Integrating Research and Practice: Reimagining Youth Community Science through Make-and-Take Data Sensing Kits

在很多方面，年轻人的生活都受到数据的影响。然而，年轻人很少有机会揭开数据科学的帷幕，体验如何收集、准备、分析数据，并将其呈现为他们每天看到的最终、整齐包装的统计数据和数字。缺乏第一手数据科学经验不仅限制了年轻人的数据科学技能——这对他们的教育和职业道路至关重要——而且还限制了他们作为公民参与决策者的技能，这些决策者能够批判性地分析基于数据的主张。该项目调查年轻人如何使用低成本传感器从社区收集数据，并参与数据科学实践，探索和讲述与社区相关的现象（例如，气候变化、城市隔离、公共交通、污染）。这些以博物馆为基础的营地是为期一周的沉浸式体验，专注于问题制定，数据收集，分析，可视化和解释。非正式的营地环境提供了高接触教学时间（即白天与其他学生和辅导员一起工作）和独立探索时间（即每天下午和晚上将硬件带回家）的混合，青年可以通过这些时间学习新技能并将其应用于家庭和社区。在这个合作项目中，一所大学的研究实验室和儿童科学博物馆共同设计、实施、研究和修改为期一周的中学生数据科学夏令营、数据科学学习评估项目和辅导员培训课程。夏令营将在冬季、春季和夏季学校放假期间实施，为期两年。该项目将调查两个主要问题：在设计、执行和解释与社区相关的科学数据收集工作的过程中，学习者获得了哪些数据科学知识和实践？博物馆如何成为跳板和试金石，将非正式STEM学习体验扩展到学习者的家庭和社区？潜在的贡献包括非正式STEM教育规划的学习理论和设计启发，将STEM探究和学习定位在参与者更广泛的社区中，并激发年轻人认识到以数据为导向的方法与理解他们的日常环境、空间和生活的相关性。由于营地编程包括四种不同的非正式数据上下文，它们的独立性和复杂性越来越高——(1)引导探索；(2) 1天项目；(3)多日项目；(4)针对一部分参与者（将硬件带回家）的纵向、在家的扩展项目——预期的贡献包括对非正式数据科学学习发生的各种环境的比较。该项目采用混合方法研究方法，包括：1)对成对的营前和营后评估进行定量分析，以确定数据科学实践和观点的转变；2)对营地前、营地后和延迟访谈进行定性专题分析，并对营地内观察结果进行相互作用分析；3)、日志文件分析学习者数据收集行为的识别模式。此外，对2.5年项目时间框架内的评估和访谈数据的纵向分析将衡量夏令营课程和评估修订在6次实施中的有效性。该项目主要从两个方面关注公平和归属感：课程开发工作侧重于包容和公平的非正式科学学习，以及关注在STEM教育中往往得不到充分服务的拉丁裔人口。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。

In dozens of ways, youths' lives are shaped by data. Yet, youth seldom have the opportunity to pull back the curtain on data science to experience how data are collected, prepared, analyzed, and presented into the final, neatly packaged statistics and figures they see every day. This lack of first-hand data science experience not only limits youths' data science skills - key to their educational and career pathways - but also limits their skills as civically engaged decision-makers capable of critically analyzing data-based claims. This project investigates how youth can use low-cost sensors to collect data from their communities and engage in data science practices to explore and tell stories about community-relevant phenomena (e.g., climate change, urban segregation, public transportation, pollution). These museum-based camps are week-long, immersive experiences that focus on question formulation, data collection, analysis, visualization, and interpretation. The informal camp setting offers a mix of both high-contact instructional time (i.e., working with other students and facilitators during the day) and independent exploration time (i.e., taking the hardware home each afternoon and evening) through which youth can both learn new skills and apply them in their homes and communities. In this collaborative project, a university research lab and children's science museum work together to design, implement, study, and revise a week-long data science camp for middle school age students, data science learning assessment items and a facilitator training curriculum. Camps will be implemented during Winter, Spring, and Summer school breaks over a two year period. The project will investigate two primary questions: What data science knowledge and practