— Cathy Hills (@fleurhills) March 23, 2017
— Cathy Hills (@fleurhills) March 22, 2017
Predictive analytics – will it be used for nudging?
Bradbury et al declare the project of behavioural economics is
to model the essential irrationality of choosers, and in so doing to render the flaws in their choosing predictable … then be used to make claims as to how social and economic systems might be designed to counteract individuals’ tendencies to make ‘bad’ decisions and to stimulate ‘good’ decisions.
(Bradbury, McGimpsey and Santori, 2012, p.250)
The Educause article similarly relates the concept of the nudge as a
theory which centers on prompting individuals to modify their behavior in a predictable way (usually to make wiser decisions) without coercing them, forbidding actions, or changing consequences.
These descriptions point to how ‘irrational’ student behaviour may emerge from learning analytics data to be met with helpful and gentle attempts at ‘correction’ in the students’ best interests.
It sounds plausible and paternalistic, yet whilst making a point of neither forbidding nor coercing the individual, the ‘choice architect’ or ‘policy maker’ is concerned with constructing a situation in which the ‘correct’ course of action is not only implicit, but foundational and pervasive. It is a dynamic bias-in-action under the guise of neutrality and provision of choice. Disingenuous too, because it advertises human irrationality as undesirable whilst sloping the ground towards the one choice it deems appropriate.
Bradbury et al describe this ‘liberal paternalism’ as ‘the co-option of behavioural economics for the continuity of the neoliberal project’ (p.255), with economic reasons for adoption in education settings being cited by the Educause article,
The combination of automation and nudges is alluring to higher education institutions because it requires minimal human intervention. This means that there are greater possibilities for more interventions and nudges, which are likely to be much more cost- and time-effective.
Nudging and its more coercive or punitive variations, ‘shoving’ and ‘smacking’, carry the risk of inappropriate application through, for example, misinterpreting data or disregarding contextual detail excluded from it. Worse, the attempt to correct or eliminate irrationality is dangerous when the long-term effects of doing so are unknown, when what is considered ‘irrational’ is up for question and when it is subject to the substitution of only one option by a determinedly non-neutral party. An attempt to curb our freedom to choose what is regarded by one political project as ‘incorrect’ is an incursion of human rights and those rights, particularly as they belong to students already dominated by institutional or commercialised powers, should be protected. As the article concludes,
with new technologies, we need to know more about the intentions and remain vigilant so that the resulting practices don’t become abusive. The unintended consequences of automating, depersonalizing, and behavioral exploitation are real. We must think critically about what is most important: the means or the end.
Bradbury, A., McGimpsey, I., and Santori, D. (2012). Revising rationality: the use of ‘Nudge’ approaches in neoliberal education policy. Journal of Education Policy 28 (2), pp. 247-267.
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These news items emphasise what happens when algorithms step outside our control or when even they aren’t sophisticated enough to do what we want at scale, requiring human input.
They’re also telling of the sort of responsibility we want to take for the tasks and code we create.
The school inspection has taken place. Some data amassing was required, but most of it was conducted by humans interacting with each other in the real world. How long this will remain the case is up for question if our study and discussions about learning analytics this week hail the beginning of an inevitable phenomenon. Inspections in the future might be done remotely with officials tapping in to the school’s metrics, viewing dashboards and delving into detailed individual student action plans, predictions and prescriptions carefully compiled by the code. Even the psychological temperature of the pupils will be available remotely in real time.
I have swithered all week between a reactionary distrust of learning analytics – a concept of learning by numbers and an ambition to instantiate a quantified student measured against coherent mapped knowledge domains – and an acknowledgment of the importance of research and a creeping suspicion that some of it might actually be useful, with a confession, too, that my happiness and motivation indicators do actually nudge up a little each time an automated comment on my lifestream applauds me for a great post.
I have bundled up all my LA thoughts into one post (not such a heavy call on the algorithmic burden), although I sprinkled a few little comments on infographics, LA reports and modelling the student elsewhere as well as starting to contribute to Dan’s Milanote. I started my tweetorial tweets a bit early with a question which, for me, still hangs in the air.
I feel moocs on behaviourism and neuroscience coming on 🙂
This post is linke to this one
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— Cathy Hills (@fleurhills) March 17, 2017
I prefer to question our headlong acceptance of the digital, using it as an opportunity for critique rather than be persuaded that automation is ‘just’ allowing existing situations to persist.