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

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，学习数据科学的需求变得越来越重要，在这个世界里，一个人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，理解统计、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。博物馆有机会以个人关联、社会联系和协作学习为中心，在向公众介绍数据科学概念方面发挥关键作用。然而，在科学博物馆的简短学习体验中，数据科学和统计是难以提炼和提供有意义的参与的概念。这个试点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览设计师来考虑这些问题：在博物馆展览中，有哪些重要的数据科学概念值得公众探索和理解？如何设计博物馆展品以支持具有不同背景和经验的参观者参与这些数据科学概念？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更广泛的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，以及围绕数据科学展览的焦点主题的早期探索性原型。项目合作伙伴TERC、波士顿科学博物馆和圣何塞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。在第一次会议之前，将进行初步的文献总结和对会议参与者的调查，最终形成一份关于数据科学的重要思想的初步清单。定期，参与者将有机会对该列表进行排名、注释和扩展，作为持续数据收集的一种形式。在会议期间，与会者将探索初步清单，分享三个学科的相关工作，以小组形式参与相关数据科学活动，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。参与者评价将允许对会议进行迭代改进，并捕获错过的要点或被忽视的主题。在每次召集之后，博物馆合作伙伴将创建响应召集对话的原型。原型将在一组有意招募的家庭中进行试点测试（评估），其中包括经常参观博物馆的家庭和不太可能参观博物馆的家庭；种族、语言和残疾方面的多样性将反映在选择中。试点数据收集将包括有组织的观察和访谈。第一轮原型设计的结果将与召集参与者分享，作为修改大创意列表的一种方式，并进一步探讨以展览形式传达这些想法的可行性。会议和两轮原型设计的结果将合并在一份指导性文件中，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。该团队还将为对设计数据科学展品感兴趣的从业者举办一个研讨会，并在一个以博物馆展品及其设计为重点的会议上发表演讲。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学习的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，扩大STEM学习经验的获取和参与，推进非正式环境中STEM学习的创新研究和评估，以及发展参与者对深度学习的理解。该奖项反映了美国国家科学基金会的法定使命，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 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.