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

越来越多的STEM劳动力需要数据科学和数据分析的知识，包括在媒体渠道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伙伴关系，目的是让青年参与数据科学项目，为当地和国家受众制作互动媒体。技术平台Local Ground使青年能够收集、绘制和可视化数据。该模型将让学生参与实践，互动的数据科学项目，这些项目基于对学生及其社区有意义的问题。 该计划的设置将是一个由同龄教育工作者和教师讲授的课后计划。它将与大约300名来自STEM和STEM相关学科代表性不足的人口以及低收入学生的青年合作。该项目还将开发和共享工具包，供其他教育工作者在校内和课后计划中使用数据分析项目。该项目将推进学生和教师创新技术体验（ITEST）计划的努力，以更好地理解和促进提高学生在科学，技术，工程或数学（STEM）领域追求职业生涯的动机和能力的实践。研究问题将集中在支持青年参与数据科学项目的支架，学生如何在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式媒体来应用STEM内容知识。调查人员还将询问传统上被STEM专业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。该项目将使用基于迭代设计的研究模型来了解青年如何开发他们的数据科学演示和产品，以及数据科学项目如何开发内容知识。他们将收集有关青年与技术互动的数据，使用音频和视频记录，青年收集的项目和数据以及访谈。

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.