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E-raamat: E-learning Methodologies: Fundamentals, technologies and applications

Edited by (Jaypee Institute of Information Technology, Department of Computer Science and Engineering, Noida, India), Edited by (National Institute), Edited by (Jaypee Institute of Information Technology, Department of Computer Science and Engineering, Noida, India)
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  • Sari: Computing and Networks
  • Ilmumisaeg: 26-Feb-2021
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781839531217
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E-learning Methodologies: Fundamentals, technologies and applicationsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Computing and Networks
  • Ilmumisaeg: 26-Feb-2021
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781839531217
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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.



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.

About the editors xiii
Preface xv
Part I: Introduction and pedagogies of e-learning systems with intelligent techniques 1(104)
1 Introduction
3(24)
Mukta Goyal
Rajalakshmi Krishnamurthi
Divakar Yadav
1.1 Asynchronous learning and synchronous learning
4(1)
1.2 Blended learning, distance learning, and Classroom 2.0
5(4)
1.2.1 E-learning
7(1)
1.2.2 Smart e-learning
8(1)
1.3 Different frameworks of smart e-learning
9(11)
1.3.1 AI in e-learning
9(1)
1.3.2 Mobile learning
10(2)
1.3.3 Cloud-based learning
12(2)
1.3.4 Big data in e-learning
14(2)
1.3.5 IoT framework of e-learning
16(1)
1.3.6 Augmented reality in learning
17(3)
1.4 Gaps in existing frameworks
20(1)
1.5 Conclusion
20(1)
References
21(6)
2 Goal-oriented adaptive e-learning
27(26)
Sushma Hans
Shelly Sachdeva
2.1 Introduction
28(1)
2.2 Literature survey
28(7)
2.2.1 State-of-the-art
32(3)
2.3 Goal-oriented adaptive e-learning system
35(9)
2.3.1 Goal-oriented course graph structure
36(3)
2.3.2 Registration module
39(1)
2.3.3 Personalized assessment module
39(1)
2.3.4 ACO-based learning path generation
40(3)
2.3.5 Persistence into database and self-learning
43(1)
2.4 Experimental results
44(4)
2.4.1 Data preparation
44(1)
2.4.2 Evolution of learning path with regular improvement
44(2)
2.4.3 Evolution of learning path with late improvement
46(2)
2.5 Conclusion
48(1)
2.6 Future scope
49(1)
References
49(4)
3 Predicting students' behavioural engagement in microlearning using learning analytics model
53(26)
Wan Mohd Amir Fazamin Wan Hamzah
Mohd Hafiz Yusoff
Ismahafezi Ismail
Norkhatimah Ismail
3.1 Introduction
53(1)
3.2 LA studies
54(6)
3.3 Methods
60(6)
3.4 Results
66(3)
3.4.1 Analysis of using NN
66(1)
3.4.2 Analysis using LR
67(2)
3.5 Comparison analysis using NN and LR
69(4)
3.6 Conclusion
73(1)
3.7 Future scope
73(1)
References
73(6)
4 Student performance prediction for adaptive e-learning systems
79(26)
Mukta Goyal
Divakar Yadav
Mehak Sood
4.1 Introduction
79(1)
4.2 Literature survey
80(3)
4.2.1 Learner profile
80(1)
4.2.2 Soft computing techniques
81(2)
4.3 Methodology
83(5)
4.3.1 Conversion of numeric to intuitionistic fuzzy value
84(1)
4.3.2 Learning style model
85(1)
4.3.3 Personality model
86(1)
4.3.4 Assessment of knowledge level
86(1)
4.3.5 Intuitionistic fuzzy optimization algorithm and KNN classifier
87(1)
4.4 Experimental results
88(12)
4.5 Future work
100(1)
4.6 Conclusion
101(1)
References
101(4)
Part II: Technologies in e-learning 105(148)
5 AI in e-learning
107(26)
Mudita Sinha
Leena N. Fukey
Ashutosh Sinha
5.1 Artificial intelligence in India
107(1)
5.2 Artificial intelligence in education
108(1)
5.3 AI in e-learning
108(1)
5.4 Analysis and data
109(1)
5.5 Emphasis on the area that needs improvement in e-learning
110(1)
5.6 Creating comprehensive curriculum
111(2)
5.7 Immersive learning
113(1)
5.8 Intelligent tutoring systems
114(3)
5.9 Virtual facilitators and learning environment
117(1)
5.10 Content analytics
118(2)
5.11 Paving new pathways in the coming decade: AI and e-learning
120(1)
5.12 Improving accessibility for e-learning by AI
121(1)
5.13 Artificial intelligence in personalized learning
122(1)
5.14 Cuts costs for students, eases burden on teachers
122(1)
5.15 Artificial intelligence in academic connectivity
123(1)
5.16 Artificial intelligence in crowd service learning
124(1)
5.17 How to improve registration and completion of e-learning courses by using AI
125(1)
5.18 Expectations of participant in artificial intelligence in e-learning
126(1)
5.19 Future of AI in e-learning
127(2)
5.20 Conclusion
129(1)
References
129(4)
6 Mobile learning as the future of e-learning
133(14)
Muruganantham Ganesan
Vivek Kumar Singh
Subhojeet Biswas
6.1 Introduction
133(1)
6.2 E-learning
134(1)
6.3 Mobile learning
134(1)
6.3.1 Smartphone penetration in India
135(1)
6.4 Need for mobile learning
135(1)
6.5 Mobile learning in higher education
136(1)
6.5.1 Intelligent technologies
137(1)
6.6 Benefits of smartphone in academic learning
137(1)
6.7 Different types of e-learning
138(2)
6.7.1 Learning management system
138(1)
6.7.2 Blended learning
139(1)
6.7.3 Artificial intelligence
139(1)
6.7.4 Internet of Things
139(1)
6.7.5 Flipped classrooms
140(1)
6.8 M-learning challenges
140(1)
6.8.1 Cons of mobile learning
140(1)
6.9 Education 4.0
141(1)
6.10 Conclusion
141(1)
6.11 Future scope
141(2)
References
143(4)
7 Smart e-learning transition using big data: perspectives and opportunities
147(28)
T. Lucia Agnes Beena
T. Poongodi
P. Suresh
7.1 Introduction
147(2)
7.2 Big data applications in e-learning
149(10)
7.2.1 Performance prediction
149(2)
7.2.2 Attrition risk detection
151(1)
7.2.3 Data visualization
151(2)
7.2.4 Intelligent feedback
153(1)
7.2.5 Course recommendation
153(1)
7.2.6 Student skill estimation
154(1)
7.2.7 Behavior detection
155(1)
7.2.8 Collaboration and social network analysis
156(1)
7.2.9 Developing concept maps
157(1)
7.2.10 Constructing courseware
158(1)
7.2.11 Planning and scheduling
158(1)
7.3 Big data techniques for e-learning
159(2)
7.3.1 Classification in e-learning
160(1)
7.4 Big data tools
161(5)
7.4.1 Hadoop platform for e-learning
162(3)
7.4.2 Spark
165(1)
7.4.3 Orange
165(1)
7.5 Recent research perspectives and future direction
166(2)
7.5.1 Future direction
168(1)
7.6 Conclusion
168(1)
References
169(6)
8 E-learning using big data and cloud computing
175(22)
