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Table of contents
CHAPTER 1 INTRODUCTION
2H1.1 BACKGROUND &MOTIVATION
3H1.2 KEY ISSUES CONSIDERED IN THIS DISSERTATION
4H1.3 OBJECTIVES
5H1.4 THESIS ORGANIZATION
CHAPTER 2 STATE OF THE ART: E-EDUCATION, PEDAGOGIC THEORIES & MAS
7H2.1 INTRODUCTION
8H2.2 LITERATURE ON E-EDUCATION
9H2.2.1 Definition
1 0H2.2.2 Evolution of e-Education
1 1H2.2.3 Advantages & Disadvantages
1 2H2.2.4 Trend of e-Education
1 3H2.3 COGNITIVE THEORY IN EDUCATION
1 4H2.3.1 Cognitive Process
1 5H2.3.2 Taxonomy of Cognitive Domain
1 6H2.3.3 Learning Style
1 7H2.3.4 Constructivism
1 8H2.4 INTELLIGENT TUTORING SYSTEM
1 9H2.5 MULTI-AGENT SYSTEM
2 0H2.5.1 Background
2 1H2.5.2 Agent
2 2H2.5.3 Multi-agent System
2 3H2.5.4 Mobile Agent
2 4H2.5.5 FIPA Standard
2 5H2.6 SUMMARY
HCHAPTER 3 ARCHITECTURE OF MAGE
2 7H3.1 INTRODUCTION TO E-EDUCATION REFERENCE MODEL
2 8H3.1.1 A recommended e-Education reference model
2 9H3.1.2 Leaning Technology Systems Architecture (LTSA) of IEEE LTSC
3 0H3.1.3 LTSA Overview
3 1H3.1.4 Stakeholder perspectives
3 2H3.2 FRAMEWORK OF MULTI-AGENT E-EDUCATION SYSTEM (MAGE) 1 46H51
3 3H3.2.1 classifications of agents in MAGE
3 4H3.3 LEARNING SCENARIOS
3 5H3.3.1 Scenario 1—Agent enabled intelligent tutoring system (AITS)
3 6H3.3.2 Learning scenario 2—Teacher intervened learning
3 7H3.4 SUMMARY
CHAPTER 4 MAS BASED COURSE & EEO AUTHORING
3 9H4.1 INTRODUCTION
4 0H4.2 DESIGN PRINCIPLAND CONCEPTMODEL
4 1H4.3 LEARNINGOBJECT DESIGN
4 2H4.3.1 DEFINITION LEARNING OBJECT
4 3H4.3.2 STRUCTRUE MODEL OF LO
4 4H4.3.3 EXTENSION OF LO METADATA TO ENHANCE ADAPTIVIEY
4 5H4.3.4 PACKAGE MODEL
4 6H4.4 ARCHITECTURE STRUCTURE
4 7H4.5 COURSEAUTHORINGSCENARIOS
4 8H4.5.1 SERCHING LEARNING OBJECTS
4 9H4.5.2 SUBSCRIPTION
5 0H4.5.3 NEGOCIATON WITH LO CREATORS
5 1H4.6 SUMMARY
CHAPTER 5 MAS BASED ADAPTIVE & ACTIVE LEARNING FRAMEWORK 1 66H77
5 3H5.1 INTRODUCTION
5 4H5.2 DOMAIN MODELING
5 5H5.3 ADAPTIVE INDIVIDUAL LEARNING
5 6H5.3.1 agent Architecute
5 7H5.3.2 AUTOMATICE LEARNING PATH GENERATION
5 8H5.4 ADAPTIVE COLLECTIVE LEARNING
5 9H5.4.1 INTRODUCTION
6 0H5.4.2 peer help MODELING
6 1H5.5 LEARNER GROUPFORMINGMODELING
6 2H5.5.1 INTRODUCTION
6 3H5.6 SUMMARY
CHAPTER 6 AN INNOVATIVE E- ASSESSMENT APPROACH: MOBILE AGENT BASED PARADIGM
6 5H6.1 INTRODUCTION
6 6H6.2 OVERALL FUNCTION STRUCTRUE
6 7H6.3 PROTOTYPE DESIGN OF GENETIC ALGORITHM BASED MAS TEST
GENERATION SYSTEM (GAMASTG)
6 8H6.3.1 introduction
6 9H6.3.2 genetic algorithm
7 0H6.3.3 test ontology design
7 1H6.3.4 design of ga
7 2H6.3.5 Architecture
7 3H6.3.6 state Chart
7 4H6.3.7 interactive model
7 5H6.4 DESIGN OF TEST DELIVERY
7 6H6.5 DESIGN OF EVALUATION &RESULT PUBLISHING
7 7H6.6 SUMMARY
CHAPTER 7 MAS IMPLEMENTATION & SIMULATION BASED ON JADE FRAMEWORK
7 9H7.1 INTRODUCTION
8 0H7.2 JADE
8 1H7.2.1 introduction
8 2H7.2.2 jade architecure
8 3H7.3 IMPLEMENTATION OF GAMASGT
8 4H7.4 IMPLEMENTATION OF TEST ONTOLOGY WHITH PROTEGE
8 5H7.4.1 7.4.1 design of agent behavior model
8 6H7.4.2 7.4.2 agent implementation
8 7H7.4.3 7.4.2.1 generic agent internal architecture
8 8H7.4.4 7.4.2.2 Implementation of Teacher agent
8 9H7.4.5 7.4.2.3 implementation of test generaration service agent (TGSAgent) 2 03H138
9 0H7.4.6 7.4.2.4 implementation of GActrlagent
9 1H7.4.7 7.4.2.5 TPagent
9 2H7.4.8 7.4.3 platform implementation
9 3H7.4.9 7.4.3.1 simulation
9 4H7.5 IMPLEMENTATION OF LEARNER MODEL AGENT
9 5H7.5.1 Protocoal implementation
9 6H7.5.2 Scenario
9 7H7.6 IMPLEMENTATON OF PEER HELP SYSTEM
9 8H7.7 SUMMARY
CHAPTER 8 CONCLUSIONS AND PERSPECTIVES
1 00H8.1 CONCLUSIONS
1 01H8.2 PERSPECTIVES
1 02HACKNOLEDGEMENT
1 03HREFERENCES
