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

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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Learning, Media and Technology on 10-06-2019, available online: https://www.tandfonline.com/doi/full/10.1080/17439884.2019.1623251.

    Accepted author manuscript, 229 KB, PDF document

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Original languageEnglish
Pages (from-to)31-45
Number of pages15
JournalLearning, Media and Technology
Issue number1
Early online date10 Jun 2019
Publication statusPublished - 2 Jan 2020


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.

    Research areas

  • behaviourism, behavioural economics, data science, nudging, machine learning, reinforcement learning

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