Theory Informing and Arising from Learning Analytics / / edited by Kathryn Bartimote, Sarah K. Howard, Dragan Gašević

• Autore: Bartimote Kathryn
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (219 pages)
• Disciplina: 371.334
• Soggetto topico: Education - Data processing; Big data; Computers and civilization; Education; Computers and Education; Big Data; Computers and Society
• Altri autori: HowardSarah K GaševićDragan
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Theory Informing and Arising from Learning Analytics
• ISBN: 9783031605710; 3031605713
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910919824903321
• Lo trovi qui: Univ. Federico II