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

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Presentation on theme: "Application of Knowledge Based Systems in Education Dr Priti Srinivas Sajja Department of Computer Science Sardar Patel University Vallabh Vidyanagar,"— Presentation transcript:

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

2 Dr. Priti Srinivas Sajja 6-7 February, 2009 Introduction and Contact Information Speaker: Dr Priti Srinivas Sajja Communication: Email : priti_sajja@yahoo.com 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

3 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

4 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

5 Dr. Priti Srinivas Sajja 6-7 February, 2009 Knowledge Based Systems K Knowledge Based Systems (KBS) are Productive Artificial Intelligence Tools working in a narrow domain.

6 Dr. Priti Srinivas Sajja 6-7 February, 2009 How Knowledge is organized? Volume Complexity & Sophistication Wisdom(experience) Knowledge(synthesis) Information(analysis) Data Data Pyramid Source: Tuthill & Leavy, modified

7 Dr. Priti Srinivas Sajja 6-7 February, 2009 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)

8 Dr. Priti Srinivas Sajja 6-7 February, 2009 Information Processed Data Smith weight is 50 Kg. Information has usually got some meaning and purpose

9 Dr. Priti Srinivas Sajja 6-7 February, 2009 Knowledge Information can be processed further with the operations such as Synthesis Filtering Comparing etc. to get generalized knowledge

10 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

11 Dr. Priti Srinivas Sajja 6-7 February, 2009 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) VolumeSophistication and complexity TPS DSS, MIS KBS WBS IS

12 Dr. Priti Srinivas Sajja 6-7 February, 2009 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: 21 st Century Challenge EES: Executive Expert System, which is hybridization of Expert System, Executive Information System and Decision Support System. S/W Resources

13 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

14 Dr. Priti Srinivas Sajja 6-7 February, 2009 Categories of the KBS 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.

15 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

16 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

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

18 Dr. Priti Srinivas Sajja 6-7 February, 2009 Technology and Education TechnologyEducation Technology helps in learning Education helps in development of technology

19 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

20 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

21 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

22 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

23 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

24 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

25 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

26 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

27 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

28 Dr. Priti Srinivas Sajja 6-7 February, 2009 Architecture of the system Users Experts User Interface Agent Agents Learning Mgt. Drills and Quizzes Explanation Semantic Search E-mail & 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

29 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

30 Dr. Priti Srinivas Sajja 6-7 February, 2009 Some results form the System

31 Dr. Priti Srinivas Sajja 6-7 February, 2009 Some results from the System

32 Dr. Priti Srinivas Sajja 6-7 February, 2009 Some results from the System

33 Dr. Priti Srinivas Sajja 6-7 February, 2009 Some results from the System

34 Dr. Priti Srinivas Sajja 6-7 February, 2009 New architecture on Grid Environment Future extension Users Experts User Interface Agent Agents Learning Mgt. Drills and Quizzes Explanation Semantic Search E-mail & 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

35 Dr. Priti Srinivas Sajja 6-7 February, 2009 Towards reusable component library logic Learning Object Repository (LOR)

36 Dr. Priti Srinivas Sajja 6-7 February, 2009 Multi-tier KBS Accessing LOR through Fuzzy XML

37 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

38 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

39 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

40 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

41 Dr. Priti Srinivas Sajja 6-7 February, 2009 Users Students Parents Institutes and Universities Professional consultants, if allowed Researchers and policy makers

42 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.

43 Dr. Priti Srinivas Sajja 6-7 February, 2009 Methodology Fuzzy 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 P1P2P3P4P1P2P3P4 Implicit learning & self learning by ANN Friendly interface and Explicit justification, documentatio n

44 Dr. Priti Srinivas Sajja 6-7 February, 2009 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. ANN Normalized Student Info + Reference Ids * * generated from Institute +Courses + Scheme etc. 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

45 Dr. Priti Srinivas Sajja 6-7 February, 2009 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.)

46 Dr. Priti Srinivas Sajja 6-7 February, 2009 Interface Screen to collect training data

47 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

48 Dr. Priti Srinivas Sajja 6-7 February, 2009 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 suggeste d decision for Current Trends Wireless Tech.

49 Dr. Priti Srinivas Sajja 6-7 February, 2009 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

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

51 Dr. Priti Srinivas Sajja 6-7 February, 2009


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