E-learning methodologies fundamentals, technologies and applications

This book covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Goyal, Mukta (Editor ), Krishnamurthi, Rajalakshmi (Editor ), Yadav, Divakar (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London The Insitution of Engineering and Technology 2021
Colección:IET computing series ; 40
Temas:
Computer-assisted instruction > Design.
Computer-assisted instruction > Design
artificial intelligence.
augmented reality.
Big Data.
bioinformatics.
cloud computing.
computer aided instruction.
data analysis.
human factors.
Internet of Things.
mobile learning.
teaching.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Contents
  • About the editors
  • Preface
  • Part I: Introduction and pedagogies of e-learning systems with intelligent techniques
  • Part II: Technologies in e-learning
  • Part III: Case studies
  • Part I: Introduction and pedagogies of e-learning systems with intelligent techniques
  • 1 Introduction
  • 1.1 Asynchronous learning and synchronous learning
  • 1.2 Blended learning, distance learning, and Classroom 2.0
  • 1.2.1 E-learning
  • 1.2.2 Smart e-learning
  • 1.3 Different frameworks of smart e-learning
  • 1.3.1 AI in e-learning
  • 1.3.2 Mobile learning
  • 1.3.3 Cloud-based learning
  • 1.3.4 Big data in e-learning
  • 1.3.5 IoT framework of e-learning
  • 1.3.6 Augmented reality in learning
  • 1.4 Gaps in existing frameworks
  • 1.5 Conclusion
  • References
  • 2 Goal-oriented adaptive e-learning
  • 2.1 Introduction
  • 2.2 Literature survey
  • 2.2.1 State-of-the-art
  • 2.3 Goal-oriented adaptive e-learning system
  • 2.3.1 Goal-oriented course graph structure
  • 2.3.1.1 CG components
  • 2.3.1.2 Database
  • 2.3.2 Registration module
  • 2.3.3 Personalized assessment module
  • 2.3.3.1 Dynamic learning ability
  • 2.3.3.2 Dynamic learning success
  • 2.3.4 ACO-based learning path generation
  • 2.3.4.1 Objectives
  • 2.3.4.2 Time constraint
  • 2.3.4.3 Ant colony optimization
  • 2.3.5 Persistence into database and self-learning
  • 2.4 Experimental results
  • 2.4.1 Data preparation
  • 2.4.2 Evolution of learning path with regular improvement
  • 2.4.2.1 Static learning path
  • 2.4.2.2 Dynamic learning paths
  • 2.4.3 Evolution of learning path with late improvement
  • 2.4.3.1 Static learning path
  • 2.4.3.2 Dynamic learning paths
  • 2.5 Conclusion
  • 2.6 Future scope
  • References
  • 3 Predicting students' behavioural engagement in microlearning using learning analytics model
  • 3.1 Introduction
  • 3.2 LA studies
  • 3.3 Methods
  • 3.4 Results
  • 3.4.1 Analysis of using NN
  • 3.4.2 Analysis using LR
  • 3.5 Comparison analysis using NN and LR
  • 3.6 Conclusion
  • 3.7 Future scope
  • References
  • 4 Student performance prediction for adaptive e-learning systems
  • 4.1 Introduction
  • 4.2 Literature survey
  • 4.2.1 Learner profile
  • 4.2.2 Soft computing techniques
  • 4.3 Methodology
  • 4.3.1 Conversion of numeric to intuitionistic fuzzy value
  • 4.3.2 Learning style model
  • 4.3.3 Personality model
  • 4.3.4 Assessment of knowledge level
  • 4.3.5 Intuitionistic fuzzy optimization algorithm and KNN classifier
  • 4.4 Experimental results
  • 4.5 Future work
  • 4.6 Conclusion
  • References
  • Part II: Technologies in e-learning
  • 5 AI in e-learning
  • 5.1 Artificial intelligence in India
  • 5.2 Artificial intelligence in education
  • 5.3 AI in e-learning
  • 5.4 Analysis and data
  • 5.5 Emphasis on the area that needs improvement in e-learning
  • 5.6 Creating comprehensive curriculum
  • 5.7 Immersive learning
  • 5.8 Intelligent tutoring systems

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