Pramod K Nayar |
Published: 29th Jan 2020
Updated: 28th Jan 2020
No, the title does not propose the usual: that kids from very early stages are addicted to smartphones, digital technology and the screen. Rather, it points to an altogether different condition: the rise of neuropedagogies and educational data science that focus, via a host of technological processes, on the child in the classroom.
Parents have for some time now been able to view school classrooms to see what their little darlings are up to. Children wearing health tracking devices that measure the number of steps taken, for instance, is a routine feature in many countries. Such biosensing is here to stay and only likely to increase as public anxieties about child safety are amplified through media-stories of school – and class – horrors.
If the school and parents subscribe to ClassDojo, parents can have realtime data on their child in the classroom. ClassDojo updates, via an app, their child’s day. Teachers can instantly update parents about homework, upcoming trips and assignments, the day’s work, and ‘encourage students for any skill or value — whether it’s working hard, being kind, helping others or something else’.
Teachers can DojoCast materials via their mobiles. This can be in the form of images, texts and sounds from the class. Students can upload their classroom work instantly for parents to see. According to the website, 95% of US schools have already adopted the app. All of these are meant to observe the child closely, and adapt pedagogies to suit the child’s needs and moods.
Next step: the measuring of emotional intelligence and the rise of ‘emotional learning analytics’. The first International Conference on Learning Analytics and Knowledge (2011) announced the new trend:
[a] focus on integrating the technical and the social/pedagogical dimensions of learning analytics. Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
We have two options here. Use biosensors on the child to measure the learner’s moods, anxieties and levels of emotional stability, or the teacher wears a head-mounted webcam for eye tracking, facial recognition in order to assess a student’s degree of engagement in the class. A prototype, EngageSense, has already been tested.
The teacher is to now possess, via measurement of biophysical metrics, complete data on the child’s emotional, learning and intellectual engagement in the classroom. Negative emotions such as distraction, anxiety or anger can be instantly documented when the teacher locates changes in the biosensory data emanating from a child – and thereby alter her/his teaching modes so as to reduce these negativities.
IBM’s Cognitive Computing for Education proposes that content and teaching methods are to be based on two factors: content to be designed with a deep understanding of underlying cognitive neuroscience and cognitive theories of learning; there has to be a closed feedback loop that assesses, informs and refreshes the principles used to design the content to ensure its efficacy.
IBM’s website on Cognitive Learning Content states the aims:
At the intersection of cognitive neuroscience and cognitive computing lies an extraordinary opportunity. This opportunity could allow us to refine cognitive theories of learning as well as derive new principles that should guide how learning content should be structured when using cognitive computing-based technologies.
Positive Behaviour Apps
The rise of such ‘positive behaviour apps’, as Ben Williamson terms it in an essay in the journal Pedagogy, Culture & Society, is the next generation of pedagogies arriving via extensive research on technology — and biology-based classroom environments, children’s bodies and emotion cultures. Broadly terming these ‘neuropedagogies’, Williamson argues that these are new techniques of social control because they instil a model of ‘correct’ and ‘good’ classroom behaviour, teaching practices, social interaction and attitudes.
Childhood is to be measured through emotional, intellectual and biological metrics before creating syllabi, recreational techniques, assessment modes and teaching processes. Determining the behaviour of both, learner and teacher, the rise of educational data science is about to transform the classroom as we know it. Aligned closely with a worldwide concern with the student’s mental health, cognitive and digital technology experts have joined forces with education theorists here.
The teacher’s responses are to account, via instant metrics, for the child’s level of emotional and intellectual engagement. The parents are to monitor, similarly, their child’s day and activities via a measurement of their biological states.
In all this, what is likely to get lost is the spontaneity of classroom dynamics, the frisson of debate and the undetermined course of any activity in the class. With measurement and metrics comes a heightened awareness, of all three actants – parent, teacher, child – in the business of education.
Williamson’s concern over the increasing biopolitics of childhood that transforms educational processes and pedagogy into systems of governance may be a bit far-fetched though: what has education been other than modes of governance and cultural training?
The classroom and the playground are likely to be self-conscious, self-governing spaces where childhood will be the site of not just censorship but also ‘sensorship’, to coin a term. ‘Play’, which by definition is not rule-bound (as different from ‘games’) will be commodified and organised because parameters like the child’s happiness on the swing or the frustration at a dropped catch on the cricket field will be monitored by biosensors and instantly transmitted to parents, administrators and teachers, who can respond accordingly.
Welcome to biodigital childhood.
(The author is Professor, Department of English, University of Hyderabad)
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