ABSTRACT
Big data and AI have become integral to new scientific approaches to the understanding of learning and educational processes. Data-intensive forms of scientific discovery and knowledge production are promoted extensively by “learning scientists” and “learning engineers” with support from influential international institutions. Drawing extensively on technical engineering advances in knowledge discovery in disciplines including psychology, cognitive science, neuroscience, and genomics, these developments exemplify the emergence of a new science of policy-relevant research and development in education. This chapter, following the science and technology studies’ tradition of unpacking the expert practices and material apparatuses by which science makes its objects legible for intervention, provides an analysis of automated knowledge discovery as an emerging epistemology and a set of data practices in educational research. By mapping out the imaginaries, infrastructures, and practices of educational data science, the chapter traces the implications of applying learning science and engineering insights to the tasks of educational policy and practice.
