Intelligent Tutoring System 101

8/4/2019 Intelligent Tutoring System 101

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Intelligent Tutoring System

1. INTRODUCTION

With the advancement in technology, computers have become es -

sential tools in developing systems that cater to the different needs of users.

Currently, many works have been done in the field of education. Systems

known as intelligent tutoring systems (ITS) were developed to teach students

on specific topics, test their knowledge by giving exercises, and provide

remediation on topics students did not perform well. An intelligent tutoring

system is a computer program for educational support that can diagnose prob -

lems of individual learners. Such diagnostic capability enables it to adapt

instruction or remediation to the needs of individuals. Currently, the state of

ITS is focused on one-on-one learning instruction.

However, in reality, students can also learn through interactions with

his/her peers or work in a team (or a group). The information student receives

from his peers can help improve his comprehension on the topics at hand. A

new learning paradigm has emerged aiming on this area and this new learning

paradigm is known as collaborative learning. Collaborative learning empha -

sizes on how students function in a group and how the student's interaction

with his peers or work in a team can help improve students learning. This can

be seen as either gaining new knowledge or verifying the correctness of what

the students had learned so far. Meanwhile, one of the major issues in Distrib -

uted Artificial Intelligence involves multi - agency.


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The agents in a multi agent system are designed to solve a kind of

problem. This is based on the fact that agents are autonomous and can

recognize their own existence and the existence of other agents. Agents help

each other in order to achieve a common purpose within a certain

environment. Agents can assist each other by-sharing the computational load

for the execution of subtasks of the overall problem, or through sharing of

partial results that are based on somewhat different perspectives of problem

solving on the overall.

In the last half- decade, ITS have moved out from labs into class-

rooms and workplaces, where some have proven to be highly effective as

learning aids.


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2. LITERATURE REVIEW

Introduction to Intelligent Tutoring System:

An intelligent learning environment is a relatively new kind of intelligent

educational system, which combines the features of traditional Intelligent

Tutoring Systems (ITS) and learning environments. Traditional ITS are able to

support and control student's learning on several levels but doesn't provide

space for student - driven learning and knowledge acquisition. An intelligent

learning environment (ILE) includes special component to support student -

driven learning, the environment module. The term environment is used to refer

to that part of the system specifying or supporting the activities that the student

does and the methods available to the student to do those activities". (Burton,

1988, p. 109.) Some recent ITS and I L E include also a special component

(we call it as manual '') which provides an access to structured instructional

material. The student can work with the manual via help requests or via

special browsing tools exploring the instructional material on her own. An

integrated ILE, which includes the environment and the manual components in

addition to regular tutoring component, can support learning both procedural

and declarative knowledge and provide both system - control led and student -

driven styles of learning.

An ITS is really a knowledge communication system. It can be so

defined because the main emphasis in the development of these systems is to

provide them with access to a representation of the knowledge that the system


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then attempts to communicate to a student. In an ITS the emphasis is placed

upon the knowledge (what) to be communicated and not on the mechanism

(how) used to present the knowledge to the student.

WHAT IS AN ITS?

ITS is a system that provides individualized tutoring or instruction. Each

ITS must have these three components: knowledge of domain, knowl edge of

the learner, and knowledge of teacher strategies. The domain refers to the

topic or curriculum being taught. The learner refers to the student or the user

of the ITS. The teacher strategies refer the methods of instruction and how the

material shall be presented. This basic outline of requirements has been

around since 1973 when it was introduced by Derek H. Sleernari and J.R.

Hartley. The goal for every ITS is to communicate its embedded knowledge

effectively.

UserUser Interface Interface Engine Knowledge base


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HOW DOES IT WORK?

A student learns from an ITS by solving problems. The system

selects a problem and compares its solution with that of the student and then it

performs a diagnosis based on the differences. After giving feedback, the

system reassesses and updates the student skill model and the entire cycle is

repeated.


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HOW ITS WORKS

In order to provide hints, guidance, and instructional feedback to

learners, ITS systems typically rely on three types of knowledge, organized

into separate software modules (as shown in Figure 1). The "expert model"

represents subject matter expertise and provides the ITS with knowledge of

what its teaching. The "student model" represents what the user does and

doesn't know, and what he or she does and doesn't have. This knowledge lets

the ITS know who it's teaching. The "instructor model" enables the ITS to

know how to teach, by encoding instructional strategies, used via the tutoring

system user interface.

Figure 1: Components of an intelligent tutoring system

Here's how each of these components works. An expert model is a

computer representation of a domain expert's subject matter knowledge and

Expert

Model

InstructorModel

Student

Model

Tutoring

System UI

Simulation


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problem-solving ability. This knowledge enables the ITS to compare the

learner's actions and selections with those of an expert in order to evaluate

what the user does and doesn't know.

