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.
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.
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).
