E-learning methodologies : fundamentals, technologies and applications / edited by Mukta Goyal, Rajalakshmi Krishnamurthi and Divakar Yadav.

Format:

Book

Contributor:

Goyal, Mukta, editor.

Krishnamurthi, Rajalakshmi, editor.

Yadav, Divakar, 1976- editor.

ProQuest ebook central.

Series:

IET computing collection ; 40.

IET computing series ; 40

Language:

English

Subjects (All):

Computer-assisted instruction--Design.

Computer-assisted instruction.

Physical Description:

1 online resource (xviii, 332 pages) : illustrations (color.

Place of Publication:

London : The Insitution of Engineering and Technology, 2021.

System Details:

text file

Contents:

Part I Introduction and pedagogies of e-learning systems with intelligent techniques p. 1

1 Introduction p. 3 / Mukta Goyal and Rajalakshmi Krishnamurthi and Divakar Yadav

1.1 Asynchronous learning and synchronous learning p. 4

1.2 Blended learning, distance learning, and Classroom 2.0 p. 5

1.2.1 E-learning p. 7

1.2.2 Smart e-learning p. 8

1.3 Different frameworks of smart e-learning p. 9

1.3.1 AI in e-learning p. 9

1.3.2 Mobile learning p. 10

1.3.3 Cloud-based learning p. 12

1.3.4 Big data in e-learning p. 14

1.3.5 IoT framework of e-learning p. 16

1.3.6 Augmented reality in learning p. 17

1.4 Gaps in existing frameworks p. 20

2 Goal-oriented adaptive e-learning p. 27 / Sushma Hans and Shelly Sachdeva

2.2 Literature survey p. 28

2.2.1 State-of-the-art p. 32

2.3 Goal-oriented adaptive e-learning system p. 35

2.3.1 Goal-oriented course graph structure p. 36

2.3.2 Registration module p. 39

2.3.3 Personalized assessment module p. 39

2.3.4 ACO-based learning path generation p. 40

2.3.5 Persistence into database and self-learning p. 43

2.4.1 Data preparation p. 44

2.4.2 Evolution of learning path with regular improvement p. 44

2.4.3 Evolution of learning path with late improvement p. 46

2.6 Future scope p. 49

3 Predicting students' behavioural engagement in microlearning using lea ruing analytics model p. 53 / Wan Mohd Amir Fazamin Wan Hamzah and Mohd Hafiz Yusoff and Ismahafezi Ismail and Norkhatimah Ismail

