Big Data and Learning Analytics in Higher Education
Current Theory and Practice

Editors: Ben Kei Daniel



Examines big data and learning analytics and their current state in higher education

Reports on the diversity of tools and methods associated with learning analytics     

Explores new and emerging technologies that facilitate real-time analysis of large data sets

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.

Table of contents

Front Matter

Overview of Big Data and Analytics in Higher Education

BIG DATA
• Front Matter
• Thoughts on Recent Trends and Future Research Perspectives in Big Data and Analytics in Higher Education
• Big Data in Higher Education: The Big Picture
• Preparing the Next Generation of Education Researchers for Big Data in Higher Education
• Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm
• The Contemporary Research University and the Contest for Deliberative Space

LEARNING ANALYTICS
• Front Matter
• Ethical Considerations in Adopting a University- and System-Wide Approach to Data and Learning Analytics
• Big Data, Higher Education and Learning Analytics: Beyond Justice, Towards an Ethics of Care
• Curricular and Learning Analytics: A Big Data Perspective
• Implementing a Learning Analytics Intervention and Evaluation Framework: What Works?
• GraphFES: A Web Service and Application for Moodle Message Board Social Graph Extraction
• Toward an Open Learning Analytics Ecosystem
• Predicting Four-Year Student Success from Two-Year Student Data
• Assessing Science Inquiry Skills in an Immersive, Conversation-Based Scenario
• Learning Analytics of Clinical Anatomy e-Cases

Back Matter