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.

这项研究的主要目的是开发，测试和完善模型，以描述青年如何使用关键数据实践在社区中发展知识。关键数据实践包括青年人与数据有关并围绕数据而定向的事情，以了解其世界并解决新问题。例如，在整个Covid-19大流行期间，青年都从事数据，例如当地的Covid-19仪表板，用于其学校和城市，病毒传播的可视化以及社交媒体描述了应对长期隔离的心理健康策略。这项研究旨在对数据的学习和关于数据的学习方式如何受公平关注，文化和上下文因素的影响产生见解。该模型将基于对密歇根州，华盛顿，北卡罗来纳州，田纳西州和马里兰州的现有数据集的分析，该数据集中着重于年轻人参与数据。来自密歇根州和华盛顿的非主导社区的青年，家庭和教育者将共同分析数据，以与来自潜水员种族背景和地理位置的女性学习科学家一起创建数据练习模型。此外，项目参与者将合作为制定一系列原则做出贡献，以指导专注于关键数据参与的学习环境的设计。项目团队旨在了解人们如何理解，导航，批评和转化数据，以赋予能够为个人决策和行动提供依据的意义创造。这种方法将推进该领域如何理解数据文学和人们参与数据的理由。项目发现将有助于支持数据科学中新的公平学习经验的设计，以支持扩大参与。该项目将使用参与理论构建的参与方法来建立推论，以支持三个基于设计的研究周期中现有数据集的解释。第一个设计周期将借鉴对社区基于社区的批判性数据实践的经验理解，这些数据实践的青年和家庭来自中西部和西海岸的两个非职业社区的批判性数据实践。后来的周期将重点关注来自东海岸和美国南部两个城市的其他三个基于青年项目的现有数据。与研究人员，青年和社区合作伙伴对数据进行的合作检查将产生一种基于社区的批判性实践的学习模型，该模型基于知识建设的社会，文化，种族，道德和政治观点。该领域迫切需要了解人们如何使用大量且常常令人困惑的数据学习科学，以便做出直接影响日常生活和社区福祉的决策。该项目还包括在模型建设过程中的青年，家庭和教育者的声音。该项目将为该领域如何更好地识别，认可和支持年轻人批判性地参与数据和数据丰富的技术的努力而提出建议，这是正在进行的，校外的STEM学习和开发的一部分。该项目将由密歇根大学的研究人员领导，包括来自密歇根州，华盛顿大学（西雅图），北卡罗来纳州（格林斯伯勒）和马里兰州（大学公园）的潜水员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，该计划支持为STEM学习和学习环境的基础研究，扩大STEM的参与以及STEM劳动力发展的基础研究的工作。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来审查Criteria，通过评估来通过评估来获得支持。