A variety of artificial intelligence (A!) techniques are used to capture

how a problem can be solved. For example, some ITS systems capture

subject matter expertise in rules. That enables the tutoring system to generate

problems on the fly, combine and apply rules to solve the problems, assess

each learner's understanding by comparing the software's reasoning with

theirs, and demonstrate the software's solutions to the participant's. Though

this approach yields a powerful tutoring system, developing an expert system

that provides comprehensive coverage of the subject material is difficult and

expensive.

A common alternative to embedding expert rules is to supply much

of the knowledge needed to support training scenarios in the scenario

definition. For example, procedural task tutoring systems enable the course

developer to Create templates that specify an allowable sequence of correct

actions. This method avoids encoding the ability to solve all possible problems

in an expert system. Instead, it requires only the ability to specify how the

learner should respond in a scenario. Which technique is appropriate depends

on the nature of the domain and the complexity of the underlying knowledge.

The student model evaluates each learner's performance to

determine his or her knowledge, perceptual abilities, and reasoning skills.


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Valerie Shute at the Air Force Research Laboratory presents the following

simple example of a hypothetical arithmetic tutoring system. Imagine that

three learners are presented with addition problems that they answer as

follows:

Figure 2: ITS Student Modeling Example

22 46

S t u d e n t A +39 +37

51 73

22 46

S t u d e n t B +39 +37

161 183

22 46

S tu d e n t C +39 +37

62 85

Though all three participants answered incorrectly, different

underlying misconceptions caused each person's errors. Student A fails to

carry, Student B always carries (sometimes unnecessarily), and Student C

has trouble with single-digit addition. In this example, the student supplies an

answer to the problem, and the tutoring system infers the student's

misconceptions from this answer. By maintaining and referring to a detailed

model of each user's strengths and weaknesses, the ITS can provide highly

specific, relevant instruction.


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WHAT ITS MUST DO?

Although there are many types of Intelligent Tutoring Systems

around, each one must behave intelligently, not actually be intelligent. They

must be able to: accurately diagnose student's knowledge structures, skills,

and styles diagnose using principles, rather than preprogrammed responses

decide what to do next adapt instruction accordingly provide feedback.


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BENEFITS OF INTELLIGENT TUTORING SYSTEMS

Intelligent tutoring system technology can make simulator - based

or other active training courses even more effective with the following fea tures

and benefits:

Increases student / instructor ratio from around 1:1 to, perhaps,

10:1 or more, to reduce training costs enormously, and still deliver close to a

one on - one learning experience.

Shortens training time and / or improve skill level even further than

simulator alone by individualizing instruction for each student.

Automatically optimizes individual learning.

Compensates for a shortage of expert instructors. Also enables

quickly coping with sudden unanticipated upsurges in student enrollment, re -

ducing or eliminating the need to hire and train more instructors.

Builds "student model" for each student, that includes:

Performance on training exercises.

Details of information / remediation received

Details of knowledge mastered / failed / unknown / misunderstood.

Performance on remediation activities.

Student preferred learning style.


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Adaptively makes decisions on how to best teach each stu dent

based on knowledge of student in student model", knowledge of principles to

be taught and embedded teaching method. Present student with ap propriate

simulation scenarios, descriptive material, or remediation material (including

reruns of simulations) which the ITS intelligently and automati cally selects.

Automatically assesses each student's actions, so an ITS can

provide a full record of student performance on the simulator to:

Aid instructor in helping student.

Provide a permanent record of student's training performance.

Aids and documents achievement of job mastery in critical skills.

Reduce administrative work

Adaptively improves its teaching style with each student the more

the ITS works with a student.

Provides each individual student with remediation in the spe cific

fine details that are needed.

Extensible to other applications besides education because of

ability to monitor people doing things and summarize this information. E.g.,

could automatically assist on - the -job individual air traffic controllers oper ate

their equipment under difficult circumstances by dynamically improving their

interfaces and decision support software based on information about in dividual

students gained by ITS during training.


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11) Works on standard PCs and across the Web, and thus can in -

crease the effectiveness of distance learning.

Enables the skills of a master instructor to be transferred to less

experienced instructors via the default instructional methods that the master

provides for a course of instruction in the software.

Allows training to keep pace with rapidly changing technology,

customization of products, and other sources of rapid change.

An intelligent tutoring system can be guaranteed to stay with a

student throughout the duration of a course and continue to learn about their

Individual students; human instructors may not.

15) For team training, intelligent software agents can be substi tuted

for real people.


