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Matthew Berland and Antero Garcia, "The Left Hand of Data: Designing Education Data for Justice" (MIT Press, 2024)

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New Books Network에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 New Books Network 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Educational analytics tend toward aggregation, asking what a “normative” learner does. In The Left Hand of Data: Designing Education Data for Justice (MIT Press, 2024, open access at this link), educational researchers Matthew Berland and Antero Garcia start from a different assumption—that outliers are, and must be treated as, valued individuals. Berland and Garcia argue that the aim of analytics should not be about enforcing and entrenching norms but about using data science to break new ground and enable play and creativity. From this speculative vantage point, they ask how we can go about living alongside data in a better way, in a more just way, while also building on the existing technologies and our knowledge of the present.

The Left Hand of Data explores the many ways in which we use data to shape the possible futures of young people—in schools, in informal learning environments, in colleges, in libraries, and with educational games. It considers the processes by which students are sorted, labeled, categorized, and intervened upon using the bevy of data extracted and collected from individuals and groups, anonymously or identifiably. When, how, and with what biases are these data collected and utilized? What decisions must educational researchers make around data in an era of high-stakes assessment, surveillance, and rising inequities tied to race, class, gender, and other intersectional factors? How are these complex considerations around data changing in the rapidly evolving world of machine learning, AI, and emerging fields of educational data science? The surprising answers the authors discover in their research make clear that we do not need to wait for a hazy tomorrow to do better today.

Jen Hoyer is Technical Services and Electronic Resources Librarian at CUNY New York City College of Technology. Jen edits for Partnership Journal and organizes with the TPS Collective. She is co-author of What Primary Sources Teach: Lessons for Every Classroom and The Social Movement Archive.

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1810 에피소드

Artwork
icon공유
 
Manage episode 418353968 series 2421444
New Books Network에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 New Books Network 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Educational analytics tend toward aggregation, asking what a “normative” learner does. In The Left Hand of Data: Designing Education Data for Justice (MIT Press, 2024, open access at this link), educational researchers Matthew Berland and Antero Garcia start from a different assumption—that outliers are, and must be treated as, valued individuals. Berland and Garcia argue that the aim of analytics should not be about enforcing and entrenching norms but about using data science to break new ground and enable play and creativity. From this speculative vantage point, they ask how we can go about living alongside data in a better way, in a more just way, while also building on the existing technologies and our knowledge of the present.

The Left Hand of Data explores the many ways in which we use data to shape the possible futures of young people—in schools, in informal learning environments, in colleges, in libraries, and with educational games. It considers the processes by which students are sorted, labeled, categorized, and intervened upon using the bevy of data extracted and collected from individuals and groups, anonymously or identifiably. When, how, and with what biases are these data collected and utilized? What decisions must educational researchers make around data in an era of high-stakes assessment, surveillance, and rising inequities tied to race, class, gender, and other intersectional factors? How are these complex considerations around data changing in the rapidly evolving world of machine learning, AI, and emerging fields of educational data science? The surprising answers the authors discover in their research make clear that we do not need to wait for a hazy tomorrow to do better today.

Jen Hoyer is Technical Services and Electronic Resources Librarian at CUNY New York City College of Technology. Jen edits for Partnership Journal and organizes with the TPS Collective. She is co-author of What Primary Sources Teach: Lessons for Every Classroom and The Social Movement Archive.

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Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology

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1810 에피소드

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