MEASURED EDUCATION: SENSING, CONFIGURING AND INTERVENING WITH ADVANCED MEDIA
AbstractEducation has long been a space of in which knowledge was created through data practices. But the ongoing datafication and digitalisation has made new forms of datafied knowledge production within educational research possible. This new form of datafied knowledge creation has shifted the sites of expertise and the authority to create educational knowledge to a more-than-human network. This panel conceptually and empirically examines the possibilities and implications that arise from the entanglement of education with advanced media such as ubiquitous sensory environments, APIs, machine learning, and codes. The panel shows how the idea of measurable and re-configurable bodies of students is being performed and stabilized through trade shows and academic conferences; it moves towards a critical analysis of different applications of facial recognition in education and the role of doubt in machine learning methods; it shows the complex involvement of advanced learning analytics through a critical examination of interrelated studies in behavioural genetics and genoeconomics looking for associations between genes and educational outcomes through bioinformatic methods; and, it examines learning and living spaces that create a situation of ubiquitous sensation and explores interventions to disrupt the technical milieu. What connects these papers is more than the spaces, ideas and practices that surround education. All contributions look at datafied knowledge about human life – whether in behavioural, physiological, emotional, or genetic form. The panel aims to show what critical education research has adopted from other disciplines, but also show how it can contribute to the wider discourse around science, technology and society.
How to Cite
Witzenberger, K., Gulson, K., Sellar, S., Williamson, B., & de Freitas, E. (2020). MEASURED EDUCATION: SENSING, CONFIGURING AND INTERVENING WITH ADVANCED MEDIA. AoIR Selected Papers of Internet Research, 2020. https://doi.org/10.5210/spir.v2020i0.11153