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Big Data and Learning Analytics in Higher EducationCurrent Theory and PracticeEditors: Kei Daniel, Ben (Ed.) |
| INTRODUCTION Institutions of higher education operate in turbulent and competitive environments. Pressure from political, cultural, economic, and technological factors can influence change at virtually every level of the system, from curriculum design and student learning to national provisioning and governance of education. Growing regulatory demands for transparency and accountability, coupled with declining support from government, business and the private sector, mean institutions need to develop new ways of thinking, research theories, and approaches to manage increasingly complex challenges.Big Data is a set of approaches, techniques and models which have already captured the attention of many fields due to their potential to transform management decision-making and governance theories and practices. The ability to harness large, complex and incongruent data sets offers higher education providers new opportunities to gather evidence on their processes in order to address and predict changes in their environment. Put simply, Big Data allows institutions to see patterns that have, until now, remained hidden. It can provide insights into the growing data available from various sources, and can help institutions understand the complexity of influences on student-related outcomes, teaching and the ‘what-if questions’ of research experimentation.OBJECTIVES This book satisfies the need for a comprehensive understanding of the current state of Big Data and Learning Analytics within Higher Education. Order your copy from Springer Online:  |
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