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Application of Knowledge Based Systems in Education

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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 : 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. 6-7 February, 2009

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

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) 6-7 February, 2009

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

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

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. 6-7 February, 2009

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 6-7 February, 2009

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 6-7 February, 2009

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. 6-7 February, 2009

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. 6-7 February, 2009

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 6-7 February, 2009

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. 6-7 February, 2009

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      “” ) 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 6-7 February, 2009

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) 6-7 February, 2009

36 Multi-tier KBS Accessing LOR through Fuzzy XML
6-7 February, 2009

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 6-7 February, 2009

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

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.) 6-7 February, 2009

46 Interface Screen to collect training data
6-7 February, 2009

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. 6-7 February, 2009

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

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