Srinivasa / Saritha / Kurni | Learning, Teaching, and Assessment Methods for Contemporary Learners | Buch | 978-981-19-6733-7 | sack.de

Buch, Englisch, 342 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 546 g

Reihe: Springer Texts in Education

Srinivasa / Saritha / Kurni

Learning, Teaching, and Assessment Methods for Contemporary Learners

Pedagogy for the Digital Generation
1. Auflage 2022
ISBN: 978-981-19-6733-7
Verlag: Springer Nature Singapore

Pedagogy for the Digital Generation

Buch, Englisch, 342 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 546 g

Reihe: Springer Texts in Education

ISBN: 978-981-19-6733-7
Verlag: Springer Nature Singapore


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.
Srinivasa / Saritha / Kurni Learning, Teaching, and Assessment Methods for Contemporary Learners jetzt bestellen!

Zielgruppe


Graduate

Weitere Infos & Material


Introduction.- Crossover Learning.- Learning Through Collaborative Argumentation.- Incidental Learning.- Context-Based Learning.- Computational Thinking.- Learning by Doing.- Embodied Learning.- Adaptive Teaching/Learning.- Analytics of Emotions.- Stealth Assessment.- Pedagogy for E-learning.- Harnessing the Power of AI to Education.


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.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.