Achieving greater educational impact through data intelligence. Practice, challenges and expectations of education.

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Autor/inn/en	Wu, Bian; Hu, Yiling; Gu, Xiaoqing
Titel	Achieving greater educational impact through data intelligence. Practice, challenges and expectations of education.
Quelle	Hackensack, NJ: World Scientific (2022), XV, 191 S.Verfügbarkeit
Reihe	East china normal university scientific reports. 15
Beigaben	Illustrationen; Literaturangaben
Sprache	englisch
Dokumenttyp	gedruckt; Monographie
ISSN	2382-5715
ISBN	9789811232909 (gebundene Ausgabe); 9780000989840 (Taschenbuch); 9789811232916 (E-Book)
Schlagwörter	Education; Data processing; Research; Methodology; Aims and objectives; Bildungstheorie; Bildungspraxis + Suchen Sie Ihr Suchwort? Bildung; Erziehung; Datenverarbeitung; Forschung; Methodologie; Aim; Aims; Objectives; Zweck; Ziel; Ziele; Zielsetzung
Abstract	Chapter 1: Foundation of data intelligence in education -- History of data intelligence -- Computer-managed instruction -- Data-driven decision making --Educational data mining -- Learning analytics -- Definition and characteristics of educational data intelligence -- Definition of data intelligence -- Data types and its origin in education system -- Disciplines of data intelligence -- Data intelligence for different educational stakeholders -- From education management to education governance -- From experience-based to evidence-based teaching -- From standardized to personalized learning -- Summary -- Reference -- Chapter 2: Data intelligence in education governance -- Introduction -- Governing complex education systems in big data era -- Ecological view of education systems -- Data-driven changes in education governance -- Key themes of data-driven education governance -- Policy making in intelligent education governance -- Data-driven policy making -- Digital policy instruments and infrastructures -- Strategies for bringing about changes in policy making process -- Data-enabled education governance from school perspective -- Framework of data-driven school governance -- Practice model for data-driven school governance -- Sustainable implementation of data-driven school governance -- System Dynamics method for governing education systems -- System dynamics method and application to education system -- Cases 1: a system-level model of education governance -- Cases 2: a school-level model of education governance -- Summary -- Reference -- Chapter 3: Data-driven teaching practice -- Introduction -- From experience-based to evidence-based teaching practice -- Learning analytics for learning design -- Learning analytics for course implementation -- Learning analytics of course evaluation -- Data-empowered learning assessment --Assessment of learning and for learning -- Automated assessment technology -- E-learning portfolio -- Multimodal data for learning assessment -- Data literacy for teacher -- What is data intelligence for teaching practice? -- What ability teachers need to develop? -- The developing pathway of data intelligence for teaching -- Case studies -- Social network analysis in online collaborative learning -- Preview analytics of flipped classroom -- Precise teaching in smart classroom -- Summary -- Reference -- Chapter 4: Data-empowered student learning -- Introduction -- Modeling learner and understanding learning -- Learners' characteristics to model -- Learner modeling techniques -- Learning prediction and risk assessment -- What is being predicted? -- Prediction techniques --Personalized and adaptive learning support -- Learning support of intelligent tutoring systems -- Techniques applied in intelligent tutoring systems -- Summary -- References -- Chapter 5: Impacts, challenges and future directions -- Impacts of data intelligence on education system -- Impacts on educational governance -- Impacts on teaching practice -- Impacts on student learning -- Challenges of data intelligence in education system -- Challenges in education governance -- Challenges in teaching practice -- Challenges in learning understanding -- Future directions. "What is data intelligence? How can data intelligence influence education system systematically? The paradigm shift of scientific research implies a coming age of data-driven educational research and practice. This book presents research and practice of data intelligence in education from three levels: (i) educational governance, (ii) teaching practice, and (iii) student learning. Each chapter gives an analysis of fundamental knowledge, key themes, the state-of-the-art technologies and education application cases. This interdisciplinary book is essential reading for anyone interested in applying big data technology in education and for different stakeholders including education administrators, teachers, students, and researchers to broaden their minds to wisely use educational data to solve complex problems in the education field"--Provided by publisher.
Erfasst von	Library of Congress, Washington, DC
Update	2023/1/02