Big Data and Learning Analytics in Higher Education [electronic resource] : Current Theory and Practice / edited by Ben Kei Daniel.

Contributor(s): Kei Daniel, Ben [editor.] | SpringerLink (Online service)Material type: TextTextPublisher: Cham : Springer International Publishing : Imprint: Springer, 2017Description: XX, 272 p. 56 illus., 48 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319065205Subject(s): Education | Educational technology | Education | Educational Technology | Learning & InstructionAdditional physical formats: Printed edition:: No titleDDC classification: 371.33 LOC classification: LC8-6691Online resources: e-book Full-text access
Contents:
Theory and Practice -- Global challenges in higher education -- Technological trends in higher education -- Data Science -- Big data in higher education -- Learning analytics -- Big Data Platforms and Systems -- Analytical platforms -- Systems -- Databases -- Tools -- Visualization -- Dashboards -- Measurement and Methodologies -- Measures, indicators, metrics -- Data mining techniques -- Data capture -- Data tracking -- Metadata -- Methodologies -- Institutional Best Practices -- Case studies/best practices -- Polyicy implication on learning, teaching, and research -- Challenges and opportunities -- Future Trends -- Lessons learned -- Future perspectives in big data -- Conclusions.
In: Springer eBooksSummary: This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning . Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns .
Item type Current library Collection Call number Copy number Status Notes Date due Barcode
E-Books MEF eKitap Kütüphanesi
Springer Nature LC8 -6691 (Browse shelf (Opens below)) Available NATURE 1419773-1001

Theory and Practice -- Global challenges in higher education -- Technological trends in higher education -- Data Science -- Big data in higher education -- Learning analytics -- Big Data Platforms and Systems -- Analytical platforms -- Systems -- Databases -- Tools -- Visualization -- Dashboards -- Measurement and Methodologies -- Measures, indicators, metrics -- Data mining techniques -- Data capture -- Data tracking -- Metadata -- Methodologies -- Institutional Best Practices -- Case studies/best practices -- Polyicy implication on learning, teaching, and research -- Challenges and opportunities -- Future Trends -- Lessons learned -- Future perspectives in big data -- Conclusions.

This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning . Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns .

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