Collaborative Proposal: From Data To Awesome (D2A): Youth Learning to be Data Scientists

STEM工作人员越来越需要数据科学和数据分析的知识，包括公共使用的数据表示，如媒体。该项目将是一个非营利性组织和一所大学之间的伙伴关系，目的是让青年参与数据科学项目，因为他们为当地和国家观众制作互动媒体。这个名为Local Ground的技术平台允许年轻人收集、绘制地图并将数据可视化。该模式将让学生参与动手的、互动的数据科学项目，这些项目的基础是对学生及其社区有意义的问题。该项目的背景是由同行教育者和教职员工教授的课外项目。它将与大约300名青年合作，这些青年来自通常在STEM和STEM相关学科中任职人数较少的人群和低收入学生。该项目还将为其他教育工作者开发和共享工具包，在他们的课内和课后项目中使用数据分析项目。该项目将推动创新技术体验(ITEST)计划的努力，以更好地了解和促进提高学生在科学、技术、工程或数学(STEM)领域追求职业生涯的动机和能力的实践。研究问题将集中在数据科学项目中支持年轻人的支架，学生如何在他们的写作中嵌入数据驱动的论证，以及他们如何通过创建数据丰富的交互媒体来应用STEM内容知识。调查人员还将询问STEM专业中传统上被边缘化的年轻人参与数据科学的障碍，以及工具如何支持这些学生学习数据分析和可视化。该项目将使用以迭代设计为基础的研究模式，以了解青年如何开发他们的数据科学演示文稿和产品，以及数据科学项目如何开发内容知识。他们将使用音频和视频记录、青年收集的项目和数据以及访谈来收集关于青年与该技术互动的数据。

Increasingly, the STEM workforce requires knowledge of data science and data analysis including representations of data for public use such as in media outlets. The project will be a partnership between a non-profit organization and a university in order to engage youth in data science projects as they produce interactive media for local and national audiences. The technology platform, Local Ground, allows youth to collect, map and visualize data. The model will engage students in hands-on, interactive data science projects grounded in questions that are meaningful to the students and their communities. The setting for the program will be an after-school program taught by peer-educators and faculty. It will work with approximately 300 youth from populations typically underrepresented in STEM and STEM-related disciplines and low-income students. The project will also develop and share toolkits for other educators using data analysis projects in their in-school and after-school programs. This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM).The research questions will focus on scaffolds for supporting youth in data science projects, how students embed data-driven argumentation in their writing, and how they apply STEM content knowledge through creating data-rich interactive media. The investigators will also inquire about barriers to engagement in data science for young people traditionally marginalized in STEM professions and how tools can support such students as they learn about data analysis and visualization. The project will use an iterative design-based research model to understand how youth develop their data science presentations and products and how the data science projects develop content knowledge. They will collect data about youth interaction with the technology using audio and video recording, projects and data collected by the youth, and interviews.