Computational Intelligence in Digital Pedagogy

✍ Scribed by Arpan Deyasi, Soumen Mukherjee, Anirban Mukherjee, Arup Kumar Bhattacharjee, Arindam Mondal

• Publisher: Springer Singapore
• Year: 2021
• Tongue: English
• Leaves: 305
• Series: Intelligent Systems Reference Library, 197
• Category: Library

✦ Synopsis

This book is a useful guide for the teaching fraternity, administrators and education technology professionals to make good use of AI across outcome-based technical education (OBTE) ecosystem and infuse innovations and affordable digital technologies to traditional pedagogic processes to make teaching-learning more independent of human factor (teacher/student quality), time and place and at the same time more impactful and enjoyable for the learners. Providing access to the digital media and learning tools (even to the extent of mobile apps) to the students would allow them to keep pace with innovations in learning technologies, learn according to their own pace and improve their understanding level and have instantaneous feedback and evaluation. The book explores these new challenges and scope of using computational intelligence in educational technology. The book also addresses how based on the data collected from the outcome of conventional educational system, intelligent diagnostic and feedback system is developed which will change the teaching strategies and corresponding teaching-learning process. The book covers a wider framework of digital pedagogy and its intelligent applications on various sectors of education system.

✦ Table of Contents

Foreword
Preface
Contents
About the Editors
1 Authentic Pedagogy: A Project-Oriented Teaching–Learning Method Based on Critical Thinking
1.1 Introduction
1.2 Comparative Studies Between Pedagogic Principles
1.3 Procedure of Authentic Learning
1.4 Incorporation of Statistical Analysis
1.5 Results and Discussions
1.5.1 Performance
1.5.2 Quality Analysis
1.6 Conclusion
References
2 A Set of Empirical Models to Evaluate E-learning Web Sites and Their Comparison
2.1 Introduction
2.2 Related Work
2.3 Proposed Model
2.3.1 Parameters Considered for Evaluation
2.3.2 Proposed Model Using PCA
2.3.3 Proposed Model Using AHP
2.4 Implementation
2.4.1 Results Using PCA
2.4.2 Results Using AHP
2.4.3 Comparison Between the Proposed Methodologies
2.5 Conclusion and Future Scope
References
3 Multimedia-Based Learning Tools and Its Scope, Applications for Virtual Learning Environment
3.1 Introduction
3.2 Features and Challenges in Multimedia-Based Learning Tools
3.3 Results and Discussion
3.4 Conclusion
References
4 Social Network Analysis in Education: A Study
4.1 Introduction
4.2 Basic Terms and Concepts Associated with Social Network Analysis in Education Field
4.3 Architecture
4.3.1 Data Mining in Educational Data and Application
4.4 Application of SNA in Education: Related Work
4.5 Challenges
4.6 Conclusion and Future Scope
References
5 Personalization in Education Using Recommendation System: An Overview
5.1 Introduction
5.2 Basic Terms
5.2.1 E-learning
5.2.2 Integrated Classroom Teaching
5.2.3 Recommendation System
5.2.4 Collaborative Filtering
5.2.5 Content Based Recommendation System
5.2.6 Hybrid Recommendation System
5.3 Overview of Recommendation System in E-learning Sphere
5.4 Related Work
5.5 Challenges
5.6 Proposed Model
5.7 Conclusion and Future Direction
References
6 Automation of Attainment Calculation in Outcome-Based Technical Education (OBTE)
6.1 Introduction
6.2 Previous Works
6.3 Proposed Scheme
6.3.1 Automation
6.3.2 Description of the Software
6.4 Case Study of CO, PO, and PSO Attainment Using Rubrics for a Set of Students
6.4.1 Generation of CO–PO, CO–PSO, Course–PO, and Course–PSO Mapping
6.4.2 Generation of Course–PO and Course–PSO Mapping at Program Level
6.4.3 Measuring Course Outcomes Attained Through University Examination (External Assessment)
6.4.4 Measuring Course Outcomes Attained Through Internal Examinations, Assignments, etc. (Internal Assessment)
6.4.5 Course Outcome Direct Attainment
6.4.6 Course Outcome Indirect Attainment
6.4.7 Total PO and PSO Attainment in Program Level
6.5 Comparative Analysis
6.6 Conclusion
References
7 Quality Issues in Teaching–Learning Process
7.1 Introduction
7.2 Rationale
7.3 VUCA and Quality in the LT Process
7.4 Characteristics of Quality Teaching
7.5 Teaching Methodology
7.6 Case in Point
7.7 Quality Indicators
7.8 Quality Initiatives
7.9 Professional Development
7.10 Conclusion
References
8 Digital English Language Laboratory: Roles, Challenges and Scopes for the Future Development in India
8.1 Introduction
8.1.1 Motivation
8.1.2 Novelty
8.2 Research Questions
8.3 Methodology