3.2 LA studies p. 54

3.4.1 Analysis of using NN p. 66

3.4.2 Analysis using LR p. 67

3.5 Comparison analysis using NN and LR p. 69

3.7 Future scope p. 73

4 Student performance prediction for adaptive e-learning systems p. 79 / Mukta Goyal and Divakar Yadav and Mehak Sood

4.2 Literature survey p. 80

4.2.1 Learner profile p. 80

4.2.2 Soft computing techniques p. 81

4.3.1 Conversion of numeric to intuitionistic fuzzy value p. 84

4.3.2 Learning style model p. 85

4.3.3 Personality model p. 86

4.3.4 Assessment of knowledge level p. 86

4.3.5 Intuitionistic fuzzy optimization algorithm and KNN classifier p. 87

4.5 Future work p. 100

Part II Technologies in e-learning p. 105

5 AI in e-learning p. 107 / Mudita Sinha and Leena N. Fukey and Ashutosh Sinha

5.1 Artificial intelligence in India p. 107

5.2 Artificial intelligence in education p. 108

5.3 AI in e-learning p. 108

5.4 Analysis and data p. 109

5.5 Emphasis on the area that needs improvement in e-learning p. 110

5.6 Creating comprehensive curriculum p. 111

5.7 Immersive learning p. 113

5.8 Intelligent tutoring systems p. 114

5.9 Virtual facilitators and learning environment p. 117

5.10 Content analytics p. 118

5.11 Paving new pathways in the coming decade: AI and e-learning p. 120

5.12 Improving accessibility for e-learning by AI p. 121

5.13 Artificial intelligence in personalized learning p. 122

5.14 Cuts costs for students, eases burden on teachers p. 122

5.15 Artificial intelligence in academic connectivity p. 123

5.16 Artificial intelligence in crowd service learning p. 124

5.17 How to improve registration and completion of e-learning courses by using AI p. 125

5.18 Expectations of participant in artificial intelligence in e-learning p. 126

5.19 Future of AI in e-learning p. 127

6 Mobile learning as the future of e-learning p. 133 / Muruganantham Ganesan and Vivek Kumar Singh and Subhojeet Biswas

6.2 E-learning p. 134

6.3 Mobile learning p. 134

6.3.1 Smartphone penetration in India p. 135

6.4 Need for mobile learning p. 135

6.5 Mobile learning in higher education p. 136

6.5.1 Intelligent technologies p. 137

6.6 Benefits of smartphone in academic learning p. 137

6.7 Different types of e-learning p. 138

6.7.1 Learning management system p. 138

6.7.2 Blended learning p. 139

6.7.3 Artificial intelligence p. 139

6.7.4 Internet of Things p. 139

6.7.5 Flipped classrooms p. 140

6.8 M-learning challenges p. 140

6.8.1 Cons of mobile learning p. 140

6.9 Education 4.0 p. 141

6.11 Future scope p. 141

7 Smart e-learning transition using big data: perspectives and opportunities p. 147 / T. Lucia Agnes Beena and T. Poongodi and P. Suresh

7.2 Big data applications in e-learning p. 149

7.2.1 Performance prediction p. 149

7.2.2 Attrition risk detection p. 151

7.2.3 Data visualization p. 151

7.2.4 Intelligent feedback p. 153

7.2.5 Course recommendation p. 153

7.2.6 Student skill estimation p. 154

7.2.7 Behavior detection p. 155

7.2.8 Collaboration and social network analysis p. 156

7.2.9 Developing concept maps p. 157

7.2.10 Constructing courseware p. 158

7.2.11 Planning and scheduling p. 158

7.3 Big data techniques for e-learning p. 159

7.3.1 Classification in e-learning p. 160

7.4 Big data tools p. 161

7.4.1 Hadoop platform for e-learning p. 162

7.4.2 Spark p. 165

7.4.3 Orange p. 165

7.5 Recent research perspectives and future direction p. 166

7.5.1 Future direction p. 168

8 E-learning using big data and cloud computing p. 175 / Dhanalekshmi Gopinathan and Archana Purwar

8.2 Conventional e-learning system and its issues p. 176

8.3 E-learning on cloud computing p. 177

8.4 Characteristics of cloud in e-learning p. 179

8.5 Cloud-based e-learning architecture p. 180

8.6 Cloud computing service-oriented architecture for e-learning p. 182

8.7 Big data in e-learning p. 182

8.7.1 The need for big data in e-learning p. 182

8.8 Review on big data-based e-learning systems p. 184

8.9 Association of big data and cloud computing p. 185

8.9.1 Infrastructure as a service (IaaS) in the public cloud p. 185

8.9.2 Platform as a service (PaaS) private cloud p. 185

8.9.3 Software as a service (SaaS) in a hybrid cloud p. 185

8.10 Use of big data and cloud technology for e-learning p. 186

8.11 Casestudies on e-learning p. 189

8.12 Case study of a cloud and big data-based Evaluation and Feedback Management System (EFMS) in e-learning p. 190

8.13 Open research challenges p. 191

8.13.1 Limited control over security and privacy p. 193

8.13.2 Limited control over compliance p. 193

8.13.3 Limited control over institutional data p. 193

8.13.4 Network dependency issues p. 193

8.13.5 Latency problem p. 194

8.15 Future work p. 194

9 E-learning through virtual laboratory environment: developing of IoT workshop course based on Node-RED p. 197 / Rajalakshmi Krishnamurthi and Dhanalekshmi Gopinathan

9.2 Virtual laboratory p. 199

9.3 Building blocks of IoT p. 201

9.3.1 Edge level p. 202

9.3.2 Connectivity level p. 202

9.3.3 Communications level p. 203

9.3.4 Service level p. 203

9.4 Node-RED tool p. 203

9.4.1 Why Node-RED? p. 204

9.4.2 Installation of Node-RED p. 204

9.5 IoT workshop p. 205

9.6 Teaching methodology p. 206

9.7 Course details p. 207

9.8 Experiment and result discussion p. 209

10 Mnemonics in e-learning using augmented reality p. 215 / Dinesh Kumar Saint and Aran Kumar Yadav and Kartik Sharma

10.2 Literature survey p. 216

10.2.1 E-learning p. 216

10.2.2 Augmented reality (tools and techniques) p. 216

10.2.3 Method of loci p. 218

10.4 Theory and research approach p. 220

10.5 Implementation and results p. 220

10.5.1 Concept-1 p. 221

10.5.2 Concept-2 p. 222

10.5.3 Concept-3 p. 224

10.5.4 Concept-4 p. 224

10.5.5 Concept-5 p. 225

10.5.6 Concept-6 p. 226

10.5.7 Coticept-7 p. 226

10.5.8 Concept-8 p. 227

10.5.9 Concept-9 p. 227

10.5.10 Concept-10 p. 227

10.7 Future work p. 231

11 E-learning tools and smart campus: boon or bane during COVID-19 p. 235 / Shikha Mehta and Krishna Bihari Dubey

11.2 E-learning p. 236

11.2.1 Synchronous e-learning p. 237

11.2.2 Asynchronous e-learning p. 238

11.3 Tools for synchronous e-learning p. 240

11.4 Side effects of using online learning tools or e-learning p. 240

11.4.1 Technical challenges p. 240

11.4.2 Health issues p. 245

11.4.3 Social and economic challenges p. 245

13.5 Future of education: e-learning + smart campus p. 246

11.5.1 Smart campus p. 246

11.5.2 Smart classroom p. 247

11.5.3 Importance of smart classrooms in e-learning application p. 248

11.5.4 What turns an ordinary classroom into a smart classroom that is required for e-learning? p. 248

11.7 Future work p. 249

12 Bioinformatics algorithms: course, teaching pedagogy and assessment p. 255 / Suma Dawn and Prantik Biswas

12.2 Course content: creation and access, course outcomes p. 257

12.2.1 Access of course content p. 258

12.2.2 Course outcomes p. 259

12.2.3 Course content p. 259

12.3 Strategies of lecture delivery p. 260

12.4 Details of the topics discussed p. 261

12.4.1 Topic 1: algorithms and complexity p. 261

12.4.2 Topic 2: molecular biology p. 265

12.4.3 Topic 3: exhaustive search-mapping, searching p. 267

12.4.4 Topic 4: greedy algorithms p. 270

12.4.5 Topic 5: dynamic programming algorithms p. 271

12.4.6 Topic 6: divide-and-conquer algorithms p. 273

12.4.7 Topic 7: graph algorithms p. 274

12.4.8 Topic 8: combinatorial pattern matching p. 276

12.4.9 Topic 9: clustering and trees p. 278

12.4.10 Topic 10: applications p. 278

12.5 In-class assessment approaches p. 279

12.5.1 Self-assessment by students p. 279

12.7 Conclusions and future scope p. 282

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 p. 285 / Adarsh Kumar and Alok Aggarwal and Kriti Sharma and Mukta Goyal

13.3 The methodology of active learning process p. 288

13.4 Introduction to elliptic curve cryptography p. 289

13.4.1 Elliptic curve operations p. 290

13.4.2 Fast point multiplication algorithms p. 294

13.5 Elliptic curve cryptography (ECC)-based authentication protocols p. 311

14 Conclusion p. 319 / Mukta Goyal and Rajalakshmi Krishnamurthi and Divakar Yadav

14.1 Future work p. 320.

Notes:

Includes bibliographical references and index.

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Description based on print version record.

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ebook version :

ISBN:

9781839531217

1839531215

Publisher Number:

40030891753

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