Theory Informing and Arising from Learning Analytics
Kathryn Bartimote (Hrsg.), Dragan Gašević (Hrsg.), Sarah K. Howard (Hrsg.)
Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines, Lexika
Beschreibung
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 revolutionize 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.
Kundenbewertungen
Data-Driven Learning, Predictive Modelling, Social Network Analysis, Quantitative Ethnography, Multimodal Data, History of Learning Analytics, Collaborative Learning, Ethical Learning Analytics, Emotion Regulation (ER), Critical Theory, Sociological Perspectives, Socially Shared Regulation (SSRL), Human-Computer Interaction (HCI) Theory, Machine Learning, Theoretical Foundations for Research, Computational Methods, Self-Regulated Learning (SRL), Advanced Learning Technologies (ALTs), Educational Data Mining (EDM), Educational Research