8.4 Learning Styles
8.4.1 Personality Types and Learning Styles
8.4.2 Impact of Gender and Cultural Differences on Learning Styles
8.4.3 Learning English as a Second Language: The Role of the Digital Language Laboratory
8.4.4 Difference Between a Traditional Language Laboratory and a Digital Language Laboratory
8.4.5 Roles of a Digital Language Laboratory
8.5 A Survey
8.5.1 Language Laboratory at RCC Institute of Information Technology
8.5.2 The Survey Questionnaire
8.5.3 Data Collection and Analysis
8.5.4 Challenges of the Digital Language Laboratory in India and Probable Recommendations
8.6 Scope for the Future Development of Digital English Language Laboratory: Role of Artificial Intelligence
8.7 Conclusion
References
9 Overview and Future Scope of SWAYAM in the World of MOOCS: A Comparative Study with Reference to Major International MOOCS
9.1 Introduction
9.2 Literature Review
9.3 Research Gap
9.4 Need for Comparison of Swayam with Major MOOCS
9.5 An Overview of Swayam in 2019
9.6 Research Objective
9.7 Research Methodology
9.8 Rationale for Choosing Each Parameter and Its Contributing Factors
9.9 Analysis and Findings
9.9.1 Numerical Analysis:
9.9.2 Theoretical Explanation
9.9.3 Graphical Representation
9.9.4 Summary Table and Graph for Horizontal Summation Analysis of Parameters Discussed in Table 9.4
9.10 Application of Computational Intelligence in MOOCS
9.10.1 Stage I—Learner Enrolment
9.10.2 Stage II—Proposed Model for Learning Process in MOOCs
9.11 Limitations of the Study
9.12 Future Scope
9.13 Conclusion
References
10 Blending of Traditional System and Digital Pedagogy: An Indian Perspective
10.1 Introduction
10.2 Blending of Traditional System and Digital Pedagogy: An Indian Perspective
10.2.1 Digital Pedagogy Initiatives in India
10.2.2 Computational Intelligence: Applications in Pedagogy
10.3 Nurturing Innovation Using Digital Technology
10.3.1 Proposed Model 1: Intelligent School Network for Research (ISNR)
10.3.2 Case Study
10.3.3 Benefits of ISNR Model
10.4 Computational Intelligence for Formative Assessment
10.4.1 Proposed Model 2: Intelligent Feedback System for Classrooms (IFSC)
10.4.2 Benefits of IFSC Model
10.5 Conclusion
10.5.1 Contribution of the Work
10.5.2 Future Work
References
11 Application of Internet of Things in Digital Pedagogy
11.1 Introduction
11.2 Motivation and Contribution
11.3 Literature Survey
11.4 Advantages of IoT in Education [6, 7]
11.4.1 Data Collection
11.4.2 Personalized Learning
11.4.3 Security
11.4.4 Interactive Learning
11.4.5 Increasing Efficiency
11.5 Implementation
11.6 Results
11.7 Conclusion
References
12 An Innovative Step for Enhancement in Student Results and Teaching–Learning Process Using Educational Technology
12.1 Introduction
12.1.1 Formative Assessments
12.1.2 Summative Assessments
12.2 Literature Survey
12.3 Artificial Intelligence in Education Field
12.4 Role of Computational Intelligence in Result Analysis
12.4.1 Supervised Learning
12.4.2 Unsupervised Learning
12.4.3 Reinforcement Learning
12.4.4 Ensemble Learning Method
12.5 Fundamentals of Teachers' Teaching
12.5.1 Teaching Model
12.5.2 Teaching Skills
12.5.3 Teaching Relationship with Students
12.5.4 Reflection with Students
12.6 Statistical T-Test Analysis of CSE Students for Outcome-Based Teaching–Learning Process
12.7 Conclusion
References
13 Digital Pedagogical Paradigm in Language Lab-Based English Teaching for Higher Technical Education
13.1 Introduction
13.2 Language Lab-Based English Teaching Across Time and Clime
13.3 Digital Lesson Plan for Lab-Based English Teaching
13.4 Pedagogical Progress of English Teaching Across the Globe
13.5 Teaching English for Technical Communication
13.6 Digital Pedagogy and Teaching Strategies in Operation
13.7 Adapting Digital Pedagogy in Technical Education
13.8 Conclusion
References
14 A Novel Outcome Evaluation Model Blended with Computational Intelligence and Digital Pedagogy for UG Engineering Education
14.1 Introduction
14.1.1 Digital Pedagogy: Significance in Present Education Scenario
14.1.2 Outcome-Based Education
14.1.3 Role of Computational Intelligence in Output Prediction
14.1.4 Why Outcome Measurement Is Important in Today’s Perspective?
14.2 Literature Review
14.2.1 Related Works on Digital Pedagogy
14.2.2 Related Works on Computational Intelligence Applied to Digital Pedagogy
14.3 Measurement of Outcome
14.3.1 Relation Between PO, PSO, CO, PEO for Outcome Evaluation
14.3.2 Relevant Parameters Required for Estimation
14.3.3 Role of C.I. for Outcome Evaluation
14.4 Application of Model for Institute-Level Accreditation
14.5 Setting Guideline for Future TLP
14.6 Conclusion
14.7 Future Scope
References