THE ROLE OF LEARNING STYLES IN INTELLIGENT

TUTORING SYSTEMS

Paulo Alves, José Pires

Instituto Politécnico de Bragança, Campus de S. Apolónia apartado 134, Bragança, Portugal

Luís Amaral

Departamento de Sistemas de Informação, Universidade do Minho, Guimarães, Portugal

Keywords: e-learning, learning styles, Intelligent tutoring systems.

Abstract: The Bologna Process changes the educational paradigm, to be focus on the student and in the learning

outcomes. The majority of e-learning platforms are used as mere repositories of content, based on the

classroom paradigm and don’t support the individualism of each student learning process. Through the

integration of new pedagogical methodologies based on students learning styles, we present an approach to

intelligent tutoring systems in order to improve the learning process. This article is focused on the

importance of learning styles to create the student model in intelligent tutoring systems and what was the

student’s feedback about the adaptation of the system to each learning experience.

1 INTRODUCTION

E-learning is increasingly spread in various levels of

education, whether in education support presence,

either in the distribution of the distance courses.

The e-learning platforms today, known

generically of virtual environments for learning,

offer several features that allow the management of

courses, communication and distribution of content.

The vast majority of the platforms are based on

the paradigm of the classroom, where knowledge is

transmitted the same way for all students. This

paradigm uses the contents as the only means of

transfer of knowledge.

The Bologna process that aims to create a

European Higher Education Area by 2010, pretends

to change this paradigm, in order that focus the

educational process on the student and in the

learning outcomes, reflecting the new demands of

knowledge-based societies, which implies a more

personalized education.

According to Dias (2004), building spaces for

online learning is a challenge that goes beyond the

simple transfer of content to the Web. This approach

tends to transform the environments in online

repositories of information and not in the desired

spaces of interaction and experimentation.

To allow a greater adaptation of the learning

environment based on the student's profile, it is

proposed the adoption of theories of artificial

intelligence in education, based on the experience of

students so that the content and contexts of learning

can be reused and adapted to new situations.

In the last three decades, the artificial

intelligence has been adopted in various forms of

education. The initial experience of adoption of

artificial intelligence in education dating back to

1984. Several other approaches appeared in the

adoption of artificial intelligence in education, and

in 1988 one of the first architectures of intelligent

tutoring systems was developed by Burn and Caps.

One of the most important issues in the

adaptation of an intelligent tutoring system is the

modulation of student behaviour in order to adapt

the pedagogical model to the student model.

This adaptation to be more effective is necessary

to identify the student profile, based on several

parameters. One of the most important parameter is

the student learning style. Each student has his own

style of learning, which influences the collaboration

during the learning process

In this context, the development of adaptive

learning environments, based on the student profile,

this type of systems can contribute to the change in

315

Alves P., Pires J. and Amaral L. (2009).

THE ROLE OF LEARNING STYLES IN INTELLIGENT TUTORING SYSTEMS.

In Proceedings of the First International Conference on Computer Supported Education, pages 314-319

DOI: 10.5220/0001983903140319

 SciTePress

the educational processes, based on new pedagogical

methodologies integrated with artificial intelligence

techniques in order to provide learning environments

adaptable to the needs of each student.

The main motivation of this work focuses on the

development of intelligent tutoring systems, to

improve the educational paradigm, considering the

student learning style and the collaboration in the

learning process.

2 LEARNING STYLES

The basic theory of learning styles is that different

people learn in a different way. One way to see the

learning styles is to connect them with the learning

cycle advocated by Kolb (Kolb 1984), where

learning is seen as a continuous process based on

practical experience that incorporates a set of

observations and reflections.

Later, this model was developed by Honey and

Mumford (1986) creating a questionnaire of learning

styles based on the model proposed by Kolb. It was

identified by the authors four learning styles, related

to the four stages of the learning cycle proposed by

Kolb: activist, reflector, theorist and pragmatist.

Each learning style has the follow characteristics

(Honey and Mumford, 1986):

• Activist - Students with an active style involve

themselves fully and unreservedly in new

experiences. Have an open mind, are optimistic,

which makes them enthusiastic about something that

is new. Tend to act first and consider the

consequences later. They engage in many activities

and when they lose the enthusiasm they change to

another activity. The main philosophy is to try

everything they can. They have great enthusiasm

with the challenges of new experiences, but

discourage with the implementation and

consolidation of ideas. Tend to get involved in tasks

with other people, but usually try all activities

centred on them.

• Reflector - The reflector like to be more in the

rear to observe and reflect on experiences from

different perspectives. Collect data and prefer to

think about that before making any conclusions. Its

main philosophy is to be cautious. They are very

balanced, preferring to consider all possible angles

and implications before taking any action. They

prefer to watch other people in action. The reflector

people are by nature discreet.

