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    0th review(siva) 0th review(siva) Presentation Transcript

    • E-LEARNING FOR RURAL SCHOOLS
      EASWARI ENGINEERING COLLEGE
      Department of Information Technology
      NAME: SIVAKUMAR.V
      REG NO: 1036282
      DEPT: M.E SOFTWARE ENGINEERING
    • PROJECT CO-ORDINATOR:
      Dr.K.KathiravanM.Tech.,Ph.D
      Head of the Department
      PROJECT SUPERVISOR:
      Mrs.A.L.AgastaAdline M.E.,(Ph.D)
      Asst.Professor
    • INTRODUCTION: E- Learning
      • What is E-Learning ?
      • The delivery of a learning , training or education program by electronic means.
      • Network-enabled transfer of skills and knowledge.
    • OBJECTIVES
      To impart e-learning in Rural Schools for providing enhanced knowledge delivery.
    • Student Behavior and Interaction Patterns With anLMS as Motivation Predictors in E-Learning Settings
      Student motivation is an important factor for the successful
      completion of an e-learning course.
      Detecting motivational problems for particular students at an early
      stage of a course opens the door for instructors to be able to provide additional motivating activities for these students.
      This paper analyzes how the behavior patterns in the interaction of each particular student with the contents and services in a learning management system (LMS)
      used to predict student motivation and if this student motivation
      can be used to predict the successful completion of an e-learning
      course.
      The interactions of 180 students of six different universities
      taking a course in three consecutive years are analyzed.
    • BROAD AREA OF THE PROJECTARTIFICAL INTELLIGENCE
      Knowledge Acquisition
      Subject matter expert
      Knowledge Representation
      Eg. creation of resources
      Knowledge Encoding
      Eg. creation of if-then structures
    • Need for E-Learning
      The purpose we need e-learning to get virtual education.
      Learning occurs through connections with other learners
      Learning is based on conversation and interaction
    • AREA OF SPECIFICTION:
      • ARTIFICAL INTELLIGENCE
      • KNOWLEDGE MINING
    • LITERATURE SURVEY
      SMART LEARNING
      PROBLEM BASED LEARNING
      ADAPTIVE E-LEARNING
      PERSONAL E-LEARNING
      • ONTOLOGY EXTRACTION FOR KNOWLEDGE REUSE:THE E-LEARNING PERSPECTIVE.
      INTEGRATING EMBEDDED COMPUTING SYSTEMS INTO HIGH SCHOOL AND EARLY UNDERGRADUTE EDUCATION
    • REFERENCE
      http://en.wikipedia.org/wiki/E-learning
      http://www.eco.utexas.edu/faculty/Norman/long.extra/Info.S98/Exp/intro.html
      www.ieee.org
      E-learning Strategies
      How to get implementation and delivery right first time
      Don Morrison
    • THANK YOU