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

这项研究的主要目标是开发、测试和完善一个模型，以描述青年如何利用关键数据做法在其社区内发展知识。关键的数据实践包括青年如何处理数据、与数据相关并围绕数据开展工作，以了解他们的世界并解决新问题。例如，在整个COVID-19大流行期间，年轻人参与了各种数据，如学校和城市的当地COVID-19仪表板、病毒传播的可视化以及描述应对长期隔离的心理健康策略的社交媒体。本研究旨在深入了解如何通过公平问题，文化和背景因素来塑造与数据相关的学习。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现有数据集的分析，重点关注年轻人对数据的参与。 来自密歇根州和华盛顿非主流社区的青年、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学习科学家一起创建数据实践模型。 此外，项目参与者还将协同制定一套原则，以指导设计注重关键数据参与的学习环境。项目团队的目标是了解人们如何理解、导航、批评和转换数据，以实现能够为个人决策和行动提供信息的授权意义。这种方法将推进该领域如何理解数据素养以及人们参与和脱离数据的原因。项目结果将有助于支持设计数据科学方面新的和公平的学习经验，以支持扩大参与。该项目将使用参与式理论构建方法，在三个基于设计的研究周期中建立推理，以支持对现有数据集的解释。第一个设计周期将借鉴对COVID-19大流行期间来自中西部和西海岸两个非主导社区的青年和家庭基于社区的关键数据实践的经验理解。后续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，将产生一个青年基于社区的批判性做法的学习模式，以社会、文化、种族、道德和政治观点为基础，建立知识。该领域迫切需要了解人们如何利用大量且往往令人困惑的数据学习科学，以便做出对日常生活和社区福祉有直接影响的决策。该项目还在模型建立过程中听取了来自非主流社区的青年、家庭和教育工作者的意见。该项目将为该领域如何更好地识别，承认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提供建议，作为持续的校外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）、北卡罗来纳州大学（格林斯伯勒）和马里兰州大学（学院公园）的多元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STEM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价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.