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永远存在。在一个不断增长的数据，经常收获和销售数据的数据，数据科学职业机会正在迅速增加，了解统计数据，数据源和数据表示是理解STEM和我们周围的世界的必不可少的地方，与数据科学学习和有关数据科学的需求变得越来越重要。博物馆有机会在以个人相关性，社交联系和协作学习的方式将公众介绍到数据科学概念中发挥关键作用。但是，数据科学和统计是很难提取并在科学博物馆典型的简短学习经验中提供有意义的参与的概念。这项飞行员和可行性研究汇集了数据科学家，数据科学教育者和博物馆展示设计师来考虑以下问题：1。公众在博物馆展览中探索和理解的重要数据科学概念是什么？2。如何设计博物馆展览来支持具有潜水员背景和经验的游客，以与这些数据科学概念相互动？3。哪些原则可以塑造这些设计，以促进对数据科学的扩大参与，并更广泛地阻止？这个飞行员和可行性项目结合了多学科专家召集，可行性测试以及围绕数据科学展览的焦点主题的早期探索性原型。 Project Partners，TERC，科学博物馆，波士顿和圣何塞的Tech Interactive将进行迭代过程，为博物馆实践者开发理论基础和实践指导。该项目将包括两个召集，将来自数据科学，数据科学教育和博物馆展览设计领域的专家团队汇集在一起​​。在第一次召开之前，将进行初始文献摘要和召集参与者的调查，最终在有关数据科学的大量思想的初步清单中。定期，参与者将有机会作为正在进行的数据收集的一种形式进行排名，注释和扩展此列表。在召集期间，参与者将探索初步列表，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，围绕有希望的数据科学主题和展览方法建立共识。参与者的评估将允许迭代改善召集，并捕获错过或被忽视的主题。每次召开后，博物馆合作伙伴将创建原型，以响应召集对话。原型将进行试点测试（评估），其中有意招募的家庭包括经常游客和那些不太可能参观博物馆的家庭；在种族，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的观察和访谈。第一轮原型制作的结果将与召集的参与者共享，以修改大型想法列表，并进一步询问以展览形式传达这些想法的可行性。召集和两轮原型制作的结果将结合在指导文档中，该文件将在所有三个合作伙伴网站上共享，并且更广泛地与非正式的STEM学习领域共享。该团队还将为有兴趣设计数据科学展览的从业人员举办一个研讨会，并参加了一场专注于博物馆展览及其设计的会议。该项目由前进的非正式STEM学习计划（AISL）计划提供资金，该计划试图推进新的方法和基于证据的方法，以及在信息环境中对STEM学习的基于证据的理解和开发。包括提供多种途径，以扩大对STEM学习经验的访问和参与度，推进对非正式环境中STEM学习的创新研究和评估，以及参与者对更深入学习的理解。该奖项反映了NSF的法定任务，并被认为是通过使用基金会的知识和更广泛影响的评估来审查Criteria来通过评估来通过评估来获得支持的。