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Cited by 261Digital Education and SocietyOnline Learning and AnalyticsGlobal Educational Policies and Reforms

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies

Open access
Knox, Jeremy, Williamson, Ben, Bayne, Sian·2020·Learning Media and Technology
DOI 10.1080/17439884.2019.1623251
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Abstract

This paper examines visions of ‘learning’ across humans and machines in a near-future of intensive data analytics. Building upon the concept of ‘learnification’, practices of ‘learning’ in emerging big data-driven environments are discussed in two significant ways: the training of machines, and the nudging of human decisions through digital choice architectures. Firstly, ‘machine learning’ is discussed as an important example of how data-driven technologies are beginning to influence educational activity, both through sophisticated technical expertise and a grounding in behavioural psychology. Secondly, we explore how educational software design informed by behavioural economics is increasingly intended to frame learner choices to influence and ‘nudge’ decisions towards optimal outcomes. Through the growing influence of ‘data science’ on education, behaviourist psychology is increasingly and powerfully invested in future educational practices. Finally, it is argued that future education may tend toward very specific forms of behavioural governance – a ‘machine behaviourism’ – entailing combinations of radical behaviourist theories and machine learning systems, that appear to work against notions of student autonomy and participation, seeking to intervene in educational conduct and shaping learner behaviour towards predefined aims.

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Citations

Cites

Research works printed but not linked: 15

  1. Berry D. M., 2017, Media Theory, 1, 74
  2. Biesta Gert., 2005, Nordisk Pedagogik, 25, 54
  3. Campbell John P., 2007, Educause Review, 42, 40
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  8. Long P., 2011, EDUCAUSE Review, 46, 31
  9. OECD, 2017, Social and Emotional Skills: Well-being, Connectedness and Success
  10. O’Reilly F., 2017, Behavioural Insights for Education: A Practical Guide for Parents, Teachers and School Leaders
  11. Pea R., 2014, The Learning Analytics Workgroup: A Report on Building the Field of Learning Analytics for Personalized Learning at Scale
  12. Rose N., 2016, Future Computing and Robotics: A Foresight Report from the Human Brain Project Lab
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  14. van Barneveld Angela, 2012, ELI Paper, 2012, 1
  15. Wachter-boettch S., 2017, Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech

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Metadata

Authors
Knox, J., Williamson, B., & Bayne, S.
Affiliations
  • University of Edinburgh (GB)
  • Centre for Research in Digital Education, University of Edinburgh, Edinburgh, UK
Corresponding author
Knox, Jeremy
ORCID
Venue
Learning Media and Technology
Publisher
Informa UK Limited
Published
2019-06-10
Volume
45
Issue
1
Pages
31-45
ISSN
  • 1743-9884 (ISSN-L)
  • 1743-9892
DOI
10.1080/17439884.2019.1623251
OpenAlex
W2951066188
Cited by
261
References
47
Citations by year
2026: 46 · 2025: 46 · 2024: 34 · 2023: 46 · 2022: 39 · 2021: 34 · 2020: 15 · 2019: 1
FWCI
27.69
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Learning Media and Technology (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
Topics
  • Social Sciences › Education › Digital Education and Society
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Political Science and International Relations › Global Educational Policies and Reforms
Keywords
learnification, datafication, machine learning in education, nudging, choice architecture, behavioral economics, behaviorism, educational software design, educational data science, student independence, learner behavior, big data in education

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