Machine Behaviouralism Dr Jeremy Knox Data Controversies 2019
From James Stewart on March 13th, 2019
In recent years, the project of education has been reframed as one of ‘learning’. In other words, while education has previously privileged a focus on the practice of teaching, and the role of the teacher, as ways of understanding the educational process, these concerns have gradually shifted towards ‘learning’ and the ‘learner’. However, while the focus on learning might be seen as beneficial in promoting the autonomy and independence of individuals, it’s alignment with marketised and transactional models of education has also been questioned, where the responsibility of the institution is diminished, and students are assumed to be entrepreneurial ‘life-long learners’. Nevertheless, this vision of learning seems to be one in which students are ‘centred’, have agency, and are to all intents and purposes ‘in control’ of the educational process.
This paper will suggest that the understanding and practice of ‘learning’ is set to shift again, as we progress into a 21st century of increasing ‘datafication’. Where educational institutions appear to be adopting data-driven approaches - involving the collection of ‘big data’ from student activities, and the designing of powerful interventions in learner behaviours through the use of sophisticated software – the extent to which students are ‘centred’ in the educational process is becoming questionable. Two significant forms of ‘learning’ are discussed: the training of machines (so called ‘machine learning’), and the nudging of human decisions through digital choice architectures. In both these examples, and through the growing influence of ‘data science’ on education, behaviourist psychology is suggested to be increasingly and powerfully invested in future educational practices. Finally, this paper will argue 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.