The
idea of “learning” is changing in the data society: away from notions
of something purely “human”, towards scenarios in which machines
undertake learning independently, and increasingly structure and direct
the learning of humans.
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.