Dhanalekshmi Gopinathan
Archana Purwar
8.1 Introduction
175(1)
8.2 Conventional e-learning system and its issues
176(1)
8.3 E-learning on cloud computing
177(2)
8.4 Characteristics of cloud in e-learning
179(1)
8.5 Cloud-based e-learning architecture
180(2)
8.6 Cloud computing service-oriented architecture for e-learning
182(1)
8.7 Big data in e-learning
182(2)
8.7.1 The need for big data in e-learning
182(2)
8.8 Review on big data-based e-learning systems
184(1)
8.9 Association of big data and cloud computing
185(1)
8.9.1 Infrastructure as a service (IaaS) in the public cloud
185(1)
8.9.2 Platform as a service (PaaS) private cloud
185(1)
8.9.3 Software as a service (SaaS) in a hybrid cloud
185(1)
8.10 Use of big data and cloud technology for e-learning
186(3)
8.11 Case studies on e-learning
189(1)
8.12 Case study of a cloud and big data-based Evaluation and Feedback Management System (EFMS) in e-learning
190(1)
8.13 Open research challenges
191(3)
8.13.1 Limited control over security and privacy
193(1)
8.13.2 Limited control over compliance
193(1)
8.13.3 Limited control over institutional data
193(1)
8.13.4 Network dependency issues
193(1)
8.13.5 Latency problem
194(1)
8.14 Conclusion
194(1)
8.15 Future work
194(1)
References
194(3)
9 E-learning through virtual laboratory environment: developing of IoT workshop course based on Node-RED
197(18)
Rajalakshmi Krishnamurthi
Dhanalekshmi Gopinathan
9.1 Introduction
197(2)
9.2 Virtual laboratory
199(2)
9.3 Building blocks of IoT
201(2)
9.3.1 Edge level
202(1)
9.3.2 Connectivity level
202(1)
9.3.3 Communications level
203(1)
9.3.4 Service level
203(1)
9.4 Node-RED tool
203(2)
9.4.1 Why Node-RED?
204(1)
9.4.2 Installation of Node-RED
204(1)
9.5 IoT workshop
205(1)
9.6 Teaching methodology
206(1)
9.7 Course details
207(2)
9.8 Experiment and result discussion
209(2)
9.9 Conclusion
211(1)
References
212(3)
10 Mnemonics in e-learning using augmented reality
215(20)
Dinesh Kumar Saini
Arun Kumar Yadav
Kartik Sharma
10.1 Introduction
215(1)
10.2 Literature survey
216(3)
10.2.1 E-learning
216(1)
10.2.2 Augmented reality (tools and techniques)
216(2)
10.2.3 Method of loci
218(1)
10.3 Related work
219(1)
10.4 Theory and research approach
220(1)
10.5 Implementation and results
220(10)
10.5.1 Concept-1
221(1)
10.5.2 Concept-2
222(2)
10.5.3 Concept-3
224(1)
10.5.4 Concept-4
224(1)
10.5.5 Concept-5
225(1)
10.5.6 Concept-6
226(1)
10.5.7 Concept-7
226(1)
10.5.8 Concept-8
227(1)
10.5.9 Concept-9
227(1)
10.5.10 Concept-10
227(3)
10.6 Conclusion
230(1)
10.7 Future work
231(1)
References
231(4)
11 E-learning tools and smart campus: boon or bane during COVID-19
235(18)
Shikha Mehta
Krishna Bihari Dubey
11.1 Introduction
235(1)
11.2 E-learning
236(4)
11.2.1 Synchronous e-learning
237(1)
11.2.2 Asynchronous e-learning
238(2)
11.3 Tools for synchronous e-learning
240(1)
11.4 Side effects of using online learning tools or e-learning
240(6)
11.4.1 Technical challenges
240(5)
11.4.2 Health issues
245(1)
11.4.3 Social and economic challenges
245(1)
11.5 Future of education: e-learning + smart campus
246(3)
11.5.1 Smart campus
246(1)
11.5.2 Smart classroom
247(1)
11.5.3 Importance of smart classrooms in e-learning application
248(1)
