| Description |
1 online resource (xviii, 332 pages) illustrations |
| Series |
IET computing series 40 |
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IET computing series ; 40
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| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
5.9 Virtual facilitators and learning environment |
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Print version record |
| Subject |
Computer-assisted instruction -- Design
|
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Computer-assisted instruction -- Design
|
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artificial intelligence.
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augmented reality.
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Big Data.
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bioinformatics.
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cloud computing.
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computer aided instruction.
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data analysis.
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human factors.
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Internet of Things.
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mobile learning.
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teaching.
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| Form |
Electronic book
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| Author |
Goyal, Mukta editor
|
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Krishnamurthi, Rajalakshmi editor
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Yadav, Divakar editor
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| ISBN |
1839531215 |
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9781839531217 |
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