Machine Learning in Educational Sciences: Approaches, Applications and Advances - Rilegato

Rilegato

ISBN 10: 9819993784 ISBN 13: 9789819993789

Casa editrice: Springer Nature, 2024

Riassunto

This comprehensive volume investigates the untapped potential of machine learning in educational settings. It examines the profound impact machine learning can have on reshaping educational research. Each chapter delves into specific applications and advancements, sheds light on theory-building, and multidisciplinary research, and identifies areas for further development. It encompasses various topics, such as machine-based learning in psychological assessment. It also highlights the power of machine learning in analyzing large-scale international assessment data and utilizing natural language processing for science education. With contributions from leading scholars in the field, this book provides a comprehensive, evidence-based framework for leveraging machine-learning approaches to enhance educational outcomes. The book offers valuable insights and recommendations that could help shape the future of educational sciences. 

Su questo libro

Informazioni sull?autore

Myint Swe Khine currently teaches at the School of Education, Curtin University, Australia. He has more than 30 years of experience in teacher education. He received Master's degrees from the University of Southern California, USA, University of Surrey, UK, and the University of Leicester, UK, and a Doctoral degree from Curtin University, Australia. He worked at the National Institute of Education, Nanyang Technological University, Singapore, and was a Professor at Emirates College for Advanced Education in the United Arab Emirates. He has wide-ranging research interests in teacher education, science education, learning sciences, psychometrics, measurement, assessment, and evaluation. He is a member of the Editorial Advisory Board of several international academic journals. Throughout his career, he has published over 40 edited books. The most recent volumes include Methodology for Multilevel Modelling in Education Research: Concepts and Applications (Springer, 2022), and Rhizomatic Metaphor: Legacy of Deleuze and Guattari in Education and Learning (Springer, 2023).

Dalla quarta di copertina

This comprehensive volume investigates the untapped potential of machine learning in educational settings. It examines the profound impact machine learning can have on reshaping educational research. Each chapter delves into specific applications and advancements, sheds light on theory-building, and multidisciplinary research, and identifies areas for further development. It encompasses various topics, such as machine-based learning in psychological assessment. It also highlights the power of machine learning in analyzing large-scale international assessment data and utilizing natural language processing for science education. With contributions from leading scholars in the field, this book provides a comprehensive, evidence-based framework for leveraging machine-learning approaches to enhance educational outcomes. The book offers valuable insights and recommendations that could help shape the future of educational sciences. 

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9819993784
• ISBN 13: 9789819993789
• Rilegatura: Copertina rigida
• Numero di pagine: 404
• Redattore: Khine Myint Swe
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile