Application of Knowledge Based Systems in Education Dr Priti Srinivas Sajja Department of Computer Science Sardar Patel University Vallabh Vidyanagar,

Application of Knowledge Based Systems in Education

Dr Priti Srinivas SajjaDepartment of Computer ScienceSardar Patel UniversityVallabh Vidyanagar, Gujarat

Dr. Priti Srinivas Sajja6-7 February, 2009

Introduction and Contact Information• Speaker: Dr Priti Srinivas Sajja• Communication:

• Email : [email protected]• Mobile : 9824926020• URL : priti.sajja.info

• Academic qualifications : Ph. D in Computer Science• Thesis title: Knowledge-Based Systems for Socio-Economic Rural Development

• Subject area of specialization : Knowledge Based Systems

• Publications : 84 in International and National Books, Chapters and Papers

• Academic position : Associate Professor Department of Computer Science Sardar Patel University Vallabh Vidyanagar 388120


Lecture Plan

Knowledge Based Systems• Introduction to Knowledge Based Systems• Categories and Structures of KBS• Applications of KBS

KBS in Education• Symbolic Approach

• Parichay: Adult Literacy System for Leaning Gujarati Language• Multi Agent KBS fro e-Learning Accessing Distributed Databases on Grid • Multi-tier KBS Accessing LOR through Fuzzy XML

• Connectionist Approach • Symbolic verses Connectionist Approach• Soft Computing • Neuro-fuzzy System for Course Selection • Fuzzy-genetic System for Evolving Rule Bases to Measure Multiple

Intelligence • Acknowledgement, References and Contact


Artificial Intelligence

• “Artificial Intelligence(AI) is the study of how to make computers do things at which, at the moment, people are better”

• -Elaine Rich, Artificial Intelligence, Mcgraw Hill

Publications, 1986


Knowledge Based Systems

K

Knowledge Based Systems (KBS) are

Productive Artificial Intelligence Tools working in a narrow domain.


How Knowledge is organized?

Volume Complexity & Sophistication

Wisdom(experience)

Knowledge(synthesis)

Information(analysis)

Data

Data PyramidSource: Tuthill & Leavy, modified


Data

• Raw Observation• Stand alone numbers and symbols that do possess little value• Data are symbols that represent properties of objects, events and

their environments. • ANYTHING numbers, words, sentences, records, assumptions• Example BMI, 10, (smith, 50)


Information

• Processed Data• Smith weight is 50 Kg. • Information has usually got some meaning and

purpose


Knowledge

• Information can be processed further with the operations such as • Synthesis• Filtering• Comparing etc.

to get generalized knowledge


Wisdom

• Knowledge of concepts and models lead to higher level of knowledge called wisdom.

• One needs to apply morals, principles and expertise to gain and utilize wisdom.

• This takes time and requires a kind of maturity that comes with the age and experience.


Data Pyramid and Computer Based Systems

Basic transactions by operational staff using data processing

Middle management uses reports/info. generated through analysis and acts accordingly

Higher management generates knowledge By Synthesizing information

Strategy makers apply morals, principles and experience for generating policies

Wisdom (Experience)

Knowledge (Synthesis)

Information (Analysis)

Data (Raw Observations Processing)

Volume Sophistication and complexity

TPS

DSS, MIS

KBS

WBS

IS


Computer Based Systems Tree

MIS

DSS

EES*

ESS

ES

EIS

TPS

OAS

Figure 1.8: CBIS Tree (Sajja & Patel 1995)

1990

1970

1950

Hardware Base/Technology

Users Requirement

IS

Intelligent Systems: 21st Century Challenge

EES:Executive Expert System, which is hybridization of Expert System , Executive Information System and Decision Support System.

S/W Resources


Structure of KBS

Knowledge Base

Inference Engine

User Interface

Explanation/ Reasoning

Self Learning

Provides explanation

and reasoning facilitates

Knowledge base is a repository of domain knowledge and meta

knowledge. Inference Engine is a software

program, which infers the knowledge available in the

knowledge base

Friendly interface to

users working in their native language

Enriches the system with self learning capabilities


Categories of the KBS

According to Tuthill & Levy (1991), KBS can be mainly classified into 5 types:

Expert SystemsThe Expert Systems (ES) are the most popular and historically pioneer knowledge based systems, which replace one/more experts for problem solving.

Linked Systems The Hypermedia systems like hyper-text, hyper-audio, hyper-video are considered as linked knowledge based systems.

CASE Based SystemsThese systems guide in information/intelligent systems’ development for better quality and effectiveness.

Database in conjunction with an Intelligent User InterfaceAn intelligent user interface can enhance the use of the content available in the traditional format.

Intelligent Tutoring SystemsThe knowledge based systems are also used to train and guide the different level of students, trainers and practitioners in specific area. These systems are also useful to evaluate students’ skills, prepare documentation of subject material and manage the question bank for the subject.


