Artwork

المحتوى المقدم من Marshall Poe. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Marshall Poe أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
Player FM - تطبيق بودكاست
انتقل إلى وضع عدم الاتصال باستخدام تطبيق Player FM !

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

52:54
 
مشاركة
 

Manage episode 418353964 series 2421486
المحتوى المقدم من Marshall Poe. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Marshall Poe أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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/education

  continue reading

989 حلقات

Artwork
iconمشاركة
 
Manage episode 418353964 series 2421486
المحتوى المقدم من Marshall Poe. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Marshall Poe أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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/education

  continue reading

989 حلقات

كل الحلقات

×
 
Loading …

مرحبًا بك في مشغل أف ام!

يقوم برنامج مشغل أف أم بمسح الويب للحصول على بودكاست عالية الجودة لتستمتع بها الآن. إنه أفضل تطبيق بودكاست ويعمل على أجهزة اندرويد والأيفون والويب. قم بالتسجيل لمزامنة الاشتراكات عبر الأجهزة.

 

دليل مرجعي سريع