The word ‘precision’ has become a synonym for the application of data to the analysis and treatment of a wide range of phenomena. ‘Precision medicine’ describes the use of detailed patient information to individualize treatment and prevention based on genes, environment and lifestyle, while ‘precision agriculture’ has become an entire field of R&D focused on ‘engineering technology, sensor systems, computational techniques, positioning systems and control systems for site-specific application’ in the farming sector.
Precision medicine and precision farming approaches share a commitment to the collection and analysis of diverse data and scientific expertise for the purposes of highly targeted intervention. This may seem to make sense when it comes to medical diagnosis or optimizing crop production. But the production of precision may have more worrying consequences in other domains.
The recent allegations over Cambridge Analytica’s involvement in voter microtargeting through psychographic profiles has been termed ‘precision electioneering.’ Data-driven precision is therefore both a source of scientific certainty and of controversy and contestation.
Emerging interests in ‘precision education’ foresee the concerted use of learner data for purposes of implementing individualized educational practices and ‘targeted learning.’ As precision education has been described on the Blog on Learning and Development (BOLD):
Scientists who investigate the genetic, brain-based, psychological, or environmental components of learning … aim to find out as much as possible about learning, in order to accommodate successful learning tailored to an individual’s needs.
As this indicates, precision education is based on enormous ambitions. It assumes that the sciences of genes, neurology, behaviour and psychology can be combined in order to provide insights into learning processes, and to define how learning inputs and materials can be organized in ways best suited to each individual student. Advocates of precision education also suggest that complex computer programmes may be required to process these vast troves of data in order to personalize the learning experience for the individual.
The task of precision education requires the generation of ‘intimate’ data from individuals, and the constant processing of genetic, psychological, and neurological information about the interior details of their bodies and minds.
Unpacking precision education
It’s worth trying to think through what is involved in precision education, what it might look like in practice, and its implications for education policy.
In some ways, precision education looks a lot like a raft of other personalized learning practices and platform developments that have taken shape over the past few years. Driven by developments in learning analytics and adaptive learning technologies, personalized learning has become the dominant focus of the educational technology industry and the main priority for philanthropic funders such as Bill Gates and Mark Zuckerberg.
For example, the private non-profit National University (which runs concentrated online courses) has a ‘Precision Institute’ dedicated to precision education through ‘adaptive, machine learning instruction’ and ‘individualized course navigation’ using ‘real-time data generated from multiple sources of assessment tools.’ It is creating a Precision Education Platform for Personalized Learning to gather data from students in order to analyse relationships between ‘student characteristics and learning outcomes.’
A particularly important aspect of precision education as it is being advocated by others, however, is its scientific basis. Whereas most personalized learning platforms tend to focus on analysing student progress and assessment outcomes, precision education requires much more intimate data to be collected from students. Precision education represents a shift from the collection of assessment-type data about educational outcomes, to the generation of data about the intimate interior details of students’ genetic make-up, their psychological characteristics, and their neural functioning.
As such, precision education is part of a surge in interest in educational neuroscience, new psychometric techniques, and emerging advocacy for educational genomics to ‘enable educational organisations to create tailor-made curriculum programmes based on a pupil’s DNA profile.’ Researchers are already undertaking studies of the links between genes and attainment, and proposed DNA analysis devices such as ‘learning chips‘ to make reliable genetic predictions of heritable differences between children in terms of their cognitive ability and academic achievement. Cheap DNA kits for IQ testing in schools may not be far away, driven by the ‘new genetics of intelligence.’ Psychology, too, has begun ‘advancing the science and practice of precision education to enhance student outcomes.’
Two articles on the BOLD blog have made a particularly strong case for a scientific approach to precision education. BOLD is itself an initiative funded by the Jacobs Foundation. It is ‘dedicated to spreading the word about how children and young people develop and learn’, with a pronounced emphasis on ‘the science of learning,’ neuroscience, developmental psychology and genetic factors in learning, along with considerations of the technologies and programs required to ‘tailor education to children’s individual needs, taking into account biological, social and economic differences as well as differences in their upbringing. … A wide variety of disciplines – psychology, neurobiology, evolutionary biology, pediatrics, education, behavioral genetics, computer science and human-computer interaction – need to be involved.’ It is in this context that BOLD has begun to address the potential and challenges of precision education.
The precision education articles by Annie Brookman-Byrne are thoughtful and cautious, but also clearly angled towards the development of an interdisciplinary field of research. In the first post, Brookman-Byrne acknowledges that ‘We are currently a long way off from having the kinds of information needed to realise precision education’ but argues that ‘the groundwork has started’:
- Educational neuroscience is building an understanding of the science behind learning and teaching through the convergence of multiple disciplines and collaborations with educators.
- Evidence is being gathered from a diverse set of fields, which will eventually lead to a deeper understanding of the mechanisms involved in learning.
- The study of genetics is part of this investigation. Rather than something to be feared, our understanding of genes is simply another part of the puzzle in the science of learning.
- As the appetite for evidence-based practice increases, the future of teaching and learning may well be personalised education that takes into account a host of factors about the individual.
In the follow-up post, Brookman-Byrne in particular highlights how it will be ‘necessary to gather vast amounts of data’ to make precision education possible:
- This process of data collection has already begun, in the form of the many studies that aim to uncover the psychological and neurological processes that underpin learning.
- If precision education is to come to fruition, each individual learner will need to provide their own data in order to establish which type of learning materials best suit them.
- Precision education would draw on the best available evidence from a host of factors which might include test scores, genetic data, the learner’s own interests, and environmental factors.
- Precision education may also lead to greater choice for the learner – in particular, adolescents choosing which subjects to focus on later in school.
- A very strong scientific understanding of the mechanisms that influence learning will be the first step towards the realisation of precision education.
Brookman-Byrne acknowledges that it is too early to say how precision education will appear in practice, if at all. But BOLD itself has already begun to propose that neuroscience provides ‘ever-advancing technologies that allow us to image the thinking brain,’ thus enabling educational neuroscientists to ‘know more than ever before about how students learn,’ although it also cautions that ‘it’s not easy to translate these findings to the classroom.’ It has also supported researchers examining the links between genetics and educational success.
Regardless of the cautions and caveats, the sciences of the brain and the gene, as well as psychology and behavioural science, are already becoming lodged in education policy. It is easy to see the potential appeal of precision education to policymakers eager to find ‘scientific’ evidence-based solutions to educational problems. A new combination of education policy and the human sciences is currently emerging in the context of policy preoccupations with ‘what works.’ As Kalervo Gulson and P. Taylor Webb have argued, new kinds of ‘bio-edu-policy-science actors’ may be emerging as authorities in educational policy, ‘not only experts on intervening on social bodies such as a school, but also in intervening in human bodies.’
Critical approaches to precision education
Many people will find the ideas behind precision education seriously concerning. For a start, there appear to be some alarming symmetries between the logics of targeted learning and targeted advertising that have generated heated public and media attention already in 2018. Data protection and privacy are obvious risks when data are collected about people’s private, intimate and interior lives, bodies and brains. The ethical stakes in using genetics, neural information and psychological profiles to target students with differentiated learning inputs are significant too. Such concerns will be especially acute as politics press for greater emphasis on the biological determinants of learning, or as precision education approaches are developed by startup companies with dubious credentials.
Precision education also needs to be examined in considerable detail to understand the feasibility of its promises and claims.
The technical machinery alone required for precision education would be vast. It would have to include neurotechnologies for gathering brain data, such as neuroheadsets for EEG monitoring. It would require new kinds of tests, such as those of personality and noncognitive skills, as well as real-time analytics programs of the kind promoted by personalized-learning enthusiasts. Gathering intimate data might also require genetics testing technologies, and perhaps wearable-enhanced learning devices for capturing real-time data from students’ bodies as proxy psychometric measures of their responses to learning inputs and materials. By combining neurological, genetic, psychological, and behavioural data along with environmental factors and test scores, precision education is an outgrowth of current enthusiasms to ‘quantify the human condition’ while reducing human being to ‘databodies‘ of informational patterns.
Each of the technologies for the production of intimate data about students relies on complex combinations of scientific knowledge, technical innovation, business plans and social or political motivations. Some of them are likely not to be interoperable, either technically or intellectually. Just as software platforms do not always plug into each other effectively, there remain significant disciplinary cleavages between psychology, neuroscience and genetics which would need bridging for precision education to become possible. There are already concerns that precision medicine can reproduce bias and discrimination through its datasets and outcomes. Precision education data could be a similarly risky exercise in data collection and use.
In addition, brain science, genetics and psychology have all been subjected to considerable critique. Contemporary science often appears to treat the brain, the body and the mind as malleable and manipulable, able to be ‘recoded’ and ‘debugged’ in the same ways as software, as distinctions between the computational and the biological have begun to dissolve. Concerns have also been raised about ‘the new geneism’ and the potential for genetic data to reproduce ‘dangerous ideas about the genetic heritability of intelligence.’ Both old controversies in the use of genetics, neuroscience and psychology in the governing of bodies and behaviours, and new concerns about treating the body as if it were silicon, have potential for reproduction through precision education.
One productive way forward might be to approach precision education from a ‘biosocial‘ perspective. As Deborah Youdell argues, learning may be best understood as the result of ‘social and biological entanglements.’ She advocates collaborative, inter-disciplinary research across social and biological sciences to understand learning processes as the dynamic outcomes of biological, genetic and neural factors combined with socially and culturally embedded interactions and meaning-making processes. A variety of biological and neuroscientific ideas are being developed in education, too, making policy and practice more bio-inspired.
Other biosocial studies also acknowledge that ‘the body bears the inscriptions of its socially and materially situated milieu,’ being ‘influenced by power structures in society,’ and that ‘the brain is a multiply connected device profoundly shaped by social influences.’ The social gets ‘under the skin’ to impress upon the biological. As such, a biosocial approach would seek to understand precision education in both biological and social scientific terms by appreciating that the social environments in which learning takes place do in fact inscribe themselves on bodies and brains. Such an approach would view precision education as a source of power, reshaping the social environment of the school or the university in order to intervene in the biological, neurological and psychological correlates of learning.
Intimate data analytics
Whether or not precision education ever really takes off as an interdisciplinary field of R&D, let alone influences policy and practice, may itself matter very little if we recognize that many of the technologies and priorities captured in this emerging category already exist. Developments in neurotechnology, psychoinformatics and genetics technologies are already available for mining intimate data from the interior of bodies and brains. And with new developments such as neurofeedback, gene-editing and behaviour-change apps, technologies stand poised not just to mine biology, cognition and behaviour, but to tweak and modify them too. As a biosocial perspective would see it, intimate data analytics get under the skin.