Subversive learning analytics

Alyssa Friend Wise, Juan Pablo Sarmiento, Maurice Boothe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper puts forth the idea of a subversive stance on learning analytics as a theoretically-grounded means of engaging with issues of power and equity in education and the ways in which they interact with the usage of data on learning processes. The concept draws on efforts from fields such as socio-technical systems and critical race studies that have a long history of examining the role of data in issues of race, gender and class. To illustrate the value that such a stance offers the field of learning analytics, we provide examples of how taking a subversive perspective can help us to identify tacit assumptions-in-practice, ask generative questions about our design processes and consider new modes of creation to produce tools that operate differently in the world.

Original languageEnglish (US)
Title of host publicationLAK 2021 Conference Proceedings - The Impact we Make
Subtitle of host publicationThe Contributions of Learning Analytics to Learning, 11th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages639-645
Number of pages7
ISBN (Electronic)9781450389358
DOIs
StatePublished - Apr 12 2021
Event11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021 - Virtual, Online, United States
Duration: Apr 12 2021Apr 16 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021
CountryUnited States
CityVirtual, Online
Period4/12/214/16/21

Keywords

  • Critical frameworks
  • Educational equity
  • Subversive stance

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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