Learning, Teaching, and Assessment Methods for Contemporary Learners - (Springer Texts in Education) (Paperback)

Book Synopsis

Chapter 1. Introduction 1.1. What is Pedagogy? 1.2. Why Pedagogy is Important? 1.3. Different Types of Pedagogies 1.4. How is Pedagogy Changing? 1.5. How does pedagogy impact the learner? 1.6. Modern learning, teaching, and assessment methods 1.6.1. Crossover Learning 1.6.2. Learning Through Collaborative Argumentation 1.6.3. Incidental Learning 1.6.4. Context-Based Learning 1.6.5. Computational Thinking 1.6.6. Learning by Doing 1.6.7. Embodied Learning 1.6.8. Adaptive Teaching/Learning 1.6.9. Analytics of Emotions 1.6.10. Stealth AssessmentChapter 2. Crossover Learning 2.1. What is Crossover Learning? 2.2. Why is it good to implement? 2.3. Why is Crossover Learning Working in the Classroom? 2.4. How to implement it? 2.5. Limitations / Major Barriers and Disadvantages of adopting Crossover Learning 2.6. Case Studies
Chapter 3. Learning Through Collaborative Argumentation 3.1. What is Collaborative Argumentation? 3.2. Benefits of Argumentation 3.3. Techniques for Effective Collaborative Argumentation 3.4. Preparing Classroom Environments for Collaborative Argumentation 3.5. Case Studies
Chapter 4. Incidental Learning 4.1. What is Incidental Learning? 4.2. What is the Impact of Incidental Learning? 4.3. How to Engage with Incidental Teaching? 4.4. Incidental Learning in the Classroom 4.5. Incidental Learning and Intentional Learning 4.6. Informal and Intentional Learning 4.7. Case Studies
Chapter 5. Context-Based Learning 5.1. What is Context-Based Learning? 5.2. Why Context-Based Learning? 5.3. Context-Based Learning and Competence 5.4. Impact on Students' Learning 5.5. Creating Context-Based Learning Environments 5.6. Connecting Concepts and Contexts 5.7. Selection of Contexts 5.8. Assessment in context-based teaching and learning 5.9. Case Studies
Chapter 6. Computational Thinking 6.1. What is Computational Thinking? 6.2. Why Computational Thinking is important? 6.3. Key Skills for Computational thinking 6.4. Six Principles of Computational Thinking 6.5. Learning Strategies for Developing Computational Thinking Skills 6.6. Computational thinking in practice 6.7. Thinking computationally 6.8. Assessment of computational thinking 6.9. Case Studies
Chapter 7. Learning by Doing 7.1. What is Learning-by-doing? 7.2. Why Learning-by-doing is important? / Why is Learning-by-doing effective? 7.3. What We Learn When We Learn by Doing

From the Back Cover

This textbook tackles the matter of contemporary learners' needs, and introduces modern learning, teaching, and assessment methods. It provides a deeper understanding of these methods so that the students and teachers can create teaching and learning opportunities for themselves and others. It explores the meaning of 'pedagogy', why it is essential, and how pedagogy has evolved to take 21st-century skills and learning into account. This textbook showcases various modern learning, teaching, and assessment methods for contemporary learners in an increasingly digital environment. Each chapter presents insights and case studies that show how such modern methods can be applied to classrooms, and how they can support the existing curriculum. It shows students, educators, and researchers alike how to effectively make sense of and use modern learning, teaching, and assessment methods in everyday practice.

About the Author

Dr Srinivasa K G is a Professor in the Department of Data Science and Artificial Intelligence at the International Institute of Information Technology, Naya Raipur, India. He is the recipient of a number of awards for his scholarly work, has published more than 150 research papers at international conferences and journals, and has authored eight textbooks. Dr Srinivasa has been a visiting researcher at several universities abroad, is the Principal Investigator for a number of funded projects, and is the senior member of the Institute of Electrical and Electronics Engineers (IEEE) and Association for Computing Machinery (ACM). His research areas include data mining, machine learning, and cloud computing, innovative teaching practices in engineering education, pedagogy, outcomes-based education, and teaching philosophy. Dr Muralidhar Kurni is Associate Professor in the department of CSE at Anantha Lakshmi Institute of Technology & Sciences, Ananthapuramu, India. He received his Ph.D. in Computer Science and Engineering from JNTUA, Ananthapuram, in 2021. He is a senior member of IEEE and a professional member of ACM. His research interests include Learning Analytics, Learning Strategies, Digital Pedagogy, Design Thinking, Pedagogy refinement & Engineering Education Research. Dr Saritha K is Associate Professor, Department of Computer Science Engineering, Presidency University, Bangalore, India. Previously she worked as Principal, S.V. Degree & P.G. College, Anantapur. She has conducted several skill development activities for students at Madanapalle Institute of Technology & Science, and two of the student applications developed during the period were selected as 'Best Applications' by the Andhra Pradesh State Government. Dr Saritha K has more than twenty years of teaching experience and has presented more than 15 papers at various national & international conferences and in journals. Her research interests aredigital pedagogy, design thinking, pedagogy refinement, data mining, and machine learning.