Novel sources of data about the biological processes involved in learning are beginning to surface in research on education. As the sciences of the human body have been transformed by advances in computing power and data analysis, researchers have begun explaining learning and outcomes such as school attainment and achievement in terms of their embodied underpinnings. These new approaches, however, are generating controversy, and demand up-close social science analysis to understand what processes of knowledge production are involved, as well as how they are being received in public, academic and political debates.
Late last year, the Leverhulme Trust awarded us a research project grant to study the rise of data-intensive biology in education. As we now kick off the project, I’m really pleased to be working with a great interdisciplinary team that includes Jessica Pykett, a social and political geographer at Birmingham University; Martyn Pickersgill, a sociologist of science and medicine at Edinburgh; and Dimitra Kotouza, a political sociologist joining us at Edinburgh straight from an excellent previous project on the policy networks, data practices and market-making involved in addressing the ‘mental health crisis’ in UK higher education.
The project focuses on three domains of data-intensive biology in education:
- the emergence of ‘big data’ genetics in the shape of ‘genome-wide association studies’ utilizing molecular techniques and bioinformatics technologies including biobanks, microarray chips, and laboratory robot scanners to identify complex ‘polygenic patterns’ associated with educational outcomes
- neurotechnology development in the brain sciences, such as wearable electroencephalography (EEG) headsets, neuro-imaging, and brain-computer interfaces with neurofeedback capacities, and their application in school-based experiments
- rapid advances in the development and utilization of ‘affect-aware’ artificial intelligence technologies, such as voice interfaces and facial emotion detection for interactive, personalized learning, that are informed by knowledge and practice in the psychological and cognitive sciences
We are planning to track these developments and their connections with cognate advances in the learning sciences, AI in education, and recent proposals around ‘learning engineering’ and ‘precision education’. Across this range of activities, we see a concerted effort to employ data-scientific technologies, methodologies and practices to record biological data related to learning and education, and in some cases to develop responses or interventions based on it. We’re only just starting the project with the full team in place, but a couple of very recent developments help exemplify why we consider the project important and timely.
Controversy over the genetics of education
On the very same day our Leverhulme Trust grant arrived, 6 September, The New Yorker published a 10,000 word article entitled ‘Can Progressives Be Convinced That Genetics Matters?’ Primarily a long-form profile of the psychology professor Paige Harden, the article describes the long and controversial history of behaviour genetics, a field in which Harden has become a leading voice—as signified by the forthcoming publication of her book The Genetic Lottery: Why DNA Matters for Social Equality.
The main thrust of the article is about Harden’s attempts to develop a ‘middle ground’ between right wing genetic determinists and left wing progressives. She is described in the piece as a ‘left hereditarian’ who acknowledges the role played by biology in social outcomes such as educational attainment, but also the inseparability of such outcomes from social and environmental factors (‘gene x environment bidirectionality’). The article is primarily focused on the politics of behaviour genetics, which has long been a major field of controversy even within the scientific disciplines of genetics due to its ‘ugly history’ in eugenics and scientific racism.
Judging from reactions on Twitter among genetics researchers and educators, these are problems—both disciplinary and political—which are more complex and intractable than either the article or the science lets on. Concerns remain, despite optimistic hopes of a ‘middle ground’, that new molecular behaviour genetics insights will be mobilized and reframed by ideologically-motivated groups to reinforce dangerous genetically-reductionist notions of race, gender and class.
The New Yorker profile also notes that recent developments in genome-wide association studies (GWAS) have begun producing significant findings about the connections between genes and educational outcomes. These are ‘big data’ endeavours using samples of over a million subjects and complex bioinformatics infrastructures of data analysis, and are part of a burgeoning field known as ‘sociogenomics’. Again, many of these sociogenomics studies appear informed by the left hereditarian perspective—seeing complex, biological polygenic patterns related to educational outcomes as operating bidirectionally with environmental factors, and arguing that genetically-informed knowledge can lead to better, social justice-oriented outcomes.
But educational GWAS research and polygenic scoring informed by a sociogenomics paradigm is not itself a settled science. As I began illustrating in some recent preparatory research for this project, the scientific apparatus of a data-intensive, bioinformatics-driven approach to education remains in development, is producing very different forms of interpretation, and is leading to disagreement over its pedagogic and policy implications. Even from within the field, a behaviour genetics approach to education based on big data analysis remains a fraught enterprise. Outside the field, it is prone to being appropriated to support ideological right-wing positions and as fuel to attack so-called ‘progressives’ and their ‘environmental determinism’.
The controversy over behaviour genetics and education is not new, as Aaron Panofsky has shown. As part of a long-running series of critical studies and publications on behaviour genetics, he analyzed its involvement in promoting ideas about genetically-informed education reform. Focusing in particular in the work of behaviour geneticist Robert Plomin, Panofsky notes that his vision of genetically-informed education utilizing high-tech molecular genomics technologies represents a form of ‘precision education’ modelled on ‘precision medicine’ in the biomedical field. In precision medicine, doctors ‘could use genetic and biomarker information to divide individuals into distinct diagnosis and treatment categories’. A precision education approach would ostensibly use similar information to support ‘personalization’ according to students’ ‘different genetic learning predispositions’.
According to Panofsky, however, precision medicine ‘represents an approach to health and healing very much in line with our neoliberal political times’. It focuses, he argues, ‘toward “me medicine” that seeks to improve health through high-tech, expensive, privatized, individualized, and decontextualized intervention and away from “we medicine” that aims to improve health and illness in the broad public through focusing on widely available interventions and targeting health’s social determinants’.
Thus, for Panofsky, Plomin’s vision of precision education raises the risk that ‘while genetically personalized education is represented as a tool to help educate everyone, it represents more of a “me” approach than a “we” approach’. He argues it risks deflecting attention away from other educational problems and their social determinants–such as school funding, policy instability, workforce quality and labour relations, and especially underlying inequalities and poverty–by focusing instead on the identification of individuals’ biological traits and the cultivation of ‘each individual’s genetic potential’.
Overall, The New Yorker article helps illustrate the controversies that genetics research in education may continue to generate in coming years. It also shows how advances in data-intensive bioinformatics technologies and sociogenomics theorizing are already beginning to play a role in knowledge production on educational outcomes. As the high-profile publication of Harden’s The Genetic Lottery indicates, these advances and arguments are likely to continue, albeit perhaps in different forms and with different motivations. Robert Plomin’s team, for example, argues that ‘molecular genetic research, particularly recent cutting-edge advances in DNA-based methods, has furthered our knowledge and understanding of cognitive ability, academic performance and their association’, and will ‘help the field of education to move towards a more comprehensive, biologically oriented model of individual differences in cognitive ability and learning’.
A key part of our project will involve tracking these unfolding developments in biologically oriented education, their historical threads, technical and methodological practices, and their ethics and controversies.
Engineering student-AI empathy
The second development is related to ‘affect-aware’ technologies to gauge and respond to student emotional states. Recently, the National Science Foundation awarded almost US$20m to a new research institute called the National AI Institute for Student-AI Teaming (iSAT), as part of its huge National AI Research Institutes program. One of three AI Institutes dedicated to education, iSAT is focused on ‘turning AI from intelligent tools to social, collaborative partners in the classroom’. According to its entry on the NSF grants database, it spans the ‘computing, learning, cognitive and affective sciences’ and ‘advances multimodal processing, natural language understanding, affective computing, and knowledge representation’ for ‘AI-enabled pedagogies’.
The iSAT vision of ‘student-AI teaming’—a form of human-machine collaborative learning—is based on ‘train[ing] our AI on diverse speech patterns, facial expressions, eye movements and gestures from real-world classrooms’. To this end it has recruited two school districts, totalling around 5000 students, to train its AI on their speech, gestures, facial and eye movements. The existing publications of iSAT are instructive of its planned outcomes. They include ‘interactive robot tutors’, ‘embodied multimodal agents’, and an ‘emotionally responsive avatar with dynamic facial expressions’.
The last of those iSAT examples, the ‘emotionally responsive avatar’, is based on the application of ‘emotion AI’ technology from Affectiva, an MIT Affective Computing lab commercial spin-out. The lead investigator of iSAT was formerly based at the lab, and has an extensive publication record focused on such technologies as ‘affect-aware autotutors’ and ‘emotion learning analytics’. In this sense, iSAT represents the advance of a particular branch of learning analytics and AI in education, supported by federal science funding and the approval of the leading US science agency.
