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Technology, Knowledge and Learning
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21-07-2017 | Original research

Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach

Authors: Jason M. Lodge, Sakinah S. J. Alhadad, Melinda J. Lewis, Dragan Gašević

Published in: Technology, Knowledge and Learning | Issue 3/2017

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Abstract

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention.
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Metadata
Title
Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach
Authors
Jason M. Lodge
Sakinah S. J. Alhadad
Melinda J. Lewis
Dragan Gašević
Publication date
21-07-2017
Publisher
Springer Netherlands
Published in
Technology, Knowledge and Learning / Issue 3/2017
Print ISSN: 2211-1662
Electronic ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-017-9330-3

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