Machine Learning in Educational Sciences : Approaches, Applications and Advances / edited by Myint Swe Khine.

Format:

Book

Contributor:

Khine, Myint Swe, editor.

Language:

English

Subjects (All):

Education--Research.

Data mining.

Artificial intelligence.

Research Methods in Education.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Local Subjects:

Research Methods in Education.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Physical Description:

1 online resource (389 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

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. .

Contents:

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.

Notes:

Includes bibliographical references.

ISBN:

9789819993796

9819993792