Major Advantages of KBS

• Increased effectiveness with efficiency

• Documentation of knowledge for future use

• Add powers of self learning

• Provides justifications for the decisions made

• Deals with partial and uncertain information

• Friendly interface


Difficulties with the KBS

• Nature of knowledge • Large Size of knowledge base• Slow Learning and Execution • Little methodological support from typical life cycle

models• Acquisition of knowledge• Representation of knowledge


KBS Applications

HealthDevelopment

PhysicalDevelopment

EconomicalDevelopment

SocialDevelopment

NR

HRLA

NR: Natural ResourcesHR: Human ResourcesLA: Live stock and Agricultural Resources

Physical CommunicationPlanning & AdministrationForestry, Energy, Agriculture etc.

HealthNutrition, SanitationCommunity Health etc.

EconomicalSmall Scale IndustryAgri-Business & Co-operativeetc.

SocialEducation & TrainingSocial Awareness Programme etc.


Technology and Education

Technology Education

Technology helps in learning

Education helps in development of technology


Objectives of Educational SolutionDifferent Model like Class room education, Distance learning

and Virtual learning / E-Learning etc. have some common objectives as follows:

• Support learning objectives and goals• Facility to publish, update and access learning material and

announcements• Friendly interface for non-computer professionals and

students for communication• Evaluation of learners and feedback mechanism• Administrative and documentation support• Meets standards and security aspects


Content Service

Technology

· Information Retrieval· Assistance· Learning System

Management· Evaluation · Documentation etc.

· Accessibility (Internet)

· User friendliness· Security· Communication· Inference and self

learning etc.

· Domain knowledge

· Supporting databases and documents etc.

Subject Experts

Media developers, Editors, Instructors

Web Designers, Technical Experts


Symbolic KBS: Some Examples

Parichay: Adult Literacy System for Leaning Gujarati Language

This is a Single PC based system where knowledge based contains set of rules in if…then…else form.

This system has been developed as an agent to help adults to learn regional language, Gujarati.


Some results from ‘Parichay’The system gives training to adult users in multi media to speak and write Gujarati alphabets, words, sentences and numbers.

The package of ‘parichay’ is accommodated in CD with auto-run facility.

The touch screen facility helps even an illiterate person to identify icons and choose appropriate actions.


The frequent continuous development of a letter helps users to see the exact motion to write the letter.

At the end of the full letter generation, the picture representing use of the letter and pronunciation is represented to the user.


With a notepad facility given, user may practice any letter.

That letter written by the user is matched with the correct letter by measuring shapes and angles in terms of percentages.

If the degree of matching is low then user may ask to redraw/rewrite the letter.


Limitations of the System

• ‘Parichay’ is limited to single user system only.

• It can be used only for elementary Gujarati learning (reading and writing) such as simple alphabets, numbers and sentences.


Multi Agent KBS for e-Learning Accessing

Distributed Databases on Grid

• e-Learning is supported by a knowledge based systems to improve quality.

• e-Learning emphasis on on-line delivery, management and learning of educational material.

• The following aspects are given importance for such learning:• Easy access of material in user friendly way• Anytime and anywhere learning• Better control and administration of material and users• Quick results and reporting


• System considers different databases which may be available in distributed fashion.

• At many places the learning material and supporting information like students, courses and infrastructure are available in electronic form.

• The idea is to access the available data sources in knowledge based way.

• e-Learning is a big job encompasses different activities hence multiple independent agents have been considered.


Architecture of the system

Users

Experts

Use

r Inte

rface

Ag

ent

Agents

Learning Mgt.

Drills and Quizzes

Explanation

Semantic Search

E-mail & Chat

Resource Management

Question/Answer

Tutorial Path

Documentation

Distrib

uted

D

atabases

Local Data-Bases

Resources

Knowledge Mgt.

Meta knowledge

Conceptual system

Content knowledge

Learner’s ontology

Mail

Documents

Knowledge Discovery

Knowledge Utilization

Knowledge Management


Communication between Agents

• Agents developed here are communicating with a tool named KQML.

• Knowledge based Query Management Language.

(register

: sender agent_Lerning_Mgt

: receiver agent_Tutorail-Path

: reply-with message

: language common_language

: ontology common_ontology

: content “content.data”

)

Action intended for the message

Agents name sharing message

Action intended for the message

Context-specific information describing the specifics of this message

Ontology of both the agents

Language of both agents


Some results form the System


Some results from the System






New architecture on Grid Environment

Future extension

Users

Experts

Use

r Inte

rface

Ag

ent

Agents

Learning Mgt.

Drills and Quizzes

Explanation

Semantic Search

E-mail & Chat

Resource Management

Question/Answer

Tutorial Path

Documentation

Intern

et

Grid

Mid

dlew

are Services

Resource Management

(Grid Resource Allocation

Protocol-GRAM)

and

Grid FTP Replica-LocationServices

Information Discovery Services

Security Services

Distributed databases

Middleware Services and

Protocols

Local Data-Bases

Resources

Knowledge Mgt.

