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

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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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Fig. 1

(Adapted from Ackoff 1989)

Fig. 2

Adapted from Horvath and Donoghue (2016)

Fig. 3

Adapted from Lodge and Lewis (2012)

Fig. 4

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Acknowledgements

This work was supported by the Australian Research Council (JML) [Grant Number SR120300015].

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Authors and Affiliations

  1. Melbourne Centre for the Study of Higher Education, University of Melbourne, Melbourne, Australia

    Jason M. Lodge

  2. Learning Futures, Griffith University, Brisbane, Australia

    Sakinah S. J. Alhadad

  3. Learning Academy, Division of Student Learning, Charles Sturt University, Bathurst, Australia

    Melinda J. Lewis

  4. School of Education and Informatics, University of Edinburgh, Edinburgh, Scotland, UK

    Dragan Gašević

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  1. Jason M. Lodge
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  3. Melinda J. Lewis
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  4. Dragan Gašević
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Correspondence to Jason M. Lodge.

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Lodge, J.M., Alhadad, S.S.J., Lewis, M.J. et al. Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach. Tech Know Learn 22, 385–400 (2017). https://doi.org/10.1007/s10758-017-9330-3

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