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

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

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)385-400
Number of pages16
JournalTechnology, Knowledge and Learning
Volume22
Issue number3
DOIs
Publication statusPublished - 1 Oct 2017
Externally publishedYes

Keywords

  • Inference
  • Learning analytics
  • Predictive modelling
  • Transdisciplinarity

Cite this