04508nam a22005175i 4500 978-981-99-9379-6 DE-He213 20251030111442.0 cr nn 008mamaa 240224s2024 si | s |||| 0|eng d 9789819993796 978-981-99-9379-6 10.1007/978-981-99-9379-6 doi LB1028-1028.25 JN bicssc EDU037000 bisacsh JN thema Machine Learning in Educational Sciences [electronic resource] : Approaches, Applications and Advances / edited by Myint Swe Khine. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVII, 384 p. 98 illus., 77 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. . Education Research. 59832 Data mining. 6941 Artificial intelligence. 58441 Research Methods in Education. 69694 Data Mining and Knowledge Discovery. Artificial Intelligence. 58441 Khine, Myint Swe. editor. edt http://id.loc.gov/vocabulary/relators/edt 71697 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819993789 Printed edition: 9789819993802 Printed edition: 9789819993819 https://doi.org/10.1007/978-981-99-9379-6 ZDB-2-EDA ZDB-2-SXED 57863 57863