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Offers practical advice on using and improving assessment for learning in the classroom.
Offers practical advice on using and improving assessment for learning in the classroom.
The process of technological change takes a wide variety of forms. Propositions that may be accurate when referring to the pharmaceutical industry may be totally inappropriate when applied to the aircraft industry or to computers or forest products. The central theme of Nathan Rosenberg's new book is the idea that technological changes are often 'path dependent', in the sense that their form and direction tend to be influenced strongly by the particular sequence of earlier events out of which a new technology has emerged. The book advances the understanding of technological change by explictly recognising its essential diversity and path-dependent nature. Individual chapters explore the particular features of new technologies in different historical and sectoral contexts. This book presents a unique account of how technological change is generated and the processes by which improved technologies are introduced.
Every day, corporations are connecting the dots about our personal behavior—silently scrutinizing clues left behind by our work habits and Internet use. The data compiled and portraits created are incredibly detailed, to the point of being invasive. But who connects the dots about what firms are doing with this information? The Black Box Society argues that we all need to be able to do so—and to set limits on how big data affects our lives. Hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only scratched the surface of this troubling behavior. Frank Pasquale exposes how powerful interests abuse secrecy for profit and explains ways to rein them in. Demanding transparency is only the first step. An intelligible society would assure that key decisions of its most important firms are fair, nondiscriminatory, and open to criticism. Silicon Valley and Wall Street need to accept as much accountability as they impose on others.
English Inside the Black Box is an easy-to-follow booklet offering great advice and guidance on how to develop formative assessment in English.
An innovative investigation of the inner workings of Spotify that traces the transformation of audio files into streamed experience. Spotify provides a streaming service that has been welcomed as disrupting the world of music. Yet such disruption always comes at a price. Spotify Teardown contests the tired claim that digital culture thrives on disruption. Borrowing the notion of “teardown” from reverse-engineering processes, in this book a team of five researchers have playfully disassembled Spotify's product and the way it is commonly understood. Spotify has been hailed as the solution to illicit downloading, but it began as a partly illicit enterprise that grew out of the Swedish file-sharing community. Spotify was originally praised as an innovative digital platform but increasingly resembles a media company in need of regulation, raising questions about the ways in which such cultural content as songs, books, and films are now typically made available online. Spotify Teardown combines interviews, participant observations, and other analyses of Spotify's “front end” with experimental, covert investigations of its “back end.” The authors engaged in a series of interventions, which include establishing a record label for research purposes, intercepting network traffic with packet sniffers, and web-scraping corporate materials. The authors' innovative digital methods earned them a stern letter from Spotify accusing them of violating its terms of use; the company later threatened their research funding. Thus, the book itself became an intervention into the ethics and legal frameworks of corporate behavior.
From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity. Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life. This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture. Visit the book's free Discussion Guide here.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The new field of learning design has the potential to revolutionize not only technology in education, but the whole field of teaching and learning through the application of design thinking to education. Learning Design looks inside the "black box" of pedagogy to understand what teachers and learners do together, and how the best teaching ideas can be shared on a global scale. Learning design supports all pedagogical approaches, content areas, and fields of education. The book opens with a new synthesis of the field of learning design and its place in educational theory and practice, and goes on to explore the implications of learning design for many areas of education—both practical and theoretical—in a series of chapters by Larnaca Declaration authors and other international experts.