Data Science Learning Experiences for Middle School-aged Girls in Informal Gaming Clubs

数据在人们生活的各个方面越来越重要，从日常生活到职业生涯和公民参与。让青年人做好使用数据回答问题和解决问题的准备，使他们能够作为知情公民参与社会，并为21世纪的职业机会打开大门。确保数据素养和数据科学职业的公平代表性至关重要。 对于许多在STEM中代表性不足的女孩来说，发展“数据科学身份”需要个人有意义的数据工作经验。该项目旨在通过一种与身份一致的、基于社交游戏的学习方法，促进中学生对数据科学的兴趣和愿望。我们的目标是创造一个更加多样化和包容的一代数据科学家，他们将数据视为一种资源，并具备使用数据解决实际问题所需的技能和部署。该研究团队将管理10个社交俱乐部和10个数据科学俱乐部，由迈阿密大学数据科学与计算研究所招募的数据科学女性指导。参与者将是250名在佛罗里达州迈阿密和加利福尼亚州约洛县通过当地和全国女童组织招募的中学适龄女童。青少年将参加数据科学俱乐部，并将学习关键的数据科学概念和技能，包括数据结构，存储，探索，分析和可视化。这些概念将通过使用他们自己以个人有意义的方式收集的数据以及使用同一社交游戏生态系统中其他人收集的数据来学习。该项目还将开发促进者材料，使成年志愿者能够为他们所在地区的青年创造基于游戏的非正式数据科学学习体验。该项目由推进非正式STEM学习（AISL）计划资助，该计划旨在推进非正式环境中STEM学习设计和开发的新方法和基于证据的理解，并由学生和教师创新技术体验（ITEST）共同资助，该项目旨在让代表性不足的学生参与技术丰富的学习环境，包括数据素养技能，研究人员将重点关注两个主要的研究问题：1）在游戏和俱乐部体验中，参与者以何种方式与数据互动，以追求个人或社会目标？2)游戏玩法和俱乐部体验如何塑造女孩对数据、数据科学的看法，以及她们与数据和数据科学的契合度？该项目将使用基于设计的研究方法来迭代设计游戏和社交俱乐部体验。为了确保数据的使用对年轻女孩有个人和社会意义，虚拟世界的目标，叙述和活动将与数据科学中代表性不足的群体的女孩共同设计。该项目将研究两种非正式的基于游戏的学习场景中的游戏数据参与：有机的，自我导向的，社交游戏俱乐部和结构化的，成人促进的数据科学俱乐部。研究将采用定量和定性相结合的方法，包括调查，焦点小组，访谈，游戏和俱乐部观察。项目评估将确定游戏和俱乐部体验如何影响参与者对数据丰富的未来的态度和兴趣。该项目有可能通过俱乐部参与将基于游戏的学习与丰富的社交和成人指导相结合，从而扩大参与并促进对数据科学的兴趣。研究结果将通过会议报告、学术出版物和社交媒体传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Data is increasingly important in all aspects of people’s lives, from the day-to-day, to careers and to civic engagement. Preparing youth to use data to answer questions and solve problems empowers them to participate in society as informed citizens and opens doors to 21st century career opportunities. Ensuring equitable representation in data literacy and data science careers is critical. For many girls underrepresented in STEM, developing a "data science identity" requires personally meaningful experiences working with data. This project aims to promote middle school-aged girls’ interest and aspirations in data science through an identity-aligned, social game-based learning approach. The goals are to create a more diverse and inclusive generation of data scientists who see data as a resource and who are equipped with the skills and dispositions necessary to work with data in order to solve practical problems. The research team will run 10 social clubs and 10 data science clubs mentored by women in data science recruited through the University of Miami’s Institute for Data Science and Computing. Participants will be 250 middle school-aged girls recruited in Miami, FL, and Yolo County, CA, through local and national girls’ organizations. Youth will participate in a data science club and will learn key data science concepts and skills, including data structures, storage, exploration, analysis, and visualization. These concepts will be learned from working with their own data collected in personally meaningful ways in addition to working with data collected by others in the same social game eco-system. The project will also develop facilitator materials to allow adult volunteers to create game-based informal data science learning experiences for youth in their areas. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments and is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST), which seeks to engage underrepresented students in technology-rich learning environments, including skills in data literacy, and increase students’ knowledge and interest in information and communication technology (ICT) careers.Researchers will focus on two primary research questions: 1) Across gameplay and club experiences, in what ways do participants engage with data to pursue personal or social goals? 2) How do gameplay and club experiences shape girls’ perceptions of data, data science, and their fit with data and data science? The project will use design-based research methods to iteratively design the game and social club experiences. To ensure that uses of data feel personally and socially meaningful to young girls, the virtual world’s goals, narratives, and activities will be co-designed with girls from groups underrepresented in data science. The project will research engagement with game data in two informal, game-based learning scenarios: organic, self-directed, social play club, and structured, adult-facilitated data science clubs. The research will use a combination of quantitative and qualitative methods including surveys, focus groups, interviews, and gameplay and club observations. Project evaluation will determine how gameplay and club experiences impact participants' attitudes toward and interest in data-rich futures. The project holds the potential for broadening participation and promoting interest in data science by blending game-based learning with the rich social and adult mentoring through club participation. The results will be disseminated through conference presentations, scholarly publications, and social media. The game and facilitator materials will be designed for dissemination and made freely available to the public.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.