the session
(Laureano-Cruces et al., 1998; Laureano-Cruces et al., 2000b;
Sanchez-Guerrero, Laureano-Cruces, Mora-Torres, & Ramirez-
Rodriguez, 2011). The agents review the task in determined
critical points (identified by the expert) in an organized manner
due to the aforementioned hierarchy. If any of them identifies a
failure, its internal mechanism is activated. For greater detail,
consult (Sanchez-Guerrero, Laureano-Cruces, Mora-Torres, &
Ramirez-Rodriguez, 2009) (Figures 2 and 3). ProgEst has the
capacity to ask for help within the scenario, permitting the con-
tent of the respective topic to be shown prior to asking the Mi-
croWorld. This is due to the trainer approach of our ILS. Errors
are classified as: mild, serious and fatal. This classification is
developed based on the authors’ experience. According to the
multi-agent architecture with dynamic intervention proposed by
L aureano -Cruces (Laurea no-Cruces et al., 1998; Laureano-Cruces,
2000a; Laureano-Cruces et al., 2000b), specialists are created
which act as reactive agents that are activated in the moment an
error which corresponds to their expertise is made (Table 2).
For greater detail, see Reilly et al. (1991).
Interface
We designed a pedagogical agent that evokes, through facial
expressions, emotions necessary in implementing cognitive-
affective strategies in the environment of the teaching-learning
process. In Figures 4-6, some of them are shown. Some other
expressions are also shown related with interest and sympathy,
useful for interaction between pedagogical agent (ILS) and the
user.
ProgEst Application Example
In this section, we will present an example of the application
of the ProgEst system, utilizing the evaluation of the Data
Types scenario as a case study, for the purpose of explaining the
execution of the ProgEst system step by step.
It is important to comment that the system is implemented in
the Moodle (Learning Management System Moodle, obtained
of: http://moodle.org) learning management system (LMS)
platform, and integrated in the ProgEst system architecture, see
(Figure 1), and thus the scenarios and management of the
learning objects are presented. Similarly, the ILS in the inter-
face design is connected with the learning styles by means

S.-G. LOURDES ET AL.

CD
DataTypes

CompailerExpert
ErrorTableofDataTypes(Table2) 
Perceptual
&
Actuactors
PA
CD
Strategies 
Microworld

Interfa ce
Screen
PA

DidacticalTactics
(Table3&4)
Figure 3.
Representation of the data types agent.
Figure 4.
Screen depecting the scenario presentation.
color combinations for each learning type, taking the type of
intelligence which is predominant in the user into account. For
more information see Velasco-Santos, Laureano-Cruces, Mora-
Torres, & Sánchez-Guerrero, 2009; Honey & Mumford (1986).
In this example, the student is registered (Figure 7) in the sys-
tem for the first time. Upon conclusion of the registration of his
or her personal information, the system assigns a user name and
password. Next, the learning styles questionnaire (Alonso,
Gallego, & Honey, 1994) is activated, which is comprised of 80
questions and requires approximately 10 minutes for the stu-
dent to complete. The system then evaluates the responses and
determines the student’s learning style (Figure 8). In this case
study the student’s learning style is theoretical.
The ILS contains a module for evaluation of the internal mo-
tivation of the object of study (Mora-Torres et al., 2011), per-
formed through a questionnaire containing eight questions,
Figure 5.
Screen of the learning object.
which is activated after the system shows the student his or her
learning style. Completion of the questionnaire takes approxi-
mately five minutes, upon which the system evaluates the re-
sponses and determines the student’s type of motivation to
study. It is worth mentioning that the ILS included Honey and
Mumford’s definition of internal motivation of the object of
study (Honey et al., 1986), who describe the learning styles
which they define as: active—they participate in new experi-
ences without prejudices, they have open minds; reflexive—
they consider experiences, observe perspective, gather and ana-
lyze data before drawing conclusions; theoretical—they adapt
and integrate observations to logical and complex theories, they
tend to be perfectionists; pragmatic—they apply ideas in a
practical manner. This case of example of application will only
be relevant when the student has external motivation (Figure
9).
184

S.-G. LOURDES ET AL.
Figure 6.
Screen of the expressions at the pedagogical agent.
Figure 7.
Screen with the register of the system ProgEst.
Figure 8.
Screen with the CHAEA questionna ire results.
The data which were generated upon completion of the ques-
tionnaires and the record which was assigned are saved in the
database for future reference by the ILS. For this case of appli-
cation, the Data Types exercise was activated in the (LMS)
(Learning Management System Moodle, obtained of: http://
moodle.org). As we can see (Figure 10) on the main screen of
the data types exercise, the objective is presented to the user
and the instructions of the exercise or task to be developed
(scenario) are explained. Next, the data types exercise is acti-
vated as shown (Figure 11) for the user to complete it.
Upon resolving the exercise, various cases may be presented.
Only certain cases of application are described in this section:
 If the student requests help, the system displays the learning
object related to the Data Types through the user interface,
in accordance with the didactic strategies, the type of moti-
vation to study and the learning style. In this case, the con-
trol structure content which is presented is for a student
with a theoretical learning style external motivation (Figure
12), so that it will review the data types concepts and then
consult. The ILS allows the student to return to the scenario
and continue with the exercise, as mentioned in the previ-
ous section. The system has the capacity to ask for help
within the scenario (exercise), allowing the content of the
respective topic to be displayed before asking the Mi- cro-
World. This, because of our ILS’s trainer approach.
It is noted that interaction are based on a structure that allows
an enriched emotional intervention. This through a pedagogical
agent (Mora-Torres, Laureano-Cruces, & Velasco-Santos, 2010).
As stated previously the performance of the agents is divided
into two subagents, one of which carries out the diagnosis, the
objective of which is to observe the environment (comprised of
the student’s development). Following this observation, evi-
dence is obtained based on the agent’s perceptual abilities in
order to know if the student uses, does not use or incorrectly
uses the skill which is monitored and controlled exclusively by
that agent. The error(s) made are detected by means of the ob-
servations and the other sub-agent is activated, represented by a
MicroWorld, the task of which is to create an environment
which helps the student to clarify his or her doubts, all of this
through the use of didactic strategies that will guide this inter-
vention until the student is led back to the principal environ-
ment, where the error was made.
Figure 9.
Screen with the motivation questionnaire.

S.-G. LOURDES ET AL.
Figure 10.
Principal screen for the exercise of data types agent.
Figure 11.
Screen for the exercise of data types agent.
Figure 12.
Screen for the learning object for a student.
For management of errors committed by the student, three
types of errors were defined in the ILS: mild (M), serious (S)
and fatal (F). This is where ProgEst determines the distinct di-
dactic strategies to apply depending on the type of error. For
this case of application we assume that the student commits an
error in the exercise (scenario).
For this example, we will apply solely the case of mild errors.
Serious errors will be for a student with theoretical type learn-
ing with external motivation. For cases where the error is:
1) Mild error (M), imply an overall attention deficit more
than a lack of knowledge, that is to say, they possess the spe-
cific knowledge and may have used it on previous occasions;
nonetheless, due to a lack of attention they become disoriented
and fail to complete part of the process. In this case the system
verifies which type of learning and motivation the user pos-
sesses (external or internal). As the student’s learning type is
theoretical and he or she is motivated, the system applies the
message We can do it together. You must keep trying! as an
operational/cognitive strategy, and permits the student to return
to the scenario and continue if and when the number of oppor-
tunities has not been used up.
2) Serious error (S), imply a significant lack of conceptuali-
zation which leads to failure of the data types application (Fig-
ure 13). As this case involves a serious error, the ILS solely
allows one opportunity to respond to the exercise, verifies if the
motivation is internal or external and applies a cognitive opera-
tional strategy, displaying the following phrase through the
interface: Let’s continue. You can do it! It activates the data
types learning object thorough the Moodle Learning Manage-
ment System (LMS), where the student reviews the theoretical
information and the control structure application examples.
Testing and Results Analysis
The ProgEst system was applied to a group of 33 students
from the core engineering course at the Universidad Autónoma
Metropolitana campus Azcapotzalco. An access code for the
system was assigned to each student, each of who completed
the questionnaire learning style and internal motivation of the
object of study.
As shown in the graph, (Figure 14), 51.52% of the group of
students has a reflexive learning style, 24.24% is theoretical,
21.21% is pragmatic and solely 3.03% is active.
Similarly, we can see in the graph that the internal motiva-
tion of the object of study is 42.42% external motivation,
39.39% internal motivation and 18.18% internal-external moti-
vation (Figure 15).
As aforementioned, the system’s teaching and learning proc-
Figure 13.
Screen when the student had a serious error with a theoretical learning
style external motivation.
186

S.-G. LOURDES ET AL.
ess is based on an inference engine inspired in the human tuto-
rial process. Figure 16 presents data on the total student group
along with the percentages of the nine elements of the inference
engine (Laureano-Cruces et al., 2010a). These elements are
connected in a causal matrix which allows us to see the causal
interconnection of each element with the others. The learning
style was also added to these elements, which will have a direct
impact on the interface and internal motivation of the object of
study. All of these factors permit selection of the operational
strategies in the appropriate moment.
(because they committed errors or the system interrupted them
due to inactive time ), and th e types of internal motivation of the
object of study and the learning style observed in the student, as
shown in Figure 18, of the 63% of students who did not com-
plete the exercise, 29% are reflexive with external motivation,
14% are reflexive with internal motivation, 10% are pragmatic
with internal motivation, 9% are pragmatic with external mo-
tivation, 9% are reflexive with internal-external motivation, 5%
are theorical with external motivation, 5% are theorical with
As observed in Figure 16, 100% show interest and desire to
complete the task and solely 36.36% of the group concluded the
task successfully.
We can see in Figure 17 that 60.61% committed errors. The
percentage of students who committed these errors in the first
opportunity was 36% and the system applied a cognitive opera-
tive strategy, as it constituted a mild error (see errors in Tables
1 and 2). The Data Types of the LO was activated through the
Moodle (LMS) for the purpose of reinforcing the knowledge. It
can also be observed in the graph that these students took more
time to complete the exercise than had been estimated, which
constitutes inactive time, possibly due to the fact that the stu-
dent did not possess sufficient knowledge to complete the task
and the system activated the structured programming material,
beginning from the basic programming knowledge, in order to
be able a review of the material for the student. Similarly, it can
be seen that a very low percentage of 6%, one student, resigned
to continue the exercise.
Figure 14.
Learning style.
Of the two presented types of errors, the Data Types Agent
applied the learning strategies described in Tables 1 and 2 to-
gether with the inference engine, in accordance with the afore-
mentioned. As a result, twelve students of the group completed
the scenario determined for the Data Types Agent (Figures 16
and 17).
Figure 17 also shows that 36.36% completed the exercise in
the first opportunity, and these students learned 100% of the
material. For the case of students who did not complete the task Figure 15.
Motivational orientation o f student.
Inference Engine Data
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Error IdleTimeLearningQuitingInterruption Cog/Oper
Strategie s
HelpDesire Interest
Figure 16.
Inference engine data.

S.-G. LOURDES ET AL.
Table 1.
Classification of the didactical tactics and didactical actions to b e managed by the diagnostic subagent, applied in ProgEst.
Event Objective
Expressions for Cognitive/O perative
tactics, inter ruptions a n d to
ask for help. Actions Number of Response Opportunities
Interest (1)
Desire to proceed (2)
Internal and
External
Good job! Keep it up! You are
very intelligent and are making
very good progress i n t his task!
Internal It’s a good time to express doubts!
This subject is not so easy.
The expression appears. Next, the
system sends t h e student to consult
the subject material and permits him
or her to continue with the exerci se
after seeking help.
The reques ts f or help a r e not
limited as long as the student
continues the exercise.
Ask for help (3)
External ¡Hey Askin g for help doe sn’t
mean you are not able to do i t!
The expression appears. Next, the system
instructs the student to consult the subject
material and permits him or her to continue
with the exercise after seeking help.
The reques ts f or help a r e not
limited as long as the student
continues the exercise.
Internal Would you like to know
more about this?
Upon pressing the Interruption button,
the exercise is interrupted and the
expression appears.
The reques ts f or help a r e not
limited as long as the student
continues the exercise.
Interruption (5)
External Would you like more help?
Upon pressing the Interruption button, the
exercise is interrupted and the expression
described in the previous column appears.
The student is p ermitted to continue w i th
the exercise after seeking help.
The reques ts f or help a r e not
limited as long as the student c ontin-
ues the exercise.
Renunciatio n ( 6) Internal and
External Keep trying! You are getting closer!
Upon pressing the Exit button,
the exercise ends and the
expression appears.
Upon pressing the button, the sys t em
does not allow the student to return
to the exercise and sends him or
her to the m aterial selected based
on the CHA EA questionnai re.
Internal You are a winner! Remember
all of your achievements!
Upon correct comple ti on of the
exercise it opens and the student
is shown the expression.
Learning (7 )
External Success in performing this exercise demonstrates the
newly ac q ui red skills.
Upon correct comple ti on of the
exercise it opens and the student is
shown the e xp ression.
Inactive Period (8) Internal and
External Hey! It’s time to get to work!
After 30 seconds it opens and the e x pr ession
appears. If the student does not respond to
the second inactive period (lasting 30
seconds) he or she loses on e of the 3
opportunities allowed by the system
to complete the exercise.
The student may request a
maximum of 3 rest periods.
Table 2.
Classification of errors based on the d idactical tactics a nd the didactical actions , to be managed by t he diagnostic subagent, applie d i n ProgEst.
Error (9) Internal/External
Objective Number of Response Opportunities Actions Operative/Cognitive Strategy
Minor Internal and
External Three opportunities
Allows the student to return to the
situation and continue, as long a s th e
maximum number of opportunities
has not bee n r eached.
The following phrase appears:
“We can do it together. You have
to keep trying!
Serious I nternal and
External One opportunity Makes the stude n t reinitiat e the exercise. The following phrase appears: “Keep goin g. You can do it!
Fatal Internal and
External One opportunity Exits the exercise.
The followi ng phrase appears:
“Keep tryi n g, you are getting close” and
sends the student to the domain material
which corresp onds to the learning style.
188

S.-G. LOURDES ET AL.
Statistics the exercise according to the errors
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
TaskCompletionLearning 1st.Opportunity 2nd.Opportunity ReviewCourseMaterialIdleTime Quitting
Figure 17.
Statics the exercise according to the errors.
TheoricalStylewith 
Motivatio nIn te rna ll y
14%
TheoricalStylewith
Motivatio n Externally
5%
Refl e xiv e Stylewith
Mo tiv a tion In te rn al l y
14%
Refl ex iv e Stylewith
Motivatio nExternally
29%
Ref le xi ve Stylewith 
Motiva tion Internal l y
and External l y
9%
PragmaticStylewith 
Motivatio nExternal l y
9%
PragmaticStylewi th 
Motiv ati o n Internally
10%
PragmaticStylewith
Mo tiv a tion In te rn al l yand 
Exter n al ly
5%
TheoricalStylewith
Motiv ati o n Internal lyand 
Ex ternally
5%
Task NoCompleted
Task Comple ted
Figure 18.
Students that didn’t conclude the exercise.
internal-external motivation and 5% pragmatic with internal-
external motivation. As aforementioned, this allows us to con-
clude that the majority of the students with external motivation
were eliminated from the application by the system because
they failed to respond to the questions in the exercise within the
time allowed, possibly due to the fact that they needed more
examples of data types applications and exercises to understand
the di fferent data types.
Conclusion
The implementation of AI techinques in the development of
the ILS by means of the inference engine permits recommenda-
tion of distinct strategies, for the purpose of managing the com-
plexity of the prediction of didactical strategies in order to
strengthen the student’s teaching and learning process, taking
the results of the learning styles and motivational orientation
questionnaire into account.
In this system, the implementation of the inference engine
based on a general didactics tutor permits: 1) dynamic interac-
tion in the decision-making process in order to select the best
educational strategy; 2) predict the possible future status and
thus personalize the interactions through the eleven elements
which constitute the student model; 3) prevent undesirable
states, such as resignation or error, by means of these interac-
tions; and 4) develop a specialized method of handling errors
caused by reactive agents. An analysis and design methodology
has been created, which may be utilized to develop other ILS’s
that contain learning objects, through the use of the model de-
signed for the general didactic tutor.
Nonverbal communication plays an important role in our
human relationships, as it influences the other person through
expressions and transmitted in-formation on the emotional state
of the partners. With this in mind it was considered an avatar
(in the case study pedagogical agent) as the appropriate inter-
face for ILS-user interaction. The consideration of the avatar
(pedagogical agent) is appropriate if the intervention is based
on an emotional structure that has been designed according to

S.-G. LOURDES ET AL.
the perceived emotional state of the user.
In order to strengthen the teaching and learning process, the
followings are considered:
 Include more specialized subtutors.
 Continue to develop the interface using a pedagogical agent.
Acknowledgements
This Project is part of the Divisional Project Research Com-
putación Suave y Aplicaciones in the specific line of Intelligent
E-Learning, and Formación de Recursos Humanos funded by
Universidad Autónoma Metropolitana-Azcapotzalco.
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