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

2. Introduction and Contact Information
Speaker: Dr Priti Srinivas Sajja
Communication:
Mobile :
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
6-7 February, 2009

3. Lecture Plan Knowledge Based Systems KBS in Education
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
6-7 February, 2009

4. 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
6-7 February, 2009

5. Knowledge Based Systems
Knowledge Based Systems (KBS) are Productive Artificial Intelligence Tools working in a narrow domain.
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6. How Knowledge is organized?
Volume
Complexity &
Sophistication
Wisdom(experience)
Knowledge(synthesis)
Information(analysis)
Data
Data Pyramid
Source: Tuthill & Leavy, modified
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7. 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)
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8. Information Processed Data Smith weight is 50 Kg.
Information has usually got some meaning and purpose
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9. Knowledge
Information can be processed further with the operations such as
Synthesis
Filtering
Comparing etc.
to get generalized knowledge
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10. 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.
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11. 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
6-7 February, 2009

12. 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
6-7 February, 2009

13. Structure of KBS Knowledge Base Inference Engine User Interface
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
Enriches the system with self learning capabilities
Knowledge Base
Explanation/ Reasoning
Self Learning
Inference Engine
Friendly interface to users working in their native language
Provides explanation and reasoning facilitates
User Interface
6-7 February, 2009

14. Categories of the KBS Expert Systems
According to Tuthill & Levy (1991), KBS can be mainly classified into 5 types:
Expert Systems
The 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 Systems
These systems guide in information/intelligent systems’ development for better quality and effectiveness.
Database in conjunction with an Intelligent User Interface
An intelligent user interface can enhance the use of the content available in the traditional format.
Intelligent Tutoring Systems
The 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.
6-7 February, 2009

15. 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
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16. 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
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17. KBS Applications Economical Small Scale Industry
Agri-Business & Co-operative
etc.
Economical
Development
Social
Education & Training
Social Awareness Programme etc.
Physical
Communication
Planning & Administration
Forestry, Energy, Agriculture etc. NR HR LA
Physical
Development
Social
Development
NR: Natural Resources
HR: Human Resources
LA: Live stock and
Agricultural Resources
Health
Nutrition, Sanitation
Community Health etc.
Health
Development
6-7 February, 2009

18. Technology and Education
Technology helps in learning
Technology
Education
Fruits of technology should reach to every field and enlight every single aspect of life. Education is one such area where technology can directly help at different level to different user groups. Technological advancements are utilised from primary school to research education for traditional education. Non-standard methods and techniques of education like e-Learning, virtual classroom etc. are also empowered by the technological advancement.
In todays lecture we will explore an advanced information system called KBS
Education helps in development of technology
6-7 February, 2009

19. Objectives of Educational Solution
Different 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
6-7 February, 2009

20. 6-7 February, 2009 Accessibility (Internet) User friendliness Security
Communication
Inference and self learning etc.
Web Designers, Technical Experts
Content
Service
Technology
Domain knowledge
Supporting databases and documents etc.
Information Retrieval
Assistance
Learning System Management
Evaluation
Documentation etc.
Media developers, Editors, Instructors
Subject Experts
6-7 February, 2009

21. 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.
6-7 February, 2009

22. 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.
6-7 February, 2009

23. 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.
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24. 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.
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25. 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.
6-7 February, 2009

26. 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
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27. 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.
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28. Architecture of the system
Users
Experts
User Interface Agent
Agents
Learning Mgt.
Drills and Quizzes
Explanation
Semantic Search
& Chat
Resource Management
Question/Answer
Tutorial Path
Documentation
Distributed Databases
Local Data-Bases
Resources
Knowledge Mgt.
Meta knowledge
Conceptual system
Content knowledge
Learner’s ontology
Mail
Documents
Knowledge Discovery
Knowledge Utilization
Knowledge Management
6-7 February, 2009

29. 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
Context-specific information describing the specifics of this message
Ontology of both the agents
Language of both agents
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30. Some results form the System
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31. Some results from the System
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32. Some results from the System
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33. Some results from the System
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34. New architecture on Grid Environment Future extension
Users
Experts
User Interface Agent
Agents
Learning Mgt.
Drills and Quizzes
Explanation
Semantic Search
& Chat
Resource Management
Question/Answer
Tutorial Path
Documentation
Internet
Grid Middleware Services
Resource Management (Grid Resource Allocation Protocol-GRAM)
and
Grid FTP Replica-Location
Services
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 Utilization
Knowledge Management
6-7 February, 2009

35. Towards reusable component library logic
Learning Object Repository (LOR)
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36. Multi-tier KBS Accessing LOR through Fuzzy XML
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37. Knowledge Representation
Combining Neural and Fuzzy
Neural Nets
Fuzzy Logic
Knowledge Representation
Implicit, the system cannot be easily interpreted or modified (-)
Explicit, verification and optimization easy and efficient (+++)
Trainability
Trains itself by learning from data sets (+++)
None, you have to define everything explicitly (-)
Get “best of both worlds”: Explicit Knowledge Representation from Fuzzy Logic with Training Algorithms from Neural Nets
6-7 February, 2009

38. 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
6-7 February, 2009

39. Current scenario Available systems: Static information system
‘Course Selector’, University of Edinburg, UK
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 Advisor Expert System’ is developed at the Griffith University
6-7 February, 2009

40. 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
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41. Users Students Parents Institutes and Universities
Professional consultants, if allowed
Researchers and policy makers
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42. 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.
6-7 February, 2009

43. Methodology Implicit learning & self learning by ANN
Fuzzy Interface
Friendly interface and
Explicit justification, documentation
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
Underlaying ANN P1 P2 P3 P4
6-7 February, 2009

44. Normalized Student Info + Reference Ids *
Collected from User(s) through Input Screens
User
Available in Knowledge Base
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.
PI & PF
Conversion into crisp normalized values by Fuzzy Interface
ANN
Normalized Student Info Reference Ids *
* generated from Institute +Courses + Scheme etc.
Input Layer
Hidden Layers
Output Layer
Fully Connected Feed Forward Multi-Layer Back-Propagation ANN
Fuzzy Interface
Can be changed according to users demand
Array of alternatives in Sorted order with default three best suitable alternatives
6-7 February, 2009
Users

45. Elective Course Selection system:
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 ANN simulator (JavaNNS)
Training set: 100 records
Users :Final Year MCA Students at the S P University (220 app.)
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46. Interface Screen to collect training data
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47. 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]
thousand KM
Linguistic variable ‘Distance’
1.0
0.5
Membership degree
Very Near
Near
Away
Far Away
Too Far
6-7 February, 2009

48. Network Structure Bio-Informatics
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.
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49. 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
6-7 February, 2009

50. Fuzzy genetic hybridization
Fuzzy-genetic System for Evolving Rule Bases to Measure Multiple Intelligence
Fuzzy genetic hybridization
The paper will be presented by Ms. Kunjal Mankad, ISTAR
6-7 February, 2009

51. Thanks
6-7 February, 2009