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Machine Learning in Educational Sciences: Approaches, Applications and Advances de Myint Swe Khine

Machine Learning in Educational Sciences: Approaches, Applications and Advances

Editat de Myint Swe Khine
en Limba Engleză Hardback – 25 feb 2024
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. 
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Specificații

ISBN-13: 9789819993789
ISBN-10: 9819993784
Pagini: 384
Ilustrații: XVII, 384 p. 97 illus., 77 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.74 kg
Ediția:2024
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore

Cuprins

Using machine learning in educational research.- Machine learning approaches to predict non-completion in AP statistics courses.- Predicting student attrition in university courses.- Machine learning based identification strategy of circumstances in the analysis of inequality of opportunity.- Machine learning applications for early and on-going warning systems in education.- Using neural networks for analyzing large-scale international assessment data.- Utilizing natural language processing and large language models in science education.- Machine based learning in psychological assessment.- Applying topic modeling to understand assessment practices of U.S. College instructors in response to the COVID-19 pandemic.- Penalized regression in educational large-scale assessments.- Applying machine learning to augment the design and assessment of immersive learning experience.- Automatic creation of concept maps to generate ‘Learning Coefficients’ in adaptive assessments.- Camelot: A council of machine learning strategies to enhance teaching.- Research on blended learning achievement improvement based on integrated machine learning methods.- Exploring non-cognitive factors affecting students’ academic performance based on PISA data: from econometrics to machine learning.- ChatGPTing the path to K12 educational reform: Examining Generative AI in the middle east from an industry perspective.- Exploring the integration of machine learning in mathematics classrooms: A literature review and recommendations for implementation.- Identification of students at risk of low performance or failure by combining enhanced machine learning, and knowledge graph techniques.

Notă biografică

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).

Textul de pe ultima copertă

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. 

Caracteristici

Focuses on real-world applications of machine learning in education Discusses the potential of machine learning for improving education and training processes Promotes ongoing research and the refinement of machine learning methodologies in the context of education