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Fuzzy Logic-Based Modeling in Collaborative and Blended Learning

Book Description

Technology has dramatically changed the way in which knowledge is shared within and outside of traditional classroom settings. The application of fuzzy logic to new forms of technology-centered education has presented new opportunities for analyzing and modeling learner behavior. Fuzzy Logic-Based Modeling in Collaborative and Blended Learning explores the application of the fuzzy set theory to educational settings in order to analyze the learning process, gauge student feedback, and enable quality learning outcomes. Focusing on educational data analysis and modeling in collaborative and blended learning environments, this publication is an essential reference source for educators, researchers, educational administrators and designers, and IT specialists. This premier reference monograph presents key research on educational data analysis and modeling through the integration of research on advanced modeling techniques, educational technologies, fuzzy concept maps, hybrid modeling, neuro-fuzzy learning management systems, and quality of interaction.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Dedication
  6. Foreword
  7. Preface
    1. THE CHALLENGES
    2. ROOTING
    3. SEARCHING FOR A SOLUTION
    4. THE UNIT OF THE BOOK
    5. AUDIENCE
    6. FINAL NOTES
    7. REFERENCES
  8. Acknowledgment
  9. Section 1: Educational-ICT Background
    1. Chapter 1: Placing the Framework within the Educational Context
      1. ABSTRACT
      2. THE GENERIC SPACE
      3. FROM A CLOSER VIEW
      4. FOSTERING ICT-BASED EDUCATIONAL INNOVATION
      5. CONCLUDING THE OPENING
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    2. Chapter 2: Understanding Online Learning Environments (OLEs)
      1. ABSTRACT
      2. INTRODUCTION
      3. ONLINE LEARNING
      4. ONLINE TEACHING
      5. ONLINE INTERACTION
      6. ONLINE TECHNOLOGY
      7. COMMON MODELS FOR ONLINE LEARNING ENVIRONMENTS
      8. CURRENT AND UPCOMING TRENDS
      9. OVERALL PERSPECTIVE
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Computer-Supported Collaborative Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. COLLABORATIVE LEARNING
      4. COMPUTER-SUPPORTED COLLABORATIVE LEARNING (CSCL)
      5. ADAPTIVE AND INTELLIGENT SYSTEMS FOR CSCL
      6. EXAMPLES OF CSCL ENVIRONMENTS
      7. THINKING OUT OF THE BOX
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Towards Blending Potentialities within a Learning Management System
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCEPTUAL BLENDING AND BLENDED-LEARNING DEFINITIONS
      4. PEDAGOGICAL PLANNING IN B-LEARNING CONTEXTS
      5. INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) USE
      6. COURSE MANAGEMENT SYSTEM (CMS)
      7. LEARNING MANAGEMENT SYSTEM (LMS)
      8. LMS TRENDS
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    5. Chapter 5: Personal/Cloud Learning Environment, Semantic Web 3.0, and Ontologies
      1. ABSTRACT
      2. INTRODUCTION
      3. BRINGING FLEXIBILITY INTO LMS VIA PLE BRIDGING
      4. CLOUD COMPUTING (CC) IN HIGHER EDUCATION (HE)
      5. SEMANTIC WEB 3.0
      6. ONTOLOGIES
      7. GLOBAL TRENDS
      8. CONCLUDING REMARKS
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
  10. Section 2: Fuzzy Logic: Definitions and Inference Systems
    1. Chapter 6: Placing the Framework within the Fuzzy Logic World
      1. ABSTRACT
      2. FUZZY LOGIC IS NOT FUZZY!
      3. ARISTOTLE VS. ZADEH: A BATTLE FOR (IM)PRECISION AND (UN)CERTAINTY
      4. FUZZY SET THEORY VS. PROBABILITY THEORY
      5. FUZZY LOGIC OFFERINGS
      6. FUZZY SYSTEMS
      7. IT IS A FUZZY WORLD!
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    2. Chapter 7: Fuzzy Logic Essentials
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY NUMBERS (FNS) OR MEMBERSHIP FUNCTIONS (MFS)
      4. ARITHMETIC WITH FUZZY NUMBERS
      5. FUZZY SETS
      6. LINGUISTIC VARIABLES, LINGUISTIC MODIFIERS, AND PROPOSITIONS
      7. INDICATIVE FL APPLICATION SPACES
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
      11. ENDNOTE
    3. Chapter 8: Fuzzy Logic-Based Inference Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY INFERENCE SYSTEM (FIS)
      4. ADAPTIVE NETWORKS-BASED FIS (ANFIS)
      5. INTUITIONISTIC FIS (IFIS)
      6. FUZZY COGNITIVE MAP (FCM)
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  11. Section 3: FIS-Based Modeling Approaches in Learning
    1. Chapter 9: Connecting the Educational and Fuzzy Worlds
      1. ABSTRACT
      2. KNOWLEDGE REPRESENTATION THROUGH FUZZY…GLASSES
      3. FUZZY INFERENCE SYSTEM (FIS) IN EDUCATION
      4. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) IN EDUCATION
      5. INTUITIONISTIC FUZZY INFERENCE SYSTEM (IFIS) IN EDUCATION
      6. FUZZY COGNITIVE MAP (FCM) IN EDUCATION
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 10: FIS-Based Collaborative/Metacognitive Data Modeling
      1. ABSTRACT
      2. INTRODUCTION
      3. QUALITY OF COLLABORATION (QoC) WITHIN CSCL
      4. THE INSTRUCTIONAL DESIGN (ID)
      5. MODELING COLLABORATIVE/METACOGNITIVE DATA: THE C/M-FIS MODEL
      6. EVALUATION PARADIGMS
      7. KEEP BALANCED!
      8. OVERALL PERSPECTIVE
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    3. Chapter 11: ANFIS-Based Collaborative/Metacognitive Data Modeling
      1. ABSTRACT
      2. INTRODUCTION
      3. QoC IN THE VIEW OF THE C/M-ANFIS MODEL
      4. THE C/M-ANFIS MODEL
      5. EVALUATION PARADIGMS
      6. OVERALL PERSPECTIVE
      7. C/M-ANFIS AS A CHANGE REINFORCEMENT FACTOR
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 12: FIS/IFIS Modeling in Professional and Collaborative Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. SYSTEMIC APPROACH
      4. INSTRUCTIONAL DESIGN
      5. FIS/IFIS MODELING
      6. EXPERIMENTAL PARADIGMS
      7. DISCUSSION
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    5. Chapter 13: Embracing Macro-, Meso-, and Micro-Levels of Analysis of FIS-Based LMS Users' Quality of Interaction
      1. ABSTRACT
      2. INTRODUCTION
      3. THE FUZZYQOI MODEL FUNDAMENTALS
      4. EXAMINING MACRO-, MESO-, AND MICRO-LEVELS OF ANALYSIS
      5. PUTTING ALL PIECES TOGETHER: PRACTICAL IMPLICATIONS
      6. IN CLOSING
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    6. Chapter 14: FCM-Based Modeling of LMS Users' Quality of Interaction
      1. ABSTRACT
      2. INTRODUCTION
      3. THE RATIONALE BEHIND THE FCM-QOI MODEL
      4. THE FCM-QoI MODEL
      5. EXPERIMENTAL VALIDATION SET-UP
      6. EXPERIMENTAL EVALUATION SCENARIOS RESULTS
      7. OVERALL PERSPECTIVE
      8. FCM-QOI MODEL IMPLICATIONS
      9. FCM-QoI MODEL EXTENSIONS
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
      13. ENDNOTES
  12. Section 4: Overall Perspective
    1. Chapter 15: Towards a Hybrid Modeling
      1. ABSTRACT
      2. INTRODUCTION
      3. THE HYBRID MODEL-QoC/QoI IN SEMANTIC WEB 3.0
      4. THE i-TREASURES CASE STUDY
      5. FINAL CONSIDERATIONS
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
    2. Chapter 16: Concluding Remarks and Probing Further
      1. ABSTRACT
      2. AT A GLIMPSE
      3. A VISION FOR THE FUTURE
      4. THE ULTIMATE TASTE
      5. BOOK COPESTONE
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
  13. Compilation of References
  14. About the Authors