International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 2- June 2015
Multi User Student Model Intelligent Tutoring System
*1
Achi Ifeanyi Isaiah, #2Prof. Inyiama Hyacinth Chibueze, #3Agwu Chukwuemeka Odi
1
1
PhD Student, 2Professor, 2Lecturer 1
Department of Computer Science, Our Saviour Institute of Science and Technology, Enugu, Nigeria.
2
Department of Electronic and Computer Engineering, Nnamdi Azikwe Univeristy – Awka, Nigeria
3
Department of Computer Science, Ebonyi State University – Abakaliki, Nigeria.
Abstract - With massive deployment of computer networks
in various field of endeavour, people can access information
and communicate with others without being constrained by
space and time. Through network communications, people
can have access to information and discuss things with
others to solve their problems. Therefore, it became
necessary to develop an Intelligent Tutoring System (ITS),
that could allow several users access an ITS irrespective of
their locations through computer networks. In this paper, we
proposed a system of ITS which works by applying several
learning styles as the several paths to knowledge in
modeling the different student learning capacity in a typical
multi user Computer Assisted Learning (CAL) Environment
which is possible via computer network. To enable us
develop the new system, we analyzed the special needs for
developing a multi user CAL's for all the users involved
irrespective of their locations and environment where many
users are involved, and defined a set of parameters, which is
concerned with effectiveness of the learning process through
networks. A theoretical framework and some methodologies
are proposed based upon the concepts and the techniques of
Artificial Intelligence (AI) to cope with the problems such as
Rule Base System. This Rule Based system is then
implemented to guide the students during the learning
processes and to help the CAL system to present the learning
styles that is appropriate to the students.
Keywords - Intelligent Tutoring System (ITS), Computer
Assisted Learning (CAL), Rule-Based System (RBS),
Intelligent Tutoring and Evaluation System (ITES),
Cooperative Remotely Accessible Learning (CORAL),
Artificial Intelligence(AI).
I. INTRODUCTION
Education is the foundation of any nation. In the past
decade, people have tried to refine subject materials
and to develop new skills and tools for helping the
progress of education. Some researchers have
concentrated on the development of ComputerAssisted Learning (CAL) systems, which are designed
to provide an individual learning environment for each
student. Earlier CAL systems focused on the
interactions between computer and single student.
Some systems can even play the role of learning
ISSN: 2231-5381
partners. The purpose of providing some learning
partners is to encourage students learning through the
interactions among the students and those computer
partners. However, as human reactions are very
complex, it is usually difficult to simulate human
behaviors by computers. Moreover, when a student
fails to understand some unit of the subject materials,
he might have to stop learning if no instant help is
available [1]. Therefore, it is more desirable to have
human partners than computer partners [2].
Unfortunately, it is usually difficult for a student to
find a suitable learning partner at the time and place he
prefers to learn.
With the fast development of computer networks,
people can access information and communicate with
others without being constrained by space and time.
This provides a good opportunity to cope with the
learning problems. One of the most important features
of computer network is the fast communication ability.
Through network communications, people can discuss
things with others to solve their problems. Therefore,
how to implement multi user CAL systems on
computer networks is an interesting and challenging
issue. In this paper, we present a knowledge-based
system, which enables an intelligent tutoring method
in a typical multi user learning environment. To
achieve this goal, the subject materials have been
decomposed into pieces of modules to enable dynamic
combination of each learning unit. We also describe
learning status and teaching expertise with the several
learning styles as case to the student modeling process,
which is useful in finding best learning style or
combination of learning styles for each student.
Several learning styles, such as Auditory Learners
(Through Hearing), Visual Learners (Through seeing),
Kinesthetic Learners (Through Touch or practice) and
hybrid (Combination of two or more)[5] will be used
while modeling the student.
II. BACKGROUND
Intelligent Tutoring and Evaluation System (ITES)
originated from the CORAL (Cooperative Remotely
Accessible Learning) in Taiwan [3]. It was built as a
World Wide Web (WWW) server to manage learning
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 2- June 2015
requests from students through computer networks.
The entire project was initiated by a research group at
National Chaio Tung University in Taiwan, which
consists of eight sub-tasks:
The study of network-based tutoring systems,
including pattern recording, remote data
retrieval and access control on networks.
The study of a network learning environment,
including real-time monitoring and tutoring
process control.
The study of wide area networked CAL's,
including
feasibility,
scalability
and
architecture.
Testing and evaluation of network-based
CAL's, including motivation and cognition
analysis.
