The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education

Carlo Perrotta, Ben Williamson

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)

Abstract

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a ‘performative device’ that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed.

Original languageEnglish
Pages (from-to)3-16
Number of pages14
JournalLearning, Media and Technology
Volume43
Issue number1
DOIs
Publication statusPublished - 2 Jan 2018
Externally publishedYes

Keywords

  • Algorithms
  • cluster analysis
  • Learning Analytics
  • methods
  • performativity

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