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Fuzzy Logic-Based Modeling in Collaborative and Blended Learning

Fuzzy Logic-Based Modeling in Collaborative and Blended Learning

Sofia J. Hadjileontiadou (Hellenic Open University, Greece), Sofia B. Dias (Universidade de Lisboa, Portugal), José A. Diniz (Universidade de Lisboa, Portugal) and Leontios J. Hadjileontiadis (Aristotle University of Thessaloniki, Greece)
Copyright: © 2015 |Pages: 519
ISBN13: 9781466687059|ISBN10: 1466687053|EISBN13: 9781466687066
DOI: 10.4018/978-1-4666-8705-9
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MLA

Hadjileontiadou, Sofia J., Sofia B. Dias, José A. Diniz, and Leontios J. Hadjileontiadis. "Fuzzy Logic-Based Modeling in Collaborative and Blended Learning." IGI Global, 2015. 1-519. Web. 27 Mar. 2020. doi:10.4018/978-1-4666-8705-9

APA

Hadjileontiadou, S. J., Dias, S. B., Diniz, J. A., & Hadjileontiadis, L. J. (2015). Fuzzy Logic-Based Modeling in Collaborative and Blended Learning (pp. 1-519). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-8705-9

Chicago

Hadjileontiadou, Sofia J., Sofia B. Dias, José A. Diniz, and Leontios J. Hadjileontiadis. "Fuzzy Logic-Based Modeling in Collaborative and Blended Learning." 1-519 (2015), accessed March 27, 2020. doi:10.4018/978-1-4666-8705-9

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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

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Front Materials
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Title Page
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Copyright Page
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Advances in Educational Technologies and Instructional Design (AETID) Book Series
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Dedication
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Foreword
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Preface
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Acknowledgment
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Chapters
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Chapter 1
Educational-ICT Background
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Chapter 1
This chapter introduces the reader to Part I of the book, describing the educational framework where the core ideas of the book best fit. The appropriate background and fundamental concepts are epitomized, in order to surface the...
Placing the Framework within the Educational Context
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Chapter 2
An online learning environment (OLE) is a unique sociocultural context in itself. The aim of this chapter is to look at OLEs from a global point of view, based on well-recognized learning theories, in order to provide a theoretical...
Understanding Online Learning Environments (OLEs)
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Chapter 3
The aim of Computer-Supported Collaborative Learning (CSCL) is to integrate research on collaborative learning with the use of Information and Communication Technologies. From a holistic perspective, this chapter covers the research...
Computer-Supported Collaborative Learning: A Holistic Perspective
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Chapter 4
The emergence of blended (b-)learning approaches clearly highlights a pressing need for higher education institutions to embrace innovation and change. However, the process of (sociocultural) innovation should be driven by people and...
Towards Blending Potentialities within a Learning Management System: Definitions, Issues, and Trends
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Chapter 5
In the era of digital world that we live in, a new vision for learning is required. Learning is essentially personal, sociocultural, distributed, ubiquitous, flexible, dynamic, and complex in nature. There are multiple challenges...
Personal/Cloud Learning Environment, Semantic Web 3.0, and Ontologies
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Fuzzy Logic: Definitions and Inference Systems
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Chapter 6
This chapter introduces the reader to Part II of the book, describing the fuzzy logic framework where the core methodological ideas of the book best fit. The appropriate background and fundamental concepts are epitomized, so to...
Placing the Framework within the Fuzzy Logic World
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Chapter 7
Fuzzy Logic Essentials  (pages 168-208)
This chapter presents the mathematical formulation of the fuzzy logic essentials and sets and serves as a useful background for entering the mathematical expression of the knowledge representation in the fuzzy world. Particular...
Fuzzy Logic Essentials
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Chapter 8
This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world....
Fuzzy Logic-Based Inference Systems
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FIS-Based Modeling Approaches in Learning
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Chapter 9
In recent years, several researchers have proposed many fuzzy inference systems for learners' learning progress inference and evaluation. Fuzzy logic-based knowledge representation provides a functional way that achieves to...
Connecting the Educational and Fuzzy Worlds
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Chapter 10
This chapter presents an instructional design (ID) that facilitates the use of fuzzy logic (FL) systems to model the collaborative and metacognitive data that are logged while a computer-mediated collaboration takes place. The...
FIS-Based Collaborative/Metacognitive Data Modeling
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Chapter 11
Stemming from the approach presented in the previous chapter, this chapter extends the previous modeling concept further, by adopting Adaptive Neuro-Fuzzy Inference System (ANFIS) as the engine to model the collaborative and...
ANFIS-Based Collaborative/Metacognitive Data Modeling
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Chapter 12
In this chapter, the capability of the fuzzy inference systems (FISs) to model and provide evaluations in the educational context is further explored through the merits of the intuitionistic fuzzy inference systems (IFISs). The...
FIS/IFIS Modeling in Professional and Collaborative Learning: A Systemic Approach
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Chapter 13
An essential factor in determining the efficiency of the online education is the users' quality of interaction (QoI) with LMSs. In this chapter, the macro-meso-micro structure analysis is adopted, to examine the Fuzzy Inference...
Embracing Macro-, Meso-, and Micro-Levels of Analysis of FIS-Based LMS Users' Quality of Interaction
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Chapter 14
Part III is concluded with this chapter that proposes a Fuzzy Cognitive Map (FCM)-based modeling of the Quality of Interaction (QoI) of the Learning Management System (LMS) users within a blended (b)-learning context, namely FCM-QoI...
FCM-Based Modeling of LMS Users' Quality of Interaction
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Chapter 4
Overall Perspective  (pages 421-421)
Overall Perspective
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Chapter 15
Towards a Hybrid Modeling  (pages 422-442)
This chapter introduces the reader to Part IV of the book, proposing and discussing a hybrid approach that may serve, not only to synthesize and represent knowledge obtained from the data, but also to explore possible future online...
Towards a Hybrid Modeling
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Chapter 16
Arriving to the final destination of the journey started in chapter 1, this concluding chapter represents a brief reflection of the key considerations/contributions of the book and, simultaneously, provides a guidance for future...
Concluding Remarks and Probing Further
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Back Materials
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Compilation of References
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About the Authors
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Index
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