04765nam a22005655i 4500 978-3-031-60571-0 DE-He213 20251030114648.0 cr nn 008mamaa 241229s2024 sz | s |||| 0|eng d 9783031605710 978-3-031-60571-0 10.1007/978-3-031-60571-0 doi LB1028.43-1028.75 JNV bicssc EDU039000 bisacsh JNV thema Theory Informing and Arising from Learning Analytics [electronic resource] / edited by Kathryn Bartimote, Sarah K. Howard, Dragan Gašević. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. X, 221 p. 26 illus., 16 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Part I State of the Art Theory and Learning Analytics -- Theory and learning analytics a historical perspective -- Making bigger waves Automating theoretical coding to generate educationally -- In Conversation Gulson Anderson & Prinsloo Examining theoretical approaches and future directions for ethics in learning analytics -- Part II Theory Application in Practice Answering Questions and Increasing the Meaningfulness of Learning Analytics Research -- In Conversation Bannert Molenaar & Winne Multiple perspectives on researching and supporting self regulated learning via analytics -- Learning analytics Framework for Analysing Regulation in Collaborative Learning -- Theory and intermediate level knowledge in Multimodal Learning Analytics -- Collaborative learning theory and analytics -- Emotion theory and learning analytics A theoretical framework for capturing emotion regulation using process data -- Integrating theories of learning and social networks in learning analytics -- What could learning analytics learn from Human Computer Interaction theory -- Part III Innovative Theory Uses and Possibilities in Learning Analytics -- In conversation Baker Järvelä & Shaffer The relationship between computational methods and theory in learning analytics -- Theories all the way across The role of theory in learning analytics and the case for unified methods -- Towards a genealogical critical theory of learning analytics. Theory Informing and Arising from Learning Analytics delves into the dynamic intersection of learning theory and educational data analysis within the field of Learning Analytics (LA). This groundbreaking book illuminates how theoretical insights can transform data interpretation, reshape research methodologies, and expand the horizons of human learning and educational theory. Organized into three distinct sections, it offers a comprehensive introduction to the role of theory in LA, features contributions from leading scholars who apply diverse theoretical frameworks to their research, and explores cutting-edge topics where new theories are emerging. A standout feature is the inclusion of three “in conversation” chapters, where expert panels dive into the topics of ethics, self-regulated learning, and qualitative computation, enriched by accompanying podcasts that provide fresh, thought-provoking perspectives. This book is an invaluable resource for researchers, sparking debates on the evolving role of theory in LA and challenging conventional epistemological views. Published by Springer, it is an essential read for both aspiring and seasoned scholars eager to engage with the forefront of LA research. Education Data processing. Big data. 57216 Computers and civilization. 68216 Education. 59242 Computers and Education. Big Data. 57216 Computers and Society. 68218 Education. 59242 Bartimote, Kathryn. editor. edt http://id.loc.gov/vocabulary/relators/edt 89809 Howard, Sarah K. editor. edt http://id.loc.gov/vocabulary/relators/edt 89810 Gašević, Dragan. editor. edt http://id.loc.gov/vocabulary/relators/edt 89811 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031605703 Printed edition: 9783031605727 Printed edition: 9783031605734 https://doi.org/10.1007/978-3-031-60571-0 ZDB-2-EDA ZDB-2-SXED 66162 66162