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Machine learning in educational sciences = approaches, applications and advances /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in educational sciences/ edited by Myint Swe Khine.
其他題名:	approaches, applications and advances /
其他作者:	Khine, Myint Swe.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xvii, 384 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Educational applications. -
電子資源:	https://doi.org/10.1007/978-981-99-9379-6
ISBN:	9789819993796

Machine learning in educational sciences = approaches, applications and advances /
Machine learning in educational sciencesapproaches, applications and advances /[electronic resource] :edited by Myint Swe Khine. - Singapore :Springer Nature Singapore :2024. - xvii, 384 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9789819993796

Standard No.: 10.1007/978-981-99-9379-6doiSubjects--Topical Terms:

567577
Artificial intelligence
--Educational applications.

LC Class. No.: LB1028.43

Dewey Class. No.: 371.334631

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520 $a 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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