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Building a Learning Model of Youths’ Community-Based Critical Data Practices

建立青少年学习模型——基于社区的关键数据实践

基本信息

批准号：
2055166
负责人：
Angela Calabrese Barton
金额：
$ 46.54万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2055166&HistoricalAwards=false
关键词：
Building Learning Model Youths Community

项目摘要

The primary objective of this study is to develop, test and refine a model to describe how youth develop knowledge within their communities using critical data practices. Critical data practices include what youth do with, in relation to, and oriented around data to learn about their world and solve new problems. For example, throughout the COVID-19 pandemic, youth have engaged with data such as local COVID-19 dashboards for their schools and cities, visualizations of viral spread, and social media describing mental health strategies for coping with long-term isolation. This study is intended to produce insights on how learning with and about data is shaped by equity concerns, cultural, and contextual factors. This model will be grounded in the analysis of existing data sets from Michigan, Washington, North Carolina, Tennessee, and Maryland focused on youths’ engagement with data. Youth, families, and educators from non-dominant communities in Michigan and Washington will co-analyze data to create a model of data practices together with women learning scientists from diverse racial backgrounds and geographic locations. In addition, project participants will collaboratively contribute to the development of a set of principles to guide the design of learning environments focused on critical data engagement. The project team aims to understand how people make sense of, navigate, critique and transform data towards empowered meaning-making that can inform personal decisions and actions. This approach will advance how the field understands both data literacies and people’s reasons for engaging and disengaging with data. Project findings will contribute to supporting the design of new and equitable learning experiences in the data sciences in support of broadening participation. This project will build inferences to support explanations across existing datasets in three design-based research cycles using participatory approaches to theory building. The first design cycle will draw upon empirical understandings of the community-based critical data practices of youth and families from two non-dominant communities in the Midwest and West Coast during the COVID-19 pandemic. Later cycles will focus on existing data from three additional youth-based projects from the East Coast and two cities in the Southern US. Collaborative examination of data with researchers, youth and community partners will produce a learning model of youths’ community-based critical practices, grounded in social, cultural, racial, ethical, and political perspectives on knowledge building. There is an urgent need for the field to understand how people learn science with a large and often confusing volume of data in order to make decisions that have direct impact on everyday living and community well-being. This project also includes the voices of youth, families, and educators from non-dominant communities in the model-building process. This project will yield recommendations for how the field may better identify, acknowledge, and support youths’ efforts to engage critically with data and data-rich technologies as a part of ongoing, out-of-school STEM learning and development. The project will be led by researchers at the University of Michigan and will include a diverse and interdisciplinary team of learning scientists from the Universities of Michigan, Washington (Seattle), North Carolina (Greensboro) and Maryland (College Park). This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究的主要目标是开发、测试和完善一个模型，以描述青年如何使用关键数据实践在其社区内发展知识。关键数据实践包括年轻人使用数据、与数据相关和围绕数据做什么，以了解他们的世界并解决新问题。例如，在整个新冠肺炎大流行期间，年轻人都在使用数据，比如他们学校和城市的当地新冠肺炎仪表盘，病毒传播的可视化，以及描述应对长期孤立的心理健康战略的社交媒体。这项研究旨在深入了解股权、文化和背景因素是如何影响使用和关于数据的学习的。该模型将基于对密歇根州、华盛顿州、北卡罗来纳州、田纳西州和马里兰州的现有数据集的分析，这些数据集中于年轻人参与数据的情况。来自密歇根州和华盛顿州非主流社区的青年、家庭和教育工作者将共同分析数据，与来自不同种族背景和地理位置的女性一起学习科学家的数据实践模型。此外，项目参与者将协作制定一套原则，以指导以关键数据参与为重点的学习环境的设计。该项目团队的目标是了解人们如何理解、导航、批评数据，并将其转化为能够为个人决策和行动提供信息的赋权意义创造。这种方法将促进该领域如何理解数据文字以及人们参与和脱离数据的原因。项目成果将有助于支持设计新的、公平的数据科学学习经验，以支持扩大参与。该项目将在三个基于设计的研究周期中建立推论，以支持对现有数据集的解释，使用参与性方法建立理论。第一个设计周期将借鉴对新冠肺炎疫情期间中西部和西海岸两个非主要社区的青年和家庭以社区为基础的关键数据做法的经验理解。稍后的周期将侧重于来自东海岸和美国南部两个城市的另外三个以青年为基础的项目的现有数据。与研究人员、青年和社区合作伙伴合作审查数据将产生青年以社区为基础的批评做法的学习模式，以社会、文化、种族、伦理和政治观点为基础进行知识建设。这一领域迫切需要了解人们如何在大量且往往令人困惑的数据中学习科学，以便做出对日常生活和社区福祉有直接影响的决定。该项目还包括青年、家庭和来自非主要社区的教育工作者在模型建立过程中的声音。该项目将提出建议，说明该领域如何更好地识别、承认和支持青年努力将数据和数据丰富的技术作为正在进行的校外STEM学习和发展的一部分。该项目将由密歇根大学的研究人员领导，并将包括来自密歇根大学、华盛顿大学(西雅图)、北卡罗来纳大学(格林斯伯勒)和马里兰大学(学院公园)的多样化和跨学科的学习科学家团队。该项目由EHR核心研究(ECR)计划资助，该计划支持推进STEM学习和学习环境的基础研究、扩大对STEM的参与以及STEM劳动力发展的工作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。