• Theorist - People with a predominantly

theoretical style incorporate comments into complex

theories, but they are logical. They consider the

problems on a vertical way, step by step and in a

logical way. Assimilate facts based on consistent

theories. The main philosophy is "if it is logical then

it is good." They have an independent spirit and like

to formulate principles, theories, models,

assumptions and thoughts. The approach of the

problems is mainly logic.

• Pragmatist - The pragmatists tend to experiment

the ideas, theories and techniques for checking

whether they work in practice. Having new ideas

they seek for an opportunity to try it in practice.

They are impatient in discussions with subjective or

vague ideas. They are essentially practical and like

realistic decisions to solve problems. The main

philosophy is: "there is always a better way to do

things" or "if it works then it's good."

The styles of learning have become increasingly

important in education, given the change in the

paradigm of education caused by the transition to the

knowledge society. The lifelong learning paradigm

leads to new learning context, which are

increasingly more heterogeneous, where is important

to take into account the learning styles of each

student to provide an education more effective and

focused on the student.

Figueiredo and Afonso (2005) consider the

context and content as the key elements of the

learning model. The learning model defines the

learning activities as the situation in which

individuals learn. The content is the information that

is structured and consists of text, materials,

multimedia resources and lecture. The context is a

set of circumstances that are relevant to the student

to build knowledge through its connection to the

content.

In the model presented, the teacher has a

bipartite role in the presentation of content and

creating the learning context. The context can be a

classroom or a virtual learning environment, in

which the role of teacher is more focused on content

in the case of a classroom, and the context in the

case of a virtual learning environment.

The contents assume the role of transmission

knowledge, where information is transformed into

knowledge through a given learning activity.

The integration of intelligent systems in the

learning support, allows an adaptation of content and

contexts to the learning style of each student,

providing adaptive tools to support collaboration

(Lesgold et al. 1992, Goodman et al. 2003).

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3 INTELLIGENT TUTORING

SYSTEMS

The adoption of artificial intelligent in education has

the goal to improve the learning process adapting the

contents and the learning environment to the student

profile. The student profile is based on his learning

style, learning needs, goals and choices.

The first systems adopting artificial intelligent

techniques were the Intelligent Tutoring Systems

(ITS). Kearsley defined an intelligent tutoring

system as an application of artificial intelligence

techniques to teach students (Kearsley, 1987).

Sleeman and Brown defined an intelligent tutoring

system as a program that uses artificial intelligence

techniques for representing knowledge and carrying

on an interaction with a student. According to

Sleeman and Brown, an intelligent tutoring system

must have its own problem-solving expertise, its

own diagnostic or student modeling capabilities, and

its own explanatory capabilities (Sleeman & Brown,

1982).

One of the first architectures of a ITS system was

presented by Burn and Caps in 1988. This

architecture was based on four main components:

curriculum module, student module, tutor

(pedagogical module) and the interface module

between the student and the system. This basic

architecture was improved by several researchers,

including Ong and Ramachandran in 2003, Thomas

in 2003, Bass in 1998, Choquet et al. in 1998, Titter

and Blessing in 1998 and Nkambou and Gauthier in

1996.

Modern intelligent tutoring system architectures

(Figure 1) are very similar to the Burn and Caps

proposed architecture. The four modules are

represented frequently as the domain module,

student model, pedagogical module and the interface

module.

Figure 1: Components of an intelligent tutoring system

(Ally, 2004).

The student has the main role in the intelligent

tutoring system. All the features of the system have

the mission to adapt the interface and the

pedagogical material to the student profile and his

preferences.

The domain module is a knowledge management

system, storing all the concepts that the system

pretends to transmit to the student.

Connected to the domain module are the student

model and the pedagogical module. The student

model represents the learner behavior, his profile,

learning style, motivation level and his interests.

This model is based on artificial intelligent skills

that simulate the human behavior. All the student

behavior is recorded in the system and used for

“reasoning” and adapt the domain module to the

leaner needs. The pedagogical module acts has a

virtual instructor, presenting the contents in an

appropriate sequence, based on the student skills and

his learning style. This is an interactive process and

this module has the mission to explain the concepts

to the student given several points of view and

supporting all the learning process.

With the capacity to communicate and interact

with the student, the interface module has an

extremely important mission. If one ITS had a

powerful pedagogical, domain and student model,

but the interface module is very poor, the ITS will

not be effective because the interface is the front of

all the system and has the ability to cap all the

attention of the learner. To develop a good interface

module is necessary to consider the usability issues

of a user computer interface, because this module

interacts with the user and the other components of

the system. If the interface fails all the other

modules fail too.

The type of intervention of the pedagogical

module in the system is very important for the

student creativity and motivation. Wenger considers

that is more efficient to let the student search for the

solution for one problem before make any

intervention (Wenger, 1987).

