000 04508nam a22005175i 4500
001 978-981-99-9379-6
003 DE-He213
005 20251030111442.0
007 cr nn 008mamaa
008 240224s2024 si | s |||| 0|eng d
020 _a9789819993796
_9978-981-99-9379-6
024 7 _a10.1007/978-981-99-9379-6
_2doi
050 4 _aLB1028-1028.25
072 7 _aJN
_2bicssc
072 7 _aEDU037000
_2bisacsh
072 7 _aJN
_2thema
245 1 0 _aMachine Learning in Educational Sciences
_h[electronic resource] :
_bApproaches, Applications and Advances /
_cedited by Myint Swe Khine.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVII, 384 p. 98 illus., 77 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aUsing 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.
520 _aThis 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. .
650 0 _aEducation
_xResearch.
_959832
650 0 _aData mining.
_96941
650 0 _aArtificial intelligence.
_958441
650 1 4 _aResearch Methods in Education.
_969694
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aKhine, Myint Swe.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_971697
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819993789
776 0 8 _iPrinted edition:
_z9789819993802
776 0 8 _iPrinted edition:
_z9789819993819
856 4 0 _uhttps://doi.org/10.1007/978-981-99-9379-6
912 _aZDB-2-EDA
912 _aZDB-2-SXED
999 _c57863
_d57863