P. Taylor Webb – författare

The empirical focus of this book is on the twenty year struggle by parents and members of the Black community in Toronto to introduce an Africentric Alternative School (AAS) with Black-focused curricula. It brings together a seemingly disparate series of events that emerged from equity and multicultural narratives about the establishment of the school – violence, anti-racism and race-based statistics, policy entrepreneurs, and the re-birth of alternative schools in Toronto - to illustrate how these events ostensibly functioned through neoliberal choice mechanisms and practices. Gulson and Webb show how school choice can represent and manifest the hopes and fears, contestations and settlements of contemporary racial biopolitics of education in multicultural cities.

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.

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.

Exploring the influence of AI technologies on theories of reason, cognition, learning, and educationLearning Under Algorithmic Conditions presents twenty-seven concise essays that collectively chart the shifting terrain of learning in the age of artificial intelligence. Providing historical and philosophical context, this innovative volume features prominent scholars from the fields of media studies, philosophy, and education research, who shed light on how learning has become newly envisioned, machinic, and more-than-human. The contributors unravel various histories of machine intelligence and elucidate the current impact of machine learning technologies on practices of knowledge production. Teeming with theoretical and practical insights, Learning Under Algorithmic Conditions is an interdisciplinary guide for those working across the humanities and social sciences as well as anyone interested in understanding our changing social, political, and technical infrastructures.Contributors: Craig Carson, Adelphi U; Felicity Coleman, U of the Arts London; Ed Dieterle; Shayan Doroudi, U of California, Irvine; David Gauthier, Utrecht U; Cathrine Hasse, Aarhus U; Talha Can İşsevenler, CUNY; Goda Klumbytė; Robb Lindgren, U of Illinois Urbana-Champaign; Michael Madiao; Henry Neim Osman; Luciana Parisi, Duke U; Carolyn Pedwell, Lancaster U; Arkady Plotnitsky, Purdue U; Julian Quiros, U of Pennsylvania; Sina Rismanchian; Warren Sack, U of California, Santa Cruz; R. Joshua Scannell, The New School; Gregory J. Seigworth, Millersville U; Rebecca Uliasz, U of Michigan; David Wagner, U of New Brunswick; Ben Williamson, U of Edinburgh.Retail e-book files for this title are screen-reader friendly with images accompanied by short alt text and/or extended descriptions.

Exploring the influence of AI technologies on theories of reason, cognition, learning, and educationLearning Under Algorithmic Conditions presents twenty-seven concise essays that collectively chart the shifting terrain of learning in the age of artificial intelligence. Providing historical and philosophical context, this innovative volume features prominent scholars from the fields of media studies, philosophy, and education research, who shed light on how learning has become newly envisioned, machinic, and more-than-human. The contributors unravel various histories of machine intelligence and elucidate the current impact of machine learning technologies on practices of knowledge production. Teeming with theoretical and practical insights, Learning Under Algorithmic Conditions is an interdisciplinary guide for those working across the humanities and social sciences as well as anyone interested in understanding our changing social, political, and technical infrastructures.Contributors: Craig Carson, Adelphi U; Felicity Coleman, U of the Arts London; Ed Dieterle; Shayan Doroudi, U of California, Irvine; David Gauthier, Utrecht U; Cathrine Hasse, Aarhus U; Talha Can İşsevenler, CUNY; Goda Klumbytė; Robb Lindgren, U of Illinois Urbana-Champaign; Michael Madiao; Henry Neim Osman; Luciana Parisi, Duke U; Carolyn Pedwell, Lancaster U; Arkady Plotnitsky, Purdue U; Julian Quiros, U of Pennsylvania; Sina Rismanchian; Warren Sack, U of California, Santa Cruz; R. Joshua Scannell, The New School; Gregory J. Seigworth, Millersville U; Rebecca Uliasz, U of Michigan; David Wagner, U of New Brunswick; Ben Williamson, U of Edinburgh.Retail e-book files for this title are screen-reader friendly with images accompanied by short alt text and/or extended descriptions.