Emotion AI-based approaches in education, like molecular behaviour genetics, are deeply controversial. Andrew McStay describes emotion AI as ‘automated industrial psychology’ and a form of ‘empathic media’ that takes ‘autonomic biological signals’ captured through biosensors as proxies for a variety of human affective processes and behaviours. Empathic media, he argues, aims to make ‘emotional life machine-readable, and to control, engineer, reshape and modulate human behaviour’. This biologization and industrialization of the emotions for data capture by computers therefore raises major issues of privacy and human rights. Luke Stark and Jesse Hoey have argued that ‘The ethics of affect/emotion recognition, and more broadly of so-called “digital phenotyping” ought to play a larger role in current debates around the political, ethical and social dimensions of artificial intelligence systems’. Google, IBM and Microsoft have recently begun rolling back plans for emotion sensing technologies following internal reviews by their AI ethics boards.
Over the last few years, several examples have emerged of education technology applications utilizing emotion AI-based approaches. They generally tend to provoke considerable concern and even condemnation, as part of broader public, media, industry and political debates about the role of AI in societies. Given that such technologies are already currently the subject of considerable public and political contestation, it is notable then that similar biosensor technologies are being generously supported as cutting edge AI developments with direct application in educational settings. While iSAT certainly has detailed ethical safeguards in place, some broader sociological issues remain outstanding.
The first is about the apparatus of data production involved in such efforts. iSAT employs Affectiva facial vision technology, which is itself based on the taxonomy of ‘basic emotions’ and the ‘facial action coding system’ developed in the 1970s by the psychologist Paul Ekman and colleagues. As researchers including McStay, Stark and Hoey have well documented, basic emotions and facial coding are highly contested as seemingly ‘universalist’ and mechanistic measures of the diversity of human emotional life. So iSAT is bringing highly controversial psychological techniques to bear on the analysis of student affect, in the shape of biosensor-enabled automated AI teaching partners. There remains an important social science story to tell here about the long historical development of this apparatus of affect measurement, its enrolment into educational knowledge production, and its eventual receipt of multimillion dollar federal funding.
The second concerns the implications of engineering ‘empathic’ partnerships between students and AI through so-called ‘student-AI teaming’. This requires the student being made machine-readable as a biological transmitter of signals, and a subject of empathic attention from automated interactive robot tutors. Significant issues remain to explore here too about human-machine emotional relations and the consequences for young people of their emotions being read as training data to create empathic educational media.
In the research we are planning, we aim to trace the development of such apparatuses and practices of emotion detection in education, and their consequences in terms of how students are perceived, measured, understood, and then treated as objects of concern or intervention by empathic automatons.
Bio-edu-data science
Overall, what these examples indicate is how advances in AI, data, sensor technologies, and education have merged with scientific research in learning, cognitive, and biological sciences to fixate on students’ bodies as signal-transmitters of learning and its embodied substrates. While the apparatus of affective computing at iSAT tracks external biological signals from faces, eyes, speech and gestures as traces of affect, learning and cognition, the apparatus of bioinformatics is intended to record observations at the molecular level.
The bioinformatics apparatus of genetics, and the biosensor apparatus of emotion learning analytics are beginning to play significant parts in how processes of learning, cognition and affect, as well as outcomes such as attainment and achievement, are known and understood. New biologized knowledge, produced through complex technical apparatuses by new experts of both the data and life sciences, is being treated as increasingly authoritative, despite varied controversies over its validity and its political and ethical consequences. This new biologically-informed science finds traces of learning and its outcomes in polygenic patterns and facial expressions, as well as in traces of other embodied processes.
In our ongoing research, then, we are trying to document some of the key discourses, lab practices, apparatuses, and ethical and political implications and controversies of an emerging bio-edu-data science. Bio-edu-data science casts its gaze on to students’ bodies, and even through the skin to molecular dynamics and traces of autonomic biological processes. We’ll be reporting back on this work as we go.
code acts in education 
” …. biosensor apparatus of emotion learning analytics … ”
Loads of people have fitbits etc where is the winning data application ?