E-learning Methodologies Fundamentals, technologies and applications
E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of desi...
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| Jazyk: | English |
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Stevenage
The Institution of Engineering and Technology
2021
Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
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| Edícia: | IET Computing and networks |
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| ISBN: | 9781839531200, 1839531207 |
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| Abstract | E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies.
With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume 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. |
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| AbstractList | E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies.With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume 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. E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies. With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performance and reliable intelligent and adaptive real time e-learning systems and platforms. 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. E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies. With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume 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. |
| Author | Goyal Mukta Krishnamurthi Rajalakshmi Yadav Divakar |
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| Editor | Krishnamurthi, Rajalakshmi Goyal, Mukta Yadav, Divakar |
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| Keywords | data analysis human factors bioinformatics Big Data augmented reality teaching computer aided instruction Internet of Things cloud computing artificial intelligence mobile learning |
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| Snippet | E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote... 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... |
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| SubjectTerms | Communications engineering / telecommunications Computer-assisted instruction General Engineering & Project Administration General References TECHNOLOGY & ENGINEERING Telecommunications |
| Subtitle | Fundamentals, technologies and applications |
| TableOfContents | Part I: Introduction and pedagogies of e-learning systems with intelligent techniques -- Chapter 1: Introduction -- Chapter 2: Goal-oriented adaptive e-learning -- Chapter 3: Predicting students' behavioural engagement in microlearning using learning analytics model -- Chapter 4: Student performance prediction for adaptive e-learning systems --
-- Part II: Technologies in e-learning -- Chapter 5: AI in e-learning -- Chapter 6: Mobile learning as the future of e-learning -- Chapter 7: Smart e-learning transition using big data: perspectives and opportunities -- Chapter 8: E-learning using big data and cloud computing -- Chapter 9: E-learning through virtual laboratory environment: developing of IoT workshop course based on Node-RED -- Chapter 10: Mnemonics in e-learning using augmented reality -- Chapter 11: E-learning tools and smart campus: boon or bane during COVID-19 --
-- Part III: Case studies -- Chapter 12: Bioinformatics algorithms: course, teaching pedagogy and assessment -- Chapter 13: Active learning in E-learning: a case study to teach elliptic curve cryptosystem, its fast computational algorithms and authentication protocols for resource constraint RFID-sensor integrated mobile devices -- Chapter 14: Conclusion -- Title Page Preface Table of Contents 1. Introduction 2. Goal-Oriented Adaptive E-Learning 3. Predicting Students' Behavioural Engagement in Microlearning Using Learning Analytics Model 4. Student Performance Prediction for Adaptive E-Learning Systems 5. AI in E-Learning 6. Mobile Learning as the Future of E-Learning 7. Smart E-Learning Transition Using Big Data: Perspectives and Opportunities 8. E-Learning Using Big Data and Cloud Computing 9. E-Learning through Virtual Laboratory Environment: Developing of IoT Workshop Course Based on Node-Red 10. Mnemonics in E-Learning Using Augmented Reality 11. E-Learning Tools and Smart Campus: Boon or Bane during COVID-19 12. Bioinformatics Algorithms: Course, Teaching Pedagogy and Assessment 13. Active Learning in E-Learning: A Case Study to Teach Elliptic Curve Cryptosystem, its Fast Computational Algorithms and Authentication Protocols for Resource Constraint RFID-Sensor Integrated Mobile Devices 14. Conclusion Index 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 -- 5.9 Virtual facilitators and learning environment -- 5.10 Content analytics -- 5.11 Paving new pathways in the coming decade: AI and e-learning -- 5.12 Improving accessibility for e-learning by AI -- 5.13 Artificial intelligence in personalized learning -- 5.14 Cuts costs for students, eases burden on teachers -- 5.15 Artificial intelligence in academic connectivity -- 5.16 Artificial intelligence in crowd service learning -- 5.17 How to improve registration and completion of e-learning courses by using AI -- 5.18 Expectations of participant in artificial intelligence in e-learning -- 5.19 Future of AI in e-learning -- 5.20 Conclusion -- References -- 6 Mobile learning as the future of e-learning -- 6.1 Introduction -- 6.2 E-learning -- 6.3 Mobile learning -- 6.3.1 Smartphone penetration in India -- 6.4 Need for mobile learning 6.5 Mobile learning in higher education -- 6.5.1 Intelligent technologies -- 6.6 Benefits of smartphone in academic learning -- 6.7 Different types of e-learning -- 6.7.1 Learning management system -- 6.7.2 Blended learning -- 6.7.3 Artificial intelligence -- 6.7.4 Internet of Things -- 6.7.5 Flipped classrooms -- 6.7.5.1 M-learning and government -- 6.8 M-learning challenges -- 6.8.1 Cons of mobile learning -- 6.9 Education 4.0 -- 6.10 Conclusion -- 6.11 Future scope -- References -- 7 Smart e-learning transition using big data: perspectives and opportunities -- 7.1 Introduction -- 7.2 Big data applications in e-learning -- 7.2.1 Performance prediction -- 7.2.2 Attrition risk detection -- 7.2.3 Data visualization -- 7.2.4 Intelligent feedback -- 7.2.5 Course recommendation -- 7.2.6 Student skill estimation -- 7.2.7 Behavior detection -- 7.2.8 Collaboration and social network analysis -- 7.2.9 Developing concept maps -- 7.2.10 Constructing courseware -- 7.2.11 Planning and scheduling -- 7.3 Big data techniques for e-learning -- 7.3.1 Classification in e-learning -- 7.3.1.1 Fuzzy logic -- 7.3.1.2 ANN and evolutionary computation -- 7.3.1.3 Association rule -- 7.4 Big data tools -- 7.4.1 Hadoop platform for e-learning -- 7.4.1.1 Apache Hadoop -- 7.4.1.2 Hadoop Distributed File System -- 7.4.1.3 MapReduce -- 7.4.1.4 YARN -- 7.4.2 Spark -- 7.4.3 Orange -- 7.5 Recent research perspectives and future direction -- 7.5.1 Future direction -- 7.6 Conclusion -- References -- 8 E-learning using big data and cloud computing -- 8.1 Introduction -- 8.2 Conventional e-learning system and its issues -- 8.3 E-learning on cloud computing -- 8.4 Characteristics of cloud in e-learning -- 8.5 Cloud-based e-learning architecture -- 8.6 Cloud computing service-oriented architecture for e-learning -- 8.7 Big data in e-learning -- 8.7.1 The need for big data in e-learning 8.8 Review on big data-based e-learning systems -- 8.9 Association of big data and cloud computing -- 8.9.1 Infrastructure as a service (IaaS) in the public cloud -- 8.9.2 Platform as a service (PaaS) private cloud -- 8.9.3 Software as a service (SaaS) in a hybrid cloud -- 8.10 Use of big data and cloud technology for e-learning -- 8.11 Casestudies on e-learning -- 8.12 Case study of a cloud and big data-based Evaluation and Feedback Management System (EFMS) in e-learning -- 8.13 Open research challenges -- 8.13.1 Limited control over security and privacy -- 8.13.2 Limited control over compliance -- 8.13.3 Limited control over institutional data -- 8.13.4 Network dependency issues -- 8.13.5 Latency problem -- 8.14 Conclusion -- 8.15 Future work -- References -- 9 E-learning through virtual laboratory environment: developing of IoT workshop course based on Node-RED -- 9.1 Introduction -- 9.2 Virtual laboratory -- 9.3 Building blocks of IoT -- 9.3.1 Edge level -- 9.3.2 Connectivity level -- 9.3.3 Communications level -- 9.3.4 Service level -- 9.4 Node-RED tool -- 9.4.1 Why Node-RED? -- 9.4.2 Installation of Node-RED -- 9.5 IoT workshop -- 9.6 Teaching methodology -- 9.7 Course details -- 9.8 Experiment and result discussion -- 9.9 Conclusion -- References -- 10 Mnemonics in e-learning using augmented reality -- 10.1 Introduction -- 10.2 Literature survey -- 10.2.1 E-learning -- 10.2.2 Augmented reality (tools and techniques) -- 10.2.2.1 Display techniques -- 10.2.2.2 Tracking techniques -- 10.2.3 Method of loci -- 10.3 Related work -- 10.4 Theory and research approach -- 10.5 Implementation and results -- 10.5.1 Concept-1 -- 10.5.2 Concept-2 -- 10.5.3 Concept-3 -- 10.5.4 Concept-4 -- 10.5.5 Concept-5 -- 10.5.6 Concept-6 -- 10.5.7 Concept-7 -- 10.5.8 Concept-8 -- 10.5.9 Concept-9 -- 10.5.10 Concept-10 -- 10.6 Conclusion -- 10.7 Future work -- References 13.4 Introduction to elliptic curve cryptography 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 11 E-learning tools and smart campus: boon or bane during COVID-19 -- 11.1 Introduction -- 11.2 E-learning -- 11.2.1 Synchronous e-learning -- 11.2.2 Asynchronous e-learning -- 11.3 Tools for synchronous e-learning -- 11.4 Side effects of using online learning tools or e-learning -- 11.4.1 Technical challenges -- 11.4.2 Health issues -- 11.4.3 Social and economic challenges -- 11.5 Future of education: e-learning + smart campus -- 11.5.1 Smart campus -- 11.5.2 Smart classroom -- 11.5.3 Importance of smart classrooms in e-learning application -- 11.5.4 What turns an ordinary classroom into a smart classroom that is required for e-learning? -- 11.6 Conclusion -- 11.7 Future work -- References -- Part III: Case studies -- 12 Bioinformatics algorithms: course, teaching pedagogy and assessment -- 12.1 Introduction -- 12.2 Course content: creation and access, course outcomes -- 12.2.1 Access of course content -- 12.2.2 Course outcomes -- 12.2.3 Course content -- 12.3 Strategies of lecture delivery -- 12.4 Details of the topics discussed -- 12.4.1 Topic 1: algorithms and complexity -- 12.4.2 Topic 2: molecular biology -- 12.4.3 Topic 3: exhaustive search-mapping, searching -- 12.4.4 Topic 4: greedy algorithms -- 12.4.5 Topic 5: dynamic programming algorithms -- 12.4.6 Topic 6: divide-and-conquer algorithms -- 12.4.7 Topic 7: graph algorithms -- 12.4.8 Topic 8: combinatorial pattern matching -- 12.4.9 Topic 9: clustering and trees -- 12.4.10 Topic 10: applications -- 12.5 In-class assessment approaches -- 12.5.1 Self-assessment by students -- 12.6 Discussion -- 12.7 Conclusions and future scope -- References -- 13 Active learning in E-learning: a case study to teach elliptic curve cryptosystem, its fast computational algorithms and authenti -- 13.1 Introduction -- 13.2 Related work -- 13.3 The methodology of active learning process |
| Title | E-learning Methodologies |
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