The study of interface design for networkbased CAL's, including screen layout,
icons/windows design and knowledge
visualization.
The study of student modeling for networkbased CAL's, including the analysis of
hypertext navigation and communication
patterns.
The study of a knowledge-based system for
tutoring process control, including knowledge
representation for student characteristics and
communication parameters, real-time analysis
of student behaviors
and dynamic
arrangement of tutoring schedule.
The study of interaction pattern analysis,
including the analysis of social context.
Fig. 1. Multi User ITES [4]
The goal of the entire research group is to accomplish
a CAL system to help the learning progress via the
development of an intelligent tutoring environment on
computer network. The whole idea is depicted in Fig.
1[4].
The above figure shows the different users connected
to the ITS system via internet network. With the aid of
network, users could connect to the ITS from
anywhere.
III. RULE-BASED SYSTEM
In Artificial Intelligence programming, the rule basesystem is a set of "if-then" statements that uses a set of
assertions, to which rules on how to act upon those
assertions are created. In software development, rulebased systems can be used to create software that will
provide an answer to a problem in place of a human
expert. This type of system may also be called an
expert system. In this paper, we are modeling several
users that is hooked up to the ITS via the computer
network. This system should be able to evaluate each
student using the four assertions which is referred to
the different learning styles as path to model each
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 2- June 2015
student’s knowledge. As students are different, so also
their comprehension levels are different. Therefore,
this paper aims to predict and store for each student
the best path to their understanding of a subject matter.
IV.
DESIGN CONSIDERATIONS OF THE
ITES
Below is the system diagram of the proposed system,
showing the various components and how it works.
The system incorporates computer network and the
techniques deployed in interfacing multi-users on the
Intelligent Tutoring System (ITS) platform. The
diagram shows the user of the system and the
different knowledge sessions in the knowledge base
as well as the different path to tutoring the student as
adapted from Achi and Agwu, [6]. The Knowledge
base is represented in the diagram as Knowledge
database 1(KDB1) to Knowledge database N(KDBN)
where N means any number of knowledge database or
knowledge
base
with
any
number
of
path
respectively. This system models student by trying
several path and as well determine the best path to the
student knowledge so as to use it whenever the
student engages in any form of learning. For the case
of this paper, the paths are represented with various
learning styles. Every student has their preferred
To achieve the multi-user access as shown in the
diagram, the system is interfaced with several users
via the internet network. The users of the ITS new
system could be represented with user1 to userN,
where ’N’ means any number of user irrespective of
their location and distance.
learning styles but that will be determined by the
V. CONCLUSION
system while modeling the student.
Due to the advance of computer networks and
communication techniques, interactive learning, and
multi user ITS which is now possible through
computer networks had received a lot of attention. In
this paper, the Rule Base System, (RBS) expert system
for supporting the tutoring strategies of a multi base
multi user ITS learning environment is proposed. We
analyzed the parameters concerning the effectiveness
of learning through networks, and implemented the
multi user ITS with the support system of Rule Base
System and Artificial Intelligence(AI) approach. From
our experience in the research, it can be seen that a lot
of work is needed to enhance the ITS as a learning
system. We also believe that it is worthy to exert more
efforts in this field of study. Now, a knowledge
acquisition system for eliciting teaching experience is
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 2- June 2015
[3] C. Sun, and C. Chou, C. “Experiencing CORAL: design and
an aspect that will always be improved upon by
implementation of distance cooperative learning”, IEEE
different authors on this subject area and the system
Transactions on Education, 39(3), 357-366, 1996.
should also incorporate a means of keeping each
student record to avoid constant modeling of students
[4] H. Gwo-Jen “A Tutoring Strategy Supporting System for
which will become a part of this research.
Distance Learning on Computer Networks”, IEEE Transactions on
Education,41(4),IEEDAB,(ISSN 0018-9359), 1998.
REFERENCE
[1] P. Mitchell, and P. Grogono, “Modeling techniques for tutoring
systems”, Computer Education, 20 (1), 55-61, 1993.
[2] S. Hopper, “Cooperative learning and computer-based
instruction”, Educational Technology research & Development, 40
(3), 21-38, 1992.
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[5] P. Klein, "Rethinking the multiplicity of cognitive resources and
curricular representations:Alternative to learning styles and multiple
intelligences.". Journal of Curriculum Studies 35 (1), 2003.
[6] I. Achi and C. Agwu, “Multi Software Agent Based Intelligent
Tutoring System”, International Journal of Engineering Trends and
Technology (IJETT) – Volume 20 Number 5 – Feb 2015,pg 221,
2015.
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