Inferring learning from big data: The importance of a transdisciplinary and multidimensional approach

Jason Lodge, Sakinah Alhadad, Melinda Lewis, Dragan Gasevic

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 analysis forms the basis for 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.
Original languageEnglish
Pages (from-to)385–400
JournalTechnology, Knowledge and Learning
Volume22
Issue number3
Early online date21 Jul 2017
DOIs
Publication statusE-pub ahead of print - 21 Jul 2017

Keywords / Materials (for Non-textual outputs)

  • learning analytics
  • inference
  • predictive modelling
  • transdisciplinarity

Fingerprint

Dive into the research topics of 'Inferring learning from big data: The importance of a transdisciplinary and multidimensional approach'. Together they form a unique fingerprint.

Cite this