In order to adapt the tutoring system to the

learning needs, we propose the adoption of Learning

styles to intelligent tutoring systems in order to

provide a more effective adaptation, taking in

consideration student motivation and the

effectiveness of each learning tool according to the

student learning style.

4 THE ROLE OF LEARNING

STYLES IN ITS

The current generation of learning management

systems is fundamentally based on the concept of

THE ROLE OF LEARNING STYLES IN INTELLIGENT TUTORING SYSTEMS

317

virtual classroom, allowing the distribution of

contents and its discussion, but are still not very

efficient in the collaboration.

The learning management systems, even those

that are based on constructivist theory, where

collaboration is essential, not suggest ways of

adapting the learning process to specific needs of

each student.

The next generation of e-learning platforms, it

seems that this concept will changed, where the

learning support it will be the most important

component, leaving the teaching and production of

contents as less prominent. Thus, the adoption of

intelligent tutoring systems can contribute to the

improvement of learning, adapting the presentation

of content and offering support in its interpretation

and discussion, which allows a personalized

education and adapted to the learning style of each

student.

The intelligent tutoring systems have been

developed for the typical individual education

(computer-student). With the advent of the Web in

education, several authors studied the adoption of

tutors in collaborative environments, giving them the

capacity to work together, using collaborative tools

(Lesgold et al. 1992, Goodman et al. 2003).

Khuwaja (1996) says that while intelligent

tutoring systems are implemented with considerable

success, they are not practical enough to be used in

the real world. This may change with the

introduction of new methodologies applied to

multiple areas, in case of face-to-face education or at

distance.

ITS systems are based on computer-based

training (CBT) technologies and are learner centric.

The main disadvantage appointed to these systems is

the limitation of the student creativity, because the

student needs some autonomy in their process of

knowledge construction. In the other side if the

system is very passive the motivation of the student

can decrease quickly.

The heterogeneity of students in higher education

will be increased as a result of the demands of

society and knowledge economy, which demands a

life-long learning approach.

The lifelong learning has been defined as one of

the priorities of the Bologna Process. Thus, it will be

increasing the number of students in different

contexts of learning. To meet these new challenges

is a necessary a greater customization of learning

methodologies, to support each student learning

style.

The identification of the student's learning style

is an important requirement for the ITS systems to

adapt the learning environment to the needs of each

student.

To implement this approach we develop a

generative intelligent tutoring system (GITS), based

on the student learning style, to module his/her

profile.

The student module is based on Honey-Alonso

learning styles questionnaire (CHAEA), adapted and

validated for the Portuguese language by Miranda

(Miranda 2005).

To identify the learning style of each student it

was integrated in GITS system the CHAEA

questionnaire. The student when accesses the system

is invited to complete the questionnaire.

The questionnaire consists of eighty questions

enabling the identification of preferences for each

style: active, reflective, theoretical and pragmatic.

To evaluate the GITS system we made a case

study in two different groups. One of Introduction to

Computer Science, composed by 20 students, and

other of Web Development, composed by 15

students. The number of styles identified is less than

the number of users of the platform, because the

answer to the questionnaire is voluntary and does

restrict the use of the GITS system.

To identify the students’ learning styles we

consider only the experimental group, which used

the GITS. The control group used a different

platform without the ITS system.

The experimental group of Web Development

had a smaller membership in response to the

questionnaire that the group of Introduction to

Computer Science.

The analysis of the results indentifies a moderate

preference for each style: active, reflective,

theoretical and pragmatic. Only 7% of students had a

very high preference for reflective style and 13% by

the theoretical. There isn’t any student with a very

high preference to the pragmatic style. The moderate

level is the predominant.

Graphic 1: Learning styles of research group.

In the adaptation of learning context made by GITS

to each student style, shows that most of the students

had a moderate preference, which implies a very

narrow adaptation.

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Figure 2: GITS interface to add notes to contents.

Only for students with a very high preference for the

active style the GITS made an adaptation of the

learning activities to explore the potential and

students creativity. For students with a very high

preference for the reflexive and theorist styles, the

system did an adaptation on forums, to improve

reflection, and on the Chat for the Active style to

promote a direct discussion.

The GITS system modulate the user behaviour

based on the student learning style, but with a main

moderated preference for each style most of the

students had a standard view of the system. Only the

high and very high preferences change the

appearance of tools, contents and activities.

4 CONCLUSIONS

The use of intelligent systems has several

advantages in the support and personalization of e-

learning. The intelligent tutoring systems are

typically used in computer-based training (CBT) and

don’t support the collaboration and cooperation like

groupware and cooperative work technologies. We

propose the adoption of generative intelligent

tutoring system to support Web-based Educational

Systems.

The validation of the prototype was done through

data collection of the GIST prototype. We do two

case studies in two subjects, one in Introduction to

Computer Science and other in Web Development.

