Big Data and Learning Analytics in Higher Education Current Theory and Practice
Material type:
TextLanguage: English Publication details: Springer, 2017Description: xx, 272 pages : llustrations (some color) ; 24 cmISBN: - 9783319065199
| Item type | Current library | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Book | TBS Barcelona Libre acceso | LB2326.3 DAN (Browse shelf(Opens below)) | Available | B02155 |
Browsing TBS Barcelona shelves, Shelving location: Libre acceso Close shelf browser (Hides shelf browser)
|
|
|
|
|
|
No cover image available | ||
| LB1731.4 NIE An introduction to coaching skills | LB2326.3 BAI What the best college teachers do | LB2326.3 CLE L'ingénierie des formations en alternance | LB2326.3 DAN Big Data and Learning Analytics in Higher Education Current Theory and Practice | LB2326.3 FLI Sense of serving | LB2326.3 SIC La evaluación y calificación en la universidad | LB2331 BAI Lo que hacen los mejores profesores universitarios |
Chapter 1: Overview of Big Data and Analytics in Higher Education Chapter 2: Thoughts on Recent Trends and Future Research Perspectives in Big Data and Analytics in Higher Education-- Chapter 3: Big Data in Higher Education: The Big Picture-- Chapter 4: Preparing the Next Generation of Education Researchers for Big Data in Higher Education-- Chapter 5: Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm-- Chapter 6: The Contemporary Research University and the Contest for Deliberative Space-- Part II: LEARNING ANALYTICS-- Chapter 7: Ethical Considerations in Adopting a University- and System-Wide Approach to Data and Learning Analytics-- Chapter 8: Big Data, Higher Education and Learning Analytics: Beyond Justice, Towards an Ethics of Care-- Chapter 9: Curricular and Learning Analytics: A Big Data Perspective-- Chapter 10: Implementing a Learning Analytics Intervention and Evaluation Framework: What Works?-- Chapter 11: GraphFES: A Web Service and Application for Moodle Message Board Social Graph Extraction-- Chapter 12: Toward an Open Learning Analytics Ecosystem-- Chapter 13: Predicting Four-Year Student Success from Two-Year Student Data-- Chapter 14: Assessing Science Inquiry Skills in an Immersive, Conversation-Based Scenario-- Chapter 15: Learning Analytics of Clinical Anatomy e-Cases.--
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​.
