Data Science Exhibits: Developing Theoretical Grounding and Practical Guidance for Museum Practitioners

数据科学展览：为博物馆从业者提供理论基础和实践指导

• 批准号: 2215060
• 负责人: Andee Rubin
• 金额: $ 29.95万
• 依托单位: TERC Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215060&HistoricalAwards=false
• 关键词: Data; Science; Exhibits; Developing; Theoretical

Data science is ever-present in modern life. The need to learn with and about data science is becoming increasingly important in a world where the quantity of data is constantly growing, where one’s own data are often being harvested and marketed, where data science career opportunities are rapidly increasing, and where understanding statistics, data sources, and data representation is integral to understanding STEM and the world around us. Museums have the opportunity to play a critical role in introducing the public to data science concepts in ways that center personal relevance, social connections and collaborative learning. However, data science and statistics are difficult concepts to distill and provide meaningful engagement with during the brief learning experiences typical to science museums. This Pilot and Feasibility study brings together data scientists, data science educators, and museum exhibit designers to consider these questions:1. What are the important data science concepts for the public to explore and understand in museum exhibits?2. How can museum exhibits be designed to support visitors with diverse backgrounds and experiences to engage with these data science concepts?3. What principles can shape these designs to promote broadening participation in data science specifically and STEM more broadly?This Pilot and Feasibility project combines multidisciplinary expert convening, feasibility testing, and early exploratory prototyping around the focal topic of data science exhibits. Project partners, TERC, the Museum of Science, Boston, and The Tech Interactive in San Jose will engage in an iterative process to develop a theoretical grounding and practical guidance for museum practitioners. The project will include two convenings, bringing together teams of experts from the fields of data science, data science education and museum exhibit design. Prior to the first convening, an initial literature summary and a survey of convening participants will be conducted, culminating in a preliminary list of big ideas about data science. Periodically, participants will have the opportunity to rank, annotate and expand this list, as a form of ongoing data collection. During the convenings, participants will explore the preliminary list, share related work from the three disciplines, engage with related data science activities in small groups, and work together to build consensus around promising data science topics and approaches for exhibits. Participant evaluation will allow for iterative improvement of the convenings and the capture of missed points or overlooked topics. After each convening, museum partners will create prototypes that respond to the convening conversations. Prototypes will be pilot tested (evaluated) with an intentionally recruited group of families that includes both frequent visitors and those who are less likely to visit the museum; diversity in terms of race, languages and dis/ability will be reflected in selection. Pilot data collection will consist of structured observations and interviews. Results from the first round of prototyping will be shared with convening participants as a way to modify the list of big ideas and to further interrogate the feasibility of communicating these ideas in an exhibit format. Results from the convenings and from both rounds of prototyping will be combined in a guiding document that will be shared on all three partner websites, and more broadly with the informal STEM learning field. The team will also host a workshop for practitioners interested in designing data science exhibits, and present at a conference focused on museum exhibits and their design.This 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. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.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.

数据科学在现代生活中无处不在。在一个数据量不断增长的世界里，学习和了解数据科学的需求变得越来越重要，在这个世界里，人们自己的数据经常被收集和销售，数据科学的职业机会正在迅速增加，并且理解统计数据，数据源和数据表示对于理解STEM和我们周围的世界是不可或缺的。博物馆有机会在向公众介绍数据科学概念方面发挥关键作用，以个人相关性，社会联系和协作学习为中心。然而，数据科学和统计学是很难提炼的概念，并在科学博物馆典型的简短学习体验中提供有意义的参与。这个试点和可行性研究汇集了数据科学家，数据科学教育工作者和博物馆展览设计师来考虑这些问题：1。在博物馆展品中，公众可以探索和理解哪些重要的数据科学概念？2.如何设计博物馆展品，以支持具有不同背景和经验的游客参与这些数据科学概念？3.哪些原则可以塑造这些设计，以促进更广泛地参与数据科学和STEM？这个试点和可行性项目结合了多学科专家召集，可行性测试和围绕数据科学展览主题的早期探索性原型设计。项目合作伙伴TERC、波士顿科学博物馆和圣何塞的科技互动将参与一个迭代过程，为博物馆从业者提供理论基础和实践指导。该项目将包括两次会议，汇集来自数据科学，数据科学教育和博物馆展览设计领域的专家团队。在第一次会议召开之前，将进行初步的文献总结和对与会者的调查，最终形成一个关于数据科学的重要想法的初步清单。作为一种持续收集数据的形式，参与者将有机会定期对这一清单进行排名、注释和扩充。在会议期间，与会者将探讨初步名单，分享三个学科的相关工作，参与小组中的相关数据科学活动，并共同努力，围绕有前途的数据科学主题和展览方法建立共识。参与者评估将允许对会议进行迭代改进，并捕捉遗漏的要点或被忽视的主题。每次会议结束后，博物馆合作伙伴将创建原型，以回应会议的对话。原型将与有意招募的一组家庭进行试点测试（评估），其中包括经常参观博物馆的人和那些不太可能参观博物馆的人;种族，语言和能力方面的多样性将反映在选择中。试点数据收集将包括结构化观察和访谈。第一轮原型设计的结果将与与会者分享，以修改大创意清单，并进一步询问以展览形式传达这些创意的可行性。会议和两轮原型设计的结果将合并在一份指导文件中，该文件将在所有三个合作伙伴网站上共享，并更广泛地与非正式STEM学习领域共享。该团队还将为有兴趣设计数据科学展品的从业者举办研讨会，并出席一个专注于博物馆展品及其设计的会议。该项目由推进非正式STEM学习（AISL）计划资助，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。这包括提供多种途径，以扩大获得和参与STEM学习经验，推进创新研究和评估的STEM学习在非正式环境中，并发展的理解，更深层次的学习参与者。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响力审查标准的支持。