Algorithms of Education: How Datafication and Artificial Intelligence Shape Policy

Description

A critique of what lies behind the use of data in contemporary education policy    While the science fiction tales of artificial intelligence eclipsing humanity are still very much fantasies, in Algorithms of Education the authors tell real stories of how algorithms and machines are transforming education governance, providing a fascinating discussion and critique of data and its role in education policy. Algorithms of Education explores how, for policy makers, today’s ever-growing amount of data creates the illusion of greater control over the educational futures of students and the work of school leaders and teachers. In fact, the increased datafication of education, the authors argue, offers less and less control, as algorithms and artificial intelligence further abstract the educational experience and distance policy makers from teaching and learning. Focusing on the changing conditions for education policy and governance, Algorithms of Education proposes that schools and governments are increasingly turning to “synthetic governance”—a governance where what is human and machine becomes less clear—as a strategy for optimizing education. Exploring case studies of data infrastructures, facial recognition, and the growing use of data science in education, Algorithms of Education draws on a wide variety of fields—from critical theory and media studies to science and technology studies and education policy studies—mapping the political and methodological directions for engaging with datafication and artificial intelligence in education governance. According to the authors, we must go beyond the debates that separate humans and machines in order to develop new strategies for, and a new politics of, education. Cover Page Title Page Copyright Page Contents Introduction: Synthetic Governance: Algorithms of Education Chapter 1: Governing: Networks, Artificial Intelligence, and Anticipation Chapter 2: Thought: Acceleration, Automated Thinking, and Uncertainty Chapter 3: Problems: Concept Work, Ethnography, and Policy Mobility Chapter 4: Infrastructure: Interoperability, Datafication, and Extrastatecraft Chapter 5: Patterns: Facial Recognition and the Human in the Loop Chapter 6: Automation: Data Science, Optimization, and New Values Chapter 7: Synthetic Politics: Responding to Algorithms of Education Acknowledgments Notes Index About the Author