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E-LEARNING FOR RURAL SCHOOLS • NAME: SIVAKUMAR.V REG NO: 1036282 DEPT: M.E SOFTWARE ENGINEERING EASWARI ENGINEERING COLLEGE Department of Information Technology
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Page 1: 0th review(siva)

E-LEARNING FOR RURAL SCHOOLS

• NAME: SIVAKUMAR.V

• REG NO: 1036282

• DEPT: M.E SOFTWARE ENGINEERING

EASWARI ENGINEERING COLLEGE Department of Information Technology

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PROJECT CO-ORDINATOR:

Dr.K.Kathiravan M.Tech.,Ph.D

Head of the Department

PROJECT SUPERVISOR:

Mrs.A.L.Agasta Adline M.E.,(Ph.D)

Asst.Professor

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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.

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OBJECTIVES

To impart e-learning in Rural Schools for providing enhanced knowledge delivery.

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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.

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BROAD AREA OF THE PROJECT ARTIFICAL INTELLIGENCE

• Knowledge Acquisition– Subject matter expert

• Knowledge Representation– Eg. creation of resources

• Knowledge Encoding – Eg. creation of if-then structures

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• Need for E-LearningThe purpose we need e-learning to get virtual education.

Learning occurs through connections with other learners

Learning is based on conversation and interaction

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AREA OF SPECIFICTION:

•ARTIFICAL INTELLIGENCE

•KNOWLEDGE MINING

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LITERATURE SURVEY

• SMART LEARNING

• PROBLEM BASED LEARNING • ADAPTIVE E-LEARNING

• PERSONAL E-LEARNING

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• ONTOLOGY EXTRACTION FOR KNOWLEDGE REUSE:THE E-LEARNING PERSPECTIVE.

• INTEGRATING EMBEDDED COMPUTING SYSTEMS INTO HIGH SCHOOL AND EARLY UNDERGRADUTE EDUCATION

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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

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THANK

YOU