Based on the results we can conclude that the

adoption of collaborative and adaptive capabilities to

intelligent tutoring systems, like forums, and the

possibility to add notes to contents to share

knowledge, is a good feature to improve the learning

experience.

The organization of contents using learning

activities was highlighted as very important by most

of the students in the survey and the adoption of

learning styles to model the user profile was

considered important for the students.

The GIST system supports the student in their

learning activities, collaborative work, portfolio

management, agenda management, and shows

several points of view of some subjects, suggesting

Web resources to complement the student

knowledge.

These capabilities it was considered by the

students very important to improve the knowledge

and the collaboration, which can be adopted in

several learning management systems to provide a

more effective support in the learning process, going

in the direction of the needs of knowledge based

societies.

REFERENCES

Alves, P., Amaral, L., Pires, J., 2008, Case-Based

Reasoning Approach to Adaptive Web-Based

Educational Systems, ICALT '08: Proceedings of the

2008 Eighth IEEE International Conference on

Advanced Learning Technologies, pp. 260-261, IEEE

Computer Society, Santander

Ally M., 2004, Designing Distributed Environments with

Intelligent Software Agents, Idea Group Publishing

Bass, E., 1998, Towards an Intelligent Tutoring System

for Situation Awareness Training in Complex,

Dynamic Environments, Lecture Notes In Computer

Science; Vol. 1452, Proceedings of the 4th

International Conference on Intelligent Tutoring

Systems, Springer-Verlag

Dias, P., 2004, Comunidades de aprendizagem e formação

online, Nov@Formação, Revista Sobre a Formação a

Distância & E-learning, Inofor, pp. 14-17

Figueiredo, A. e Afonso, A., 2005, Context and Learning:

a philosophical framework, in A. Figueiredo e A.

Afonso (eds) Managing Learning in Virtual Settings:

The Role of Context, Hershey, PA, USA: Idea Group

Publishing

Goodman, B., Hitzeman, J., Linton, F., Ross, H., 2003,

Towards Intelligent Agents for Collaborative

Learning: Recognizing the Role of Dialogue

Participants. In Proc. of Artificial Intelligence in

Education (AIED), IOS Press, Amsterdam

Honey, P. and Mumford A., 1986, A Manual of Learning

Styles, Peter Honey, Maidenhead

Kearsley, G. P.,1987, Artificial intelligence and education:

Applications and methods, Addison-Wesley

Khuwaja, .R., Desmarais, M., Cheng, R., 1996, Intelligent

Guide: Combining User Knowledge Assessment with

THE ROLE OF LEARNING STYLES IN INTELLIGENT TUTORING SYSTEMS

319

pedagogical Guidance. International Conference on

Intelligent Tutoring Systems - ITS'96, 3.,196.

Proceedings, Berlin: Springer-Verlag

Kolb, D. , 1984, Experiential Learning, Prentice Hall

Lesgold, A., Katz, S., Greenberg, L., Hughes, E., Eggan,

G., 1992, Extensions of Intelligent Tutoring Paradigms

to Support Collaborative Learning. In S. Dijkstra, H.

Krammer, J. van Merrienboer (Eds.), Instructional

Models in Computer-Based Learning Environments.

Berlin: Springer-Verlag, pp. 291-311

Lesgold, A., Katz, S., Greenberg, L., Hughes, E., Eggan,

G., 1992, Extensions of Intelligent Tutoring Paradigms

to Support Collaborative Learning. In S. Dijkstra, H.

Krammer, J. van Merrienboer (Eds.), Instructional

Models in Computer-Based Learning Environments.

Berlin: Springer-Verlag, pp. 291-311

Miranda, L., 2005, Educação online: interacção e estilos

de aprendizagem de alunos do ensino superior numa

plataforma Web, Tese de Doutoramento, Universidade

do Minho

Moore, R., Lopes, J., 1999. Paper templates. In

TEMPLATE'06, 1st International Conference on

Template Production. INSTICC Press.

Nkambou, R., Frasson, M., Frasson, C., 1996, Generating

Courses in an Intelligent Tutoring System. In

proceedings of IEA-AIE'96

Ong, J., S. Noneman, 2000, Intelligent Tutoring Systems

for Procedural Task Training of Remote Payload,

Operations at NASA, Proceedings of

theIndustry/Interservice, Training, Simulation

&Education

Sleeman, D., Brown, J., 1982, Intelligent tutoring systems.

New York: Academic Pres

Smith, J., 1998. The book, The publishing company.

London, 2nd edition.

Wenger, E., 1987, Artificial intelligence and tutoring

systems: Computational and cognitive approaches to

the communication of knowledge. Los Altos, CA:

Morgan Kaufman

CSEDU 2009 - International Conference on Computer Supported Education

320