Abstract

Intelligent Tutoring Systems (ITS) is the interdisciplinary field that investigates how to devise educational systems that provide instruction tailored to the needs of individual learners, as many good teachers do. Research in this field has successfully delivered techniques and systems that provide adaptive support for student problem solving in a variety of domains. There are, however, other educational activities that can benefit from individualized computer-based support, such as studying examples, exploring interactive simulations and playing educational games. Providing individualized support for these activities poses unique challenges, because it requires an ITS that can model and adapt to student behaviours, skills and mental states often not as structured and well defined as those involved in traditional problem solving. This project work presents a variety of projects that illustrate some of these challenges, our proposed solutions, and future opportunities.

1.0 Introduction

Intelligent tutoring systems (ITSs) are computer programs that are designed to incorporate techniques from the AI community in order to provide tutors which know what they teach, who they teach and how to teach it. AI attempts to produce in a computer behaviour which, if performed by a human, would be described as 'intelligent': ITSs may similarly be thought of as attempts to produce in a computer behaviour which, if performed by a human, would be described as 'goad teaching' (Elsom-Cook, 1987). The design and development of such tutors lie at the intersection of computer science, cognitive psychology and educational research; this intersecting area is normally referred to as cognitive science. For historical reasons, much of the research in the domain of educational software involving AI has been conducted in the name of 'ICAI', an acronym for 'Intelligent Computer- Aided Instruction'. This phrase, in turn, evolved out of the name 'Computer-Aided Instruction' (CAI) often referring to the use of computers in education. Nevertheless, to all intents and purposes, ITSs and ICAI are synonymous. However, though some researchers still prefer 'ICAI' (e.g. Self, 1988a, uses it in the title of his recent book), it is now often replaced by the acronym 'ITS' (Sleeman& Brown, 1982b).

The latter, which is also the author's personal preference, is certainly gaining support, as confirmed by the international conference on Intelligent Tutoring Systems held in Montreal, Canada, as recently as June 2013. This preference is motivated by the claim that, in many ways, the significance of the shift in research methodology goes beyond the adding of an T to CAI (Wenger, 1987). However, some researchers are understandably hesitant to use the term 'intelligent', instead opting for labels such as 'Knowledge-Based Tutoring System' (KBTS) or 'Adaptive Tutoring System' (ATS). Wenger (2008) prefers the label Knowledge Communication Systems. Nevertheless, most researchers appear to be reasonably content with the acronym ITS. This is fine as long as everyone involved with the area understands that the usage of the word 'intelligent' is, strictly speaking, a misnomer. This

does not appear to be the case, resulting in some very ambitious goals/claims, particularly in the more theoretical parts of the literature: this also appears to be a valid criticism of the entire AI literature.

The fact that ITS research spans three different disciplines has important implications. It means that there are major differences in research goals, terminology, theoretical frameworks, and emphases amongst ITS researchers. This will become apparent later in this paper. ITS research also requires a mutual understanding of the three disciplines involved, a very stressful demand given the problems of keeping abreast with even a single discipline today. However, some researchers have stood up to the challenge. As a result, a great deal has been learnt about how to design and implement ITSs. A number of impressive ITSs described in chapter two this research paper bear testimony to this fact.

                                    Fig. 1.ITS domains.

1.1       Theoretical Background 

Since the early 1970s, the field of Intelligent Tutoring Systems (also known as Artificial Intelligence in Education) has investigated combining research in Artificial Intelligence, Cognitive Science and Education to devise intelligent agents that can act as tutors in computer-aided-instruction (CAI). Traditional CAI systems support learning by encoding sets of exercises and the associated solutions, and by providing predefined remediation actions when the students’ answers to do not match the encoded solutions. This form of CAI

can be very useful in supporting well-defined drill-and practice activities. However, it is difficult to scale to more complex pedagogical activities, because the system designer

needs to define all relevant problem components, all solutions (correct or incorrect) that the system needs to recognize, and all possible relevant pedagogical actions that the tutor may need to take.

1.2              Statement of the Problem

            This research work is carried out to design and develop an Intelligent Tutoring System (ITS); the proposed system will be aimed at reducing the level of student’s dependency on their teachers.

1.3 Objectives of the study

The objectives of the study are to design and implement an Intelligent Tutoring System (ITS) that will be able to perform the following functions:

a.       The tendency to explain instructional material to oneself in terms of the underlying domain knowledge (self-explanation)

b.      To allow students interact by asking questions.

c.       To provide possible answers to the questions from students

d.      Test the students.

e.       Analyse their performance.

1.4 Significance of the study

            The significant of these study includes the following:

1.      It makes learning easier and more interesting to student in the sense that one can learn from the comfort of his or her home.

2.      Its improve the knowledge of  lecturer  strategies ( i.e. how to teach, in what order, typical mistakes and remediation,)

3.      It improve the standard of learning for student.

1.5 Scope of the Study

This study takes overview on intelligent tutoring system. Due to time factor, we will be focusing on networking since we have so many areas of study. Implementing all the fields of study will take several months before it is successfully implemented, that is the major reason why we are limiting our research  to only one field of study which is computer networking as student learns painlessly, successfully and without instruction from their teachers/lecturers.

1.6       Limitation of the study

Intelligent tutoring systems are expensive both to develop and implement. The research phase paves the way for the development of systems that are commercially viable. However, the research phase is often expensive; it requires the cooperation and input of subject matter experts, the cooperation and support of individuals across both organizations and organizational levels. Another limitation in the development phase is the conceptualization and the development of software within both budget and time constraints. There are also factors that limit the incorporation of intelligent tutors into the real world, including the long timeframe required for development and the high cost of the creation of the system components. A high portion of that cost is a result of content component building. For instance, surveys revealed that encoding an hour of online instruction time took 300 hours of development time for touring content. Similarly, building the Cognitive Tutor took a ratio of development time to instruction time of at least 200:1 hours. The high cost of development often eclipses replicating the efforts for real world application. Intelligent tutoring systems are not, in general, commercially feasible for real-world applications.

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