Meta knowledge

Conceptual system

Content knowledge

Learner’s ontology

Mail

Documents

Knowledge Discovery

Knowledge UtilizationKnowledge Management


Towards reusable component library logic

• Learning Object Repository (LOR)


Multi-tier KBS Accessing LOR through Fuzzy XML


Neural Nets

Knowledge Representation

Fuzzy Logic

Trainability

Implicit, the system cannot be easily interpreted or modified (-)

Trains itself by learning from data sets (+++)

Explicit, verification and optimization easy and efficient (+++)

None, you have to define everything explicitly (-)

Get “best of both worlds”:Explicit Knowledge Representation from Fuzzy Logic with Training Algorithms from Neural Nets

Combining Neural and Fuzzy


Neuro-fuzzy System for Course Selection

• Critical decision + limited time period • Parents and students are not exposed to the

opportunities though educated• All alternatives are not available at one place• Continuously changing data• Changing job opportunities• Too many choices Vs. shortfall in specific stream

industry gap imbalance in trained personnel


Current scenario

• Available systems: • Local with limited scope,

• biased and

• manual systems are available

• Static information system

• Work on Database and explicit documentation required

• Lacks knowledge orientation

• No justification of the decisions

• No self learning about new opportunities and courses

‘Course Selector’, University of Edinburg, UK

‘Course Advisor Expert System’ is developed at the Griffith University


Requirements• Timely decision

• Uniform Information availability at one place

• Management of large amount of data

• Effective and knowledge oriented personalized decision support

• Justification (explanation and reasoning)

• Adaptive to new courses

• Friendly user interface working in natural fashion


Users

• Students• Parents• Institutes and Universities • Professional consultants, if allowed• Researchers and policy makers


Critical Parameter categories• Institute and course information:

• Institute name, registration number, preliminary information, courses, seats, reservation, placement, history etc.

• Users academic qualification/marks:• Name, location, degree/exam, marks, year, board etc.

• Users personal preferences:• Institute & course preference, hostel accommodation,

foreign chances etc.

• Family background:• Parents business, economical conditions etc.


Methodology

Fu

zzy Interface

Structure of the Neuro-fuzzy System

Fuzzy interface

Linguistic fuzzy

interface

Fuzzy rule base and membership

functions

Workspace

Crisp Normalized

values

Decision support

Users choice and needs

Decision support

Underlaying ANN

P1

P2

P3

P4

Implicit learning

& self learning by ANN

Friendly interface and

Explicit justification, documentatio

n


Students Information Collection:Name, Location, Score, Subject wise marks, Board Name, State information, etc.

Family Background Information: Economical conditions, parents profession etc.

Aptitude and Preference Seeking Questions: Choice of institute, course, homesickness, etc.

Institute list with Courses, seats, accredition, faculty, resources, history, placement, cut-off marks etc.

ANNNormalized Student Info + Reference Ids *

* generated from Institute +Courses + Scheme etc.

Input Layer

Hidden Layers

Output Layer

Array of alternatives in Sorted order with default three best suitable alternatives

Available in Knowledge Base

Collected from User(s) through Input Screens

Fully Connected Feed Forward Multi-Layer

Back-Propagation ANN

Can be changed according to

users demand

Users

PI & PF

Conversion into crisp normalized values by Fuzzy Interface

Fuzzy Interface

User


An Example Prototype

Elective Course Selection system:• Objective: To test feasibility of the proposed project

• Place: Department of Computer Science, S P University

• Tools: .Net 2005 + ANN simulator (JavaNNS)

• Training set: 100 records

• Users :Final Year MCA Students at the S P University (220 app.)


Interface Screen to collect training data


Fuzzification of the parameters resulting in normalized values…Linguistic Distance :[very far way, far away, away, near, very near etc.]

Distance :[ 50, 100, 150 km]

0.1 0.4 0.6 0.8 1.0 thousand KM

Linguistic variable ‘Distance’

1.0

0.5

0

Membership degree

Very Near

Near

Away

Far Away

Too Far


Network Structure

Input Layer

Output Layer

Hidden Layers

Availability of expertise

Availability of hardware/based technology

Content /length of the course

Degree of assistance required[[[

Knowledge level required for the course/ depth of the course

Market trend towards technology/course

Personal interest

Success history if any (last few years result in%)

Time taken to complete (revision)

Bio-Informatics

suggested decision for Current Trends

Wireless Tech.


Advantages• Quick and effective decision support• Ease of cloning and documentation • Knowledge Based

• Dual advantages through explicit and implicit representation

• Self learning• Manages vague parameters in fuzzy way• Explanation and reasoning• Management of large amount of data & dynamic

• Object oriented• Platform independent• Easy to use with fuzzy interface


Fuzzy-genetic System for Evolving Rule Bases to Measure Multiple Intelligence

• Fuzzy genetic hybridization

• The paper will be presented by Ms. Kunjal Mankad, ISTAR