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AI & INTELLIGENT TUTORING SYSTEMS

In 1982, Sleeinan and Brown reviewed the state of the art in

computer aided instruction and first coined the term Intelligent Tutoring

Systems (ITS) to describe these evolving systems and distinguish them from

the previous CA1 systems. The implicit assumption about the learner now

focused on learning-by-doing. They classified the existing ITS as being

computer-based (I) problem-solving monitors, (2) coaches, (3) laboratory

instructors, and (4) consultants. (Sleeman & Brown, 1982) The emphasis in

these systems was still as research platforms for refining Al theories, but now

researchers were thinking about representing student knowledge within these

systems. Here we find the first use of the term student model to describe an

abstract representation of the learner within the computer program.

Early attempts to model student knowledge were based on a

''buggy" model first proposed by Brown and Burton (Brown & Burton, 1978).

"Bugs" are student errors in discrete skills, such as incorrect carrying during

subtraction. Burton elaborated on this model with the DFBUGGY system

(Burton, 1982) DE8UGGY identified 130 "bugs" designed to account for

mistakes in subtraction. The challenge was to analyze the problem space

represented by the student's answers and determine which bug or set of bugs

best accounted for incorrect subtraction.


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Sleeman and Brown mention some learning issues related to the

problems involved in creating ITS. The y ac knowledge that much human tutor

communication is implicit and express the hope That ITS will provide a venue

for educational theorists to develop "more precise theories of teaching and

learning Their assumption is that such precision is possible, being necessary

for implementation of these theories within computer software. They also

discuss the need to construct environments that encourage collaborative

learning while acknowledging that researcher, (at that time) knew little about

how such cooperation takes place in natural learning settings.


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Table 1: ACT* assumptions and related principles for a computer-

implemented tutor.

ACT* Assumptions Corresponding Tutoring

Principles

Problem-solving behavior is

goal driven

Communicate the goal

structure underlying the problem-

solving task

Declarative and procedural

knowledge are separate. The units of

procedural knowledge are IF-THEN rules

called productions.

Represent the student's

knowledge as a production set.

Initial performance of a task is

accomplished by applying weak (general)

procedures to declarative knowledge

structures.

Provide instruction in the

problem-solving context; let student's

knowledge develop through successive

approximations to the target skill.

Task-specific productions arise

by applying weaker productions to

declarative knowledge. These task-specific

productions underlie more efficient

performance

Adjust the step size of

instruction as learning progresses

The student maintains the

current state j of the problem in a limited

capacity working memory

Minimize working memory

load


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Although Anderson and his colleagues created ACT* as a cognitive

theory, they believed that it was rigorous enough to test by implementing the

principles in computer software. Two of th e best-known examples are the

Geometry Tutor (koedinger & Anderson. 1993) and LISPITS (LISP Intelligent

Tutor ing System). The LISPITS system, a program for teaching LISP

programming, .was designed to implement these pr inciples in the context of

"model tracing LISPITS attempts to model the steps needed to write a

LISP program. The program then compares the actual steps that the student

takes wit h this model. Corbett and Anderson call the monitoring and

remediation process knowledge tracing. Their goal is a mastery model, where

every student masters 95% of the rules for a given set of exercises before

moving to the next section. Corbett & Anderson found that students using

LISPITS completed the mastery model exercises considerably faster than

students who worked alone, but not as fast as students who worked with

human tutors.

Anderson's name has become synonymous with ITS work in so far as

people often speak of "Anderson-style tutors" (Chipman, 1993) Perhaps this

is because his systems are some of the few that have actually been used in

classroom settings and were not solely research projects.

Intell igent Tutoring Systems are built on a fairly well established

architecture, which relies on three interconnected software modules:

1) Expert Module, 2) Student module

3) Curriculum module.


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CONCLUSION

With the advancement in technology, computers have become es -

sential tools in developing systems that cater to the different needs of users.

Currently, many works have been done in the field of education. Systems

known as intelligent tutoring systems (ITS) were developed to teach students

on specific topics, test their knowledge by giving exercises, and provide

remediation on topics students did not perform well. An intelligent tutoring

system is a computer program for educational support that can diagnose prob -

lems of individual learners. Such diagnostic capability enables it to adapt

instruction or remediation to the needs of individuals. Currently, the state of

ITS is focused on one-on-one learning instruction.


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REFERENCES

www.google.com

www.its.com
http://www.google.com/http://www.its.com/http://www.google.com/http://www.its.com/


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CONTENTS

Sr. No. Contents Page No.

1) INTRODUCTION 1

2) LITERATURE REVIEW 3

3) WHAT IS AN ITS? 4

4) HOW DOES IT WORK? 5

5) HOW ITS WORKS 6

6) WHAT ITS MUST DO? 8

7) BENEFITS OF INTELLIGENT TUTORING SYSTEMS 9

8) AI & INTELLIGENT TUTORING SYSTEMS 10

9) CONCLUSION 17

10) REFERENCE 18

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Intelligent Tutoring System 101