Brain data:Scanning, scraping and sculpting the plastic learning brain through neurotechnology

Neurotechnology is an advancing field of research and development with significant implications for education. As 'postdigital' hybrids of biological and informational codes, novel neurotechnologies combine neuroscience insights into the human brain with advanced technical development in brain imaging, brain-computer interfaces, neurofeedback platforms, brain stimulation and other neuroenhancement applications. Merging neurobiological knowledge about human life with computational technologies, neurotechnology exemplifies how postdigital science will play a significant role in societies and education in decades to come. As neurotechnology developments are being extended to education, they present potential for businesses and governments to enact new techniques of 'neurogovernance' by 'scanning' the brain, 'scraping' it for data and then 'sculpting' the brain toward particular capacities. The aim of this article is to critically review neurotechnology developments and implications for education. It examines the purposes to which neurotechnology development is being put in education, interrogating the commercial and governmental objectives associated with it and the neuroscientific concepts and expertise that underpin it. Finally, the article raises significant ethical and governance issues related to neurotechnology development and postdigital science that require concerted attention from education researchers

Mapping an emergent field of 'computational education policy': Policy rationalities, prediction and data in the age of Artificial Intelligence

Contemporary education policy involves the integration of novel forms of data and the creation of new data platforms, in addition to the infusion of business principles into school governance networks, and intensification of socio-Technical relations. In this paper, we examine how 'computational rationality' may be understood as intensifying of an instrumental set of logics in educational governance and decision making, and/or as opening up new explorations around the uncertainty and incompleteness of policy. We suggest that policy rationalities focused on prediction, transparency and data provide the conditions of possibility for Artificial Intelligence to be integrated into, and intensifyaspects of, what we term 'computational education policy

Measuring the Impact of Emerging Technologies in Education: A Pragmatic Approach

The evaluation of emerging technologies is important for their impacts to be effectively integrated into learning and teaching settings to bring the best benefit to learners and teachers. Educators, learners, parents and policymakers alike, therefore, need reliable methodologies for evaluating the effectiveness of such emerging technologies. However, the impact evaluations of technology in education are challenging. This challenge is more significant for emerging technologies, as change is the essense of emerging educational technologies. Therefore, the value of traditional impact evaluations in education require being reconsidered within this context. Here, we present a pragmatic approach to measuring the impact of emerging technologies in education which focuses on the suitability of the proposed evaluation methods and the types of evidence rather than on the hierarchy of these methods and evidence types. The approach has two main steps. First one is the creation of a clear theory of change to identify outcome measure(s) and assumptions that are behind the expected impact of the emerging technology intervention. Secondly, the identification of the type of evidence and methods to generate it that are the most appropriate for the current innovation stage of the emerging technology