11.5.4 What turns an ordinary classroom into a smart classroom that is required for e-learning?
248(1)
11.6 Conclusion
249(1)
11.7 Future work
249(1)
References
249(4)
Part III: Case studies 253(70)
12 Bioinformatics algorithms: course, teaching pedagogy and assessment
255(30)
Suma Dawn
Prantik Biswas
12.1 Introduction
256(1)
12.2 Course content: creation and access, course outcomes
257(3)
12.2.1 Access of course content
258(1)
12.2.2 Course outcomes
259(1)
12.2.3 Course content
259(1)
12.3 Strategies of lecture delivery
260(1)
12.4 Details of the topics discussed
261(18)
12.4.1 Topic 1: algorithms and complexity
261(4)
12.4.2 Topic 2: molecular biology
265(2)
12.4.3 Topic 3: exhaustive search-mapping, searching
267(3)
12.4.4 Topic 4: greedy algorithms
270(1)
12.4.5 Topic 5: dynamic programming algorithms
271(2)
12.4.6 Topic 6: divide-and-conquer algorithms
273(1)
12.4.7 Topic 7: graph algorithms
274(2)
12.4.8 Topic 8: combinatorial pattern matching
276(2)
12.4.9 Topic 9: clustering and trees
278(1)
12.4.10 Topic 10: applications
278(1)
12.5 In-class assessment approaches
279(2)
12.5.1 Self-assessment by students
279(2)
12.6 Discussion
281(1)
12.7 Conclusions and future scope
282(1)
References
283(2)
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
285(34)
Adarsh Kumar
Alok Aggarwal
Kriti Sharma
Mukta Goyal
13.1 Introduction
286(1)
13.2 Related work
286(2)
13.3 The methodology of active learning process
288(1)
13.4 Introduction to elliptic curve cryptography
289(22)
13.4.1 Elliptic curve operations
290(4)
13.4.2 Fast point multiplication algorithms
294(17)
13.5 Elliptic curve cryptography (ECC)-based authentication protocols
311(1)
13.6 Experimental results
312(2)
13.7 Conclusion
314(1)
References
315(4)
14 Conclusion
319(4)
Mukta Goyal
Rajalakshmi Krishnamurthi
Divakar Yadav
14.1 Future work
320(3)
Index 323
Mukta Goyal is an Assistant Professor in the Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India. She attained her doctorate in the domain of Soft Computing from Jaypee Institute of Information Technology, Noida. She has over 20 years of teaching experience at both undergraduate and postgraduate levels. Mukta has organized special sessions in conferences and is in the program committee of various conferences of repute. She has many national and international research publications to her credit. She is an active researcher in the field of soft computing, e-learning, e-governance, blockchain, and machine learning. She has guided various M.Tech. theses, more than 100 B.Tech. projects. Presently, she is guiding three Ph.D. scholars.



Rajalakshmi Krishnamurthi is currently working as an Assistant Professor (Senior Grade) in the Department of Computer Science and Engineering at Jaypee Institute of Information Technology, Noida, India. She is a senior member of IEEE, a professional member of ACM, SIAM, and CSI. She is currently serving as a treasurer, Delhi ACM-W chapter. She has over 17 years of teaching experience both at undergraduate and postgraduate levels. She has more than 50 research publications in various reputed international journals, book chapters, and international conferences. She is serving as a guest editor in Springer Nature. Her research interest includes Internet of Things, cloud computing, mobile computing, and e-learning. She has introduced and developed several courses at B.Tech and M.Tech levels. She has refereed in reputed journals like IEEE, IoT, wireless networks, peer-to-peer Springer. She has been a technical program member in several international conferences. Currently, she is supervising two Ph.D. scholars and one Ph.D. completed. She has supervised more than 14 M.Tech theses and 100 B.Tech major projects.



Divakar Yadav is currently working as an Associate Professor in the Department of Computer Science and Engineering at National Institute of Technology, Hamirpur (HP), India. Prior to joining this institute, he had worked at Madan Mohan Malaviya University of Technology, Gorakhpur (UP), India as an Associate Professor and Jaypee Institute of Information Technology, Noida, India as an Assistant as well as an Associate Professor. He did his undergraduate degree (B.Tech) in Computer Science and Engineering in 1999 from IET, Lucknow, postgraduate degree (M.Tech) in Information Technology in 2005 from Indian Institute of Information Technology, Allahabad and Ph.D. in Computer Science and Engineering in 2010 from Jaypee Institute of Information Technology, Noida. He also worked as a post-doctoral fellow at the University of Carlos III, Madrid, Spain between 2011 and 2012. He supervised four Ph.D. theses, 22 M.Tech dissertations, and many undergraduate projects. He also published more than 85 research articles in reputed international journals and conference proceedings. His area of research includes information retrieval and machine learning. He is a senior member of IEEE.
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