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

越来越多的STEM工作人员需要数据科学和数据分析知识，包括媒体等公共使用的数据表示。该项目将是一个非营利组织和一所大学之间的合作伙伴关系，目的是在年轻人为当地和全国观众制作互动媒体时，让他们参与数据科学项目。这个名为Local Ground的技术平台允许年轻人收集、绘制和可视化数据。该模型将让学生参与动手、互动的数据科学项目，这些项目以对学生及其社区有意义的问题为基础。该项目的设置将是一个课后项目，由同行教育者和教师讲授。它将与大约300名来自STEM和STEM相关学科中代表性不足的人群和低收入学生合作。该项目还将开发和分享工具包，供其他教育工作者在其校内和课外项目中使用数据分析项目。该项目将推进“面向学生和教师的创新技术体验”（ITEST）项目的工作，以更好地理解和促进提高学生在科学、技术、工程或数学（STEM）领域追求职业的动机和能力的实践。研究问题将集中在支持青年数据科学项目的框架，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互动媒体来应用STEM内容知识。调查人员还将调查传统上在STEM专业中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。该项目将使用基于设计的迭代研究模型来了解年轻人如何发展他们的数据科学演示和产品，以及数据科学项目如何发展内容知识。他们将通过音频和视频记录、青年收集的项目和数据以及访谈来收集有关青少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.