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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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コンテンツは New Books Network によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、New Books Network またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.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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1751 つのエピソード

Artwork
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Manage episode 418348889 series 2421427
コンテンツは New Books Network によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、New Books Network またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.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.

Learn more about your ad choices. Visit megaphone.fm/adchoices

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

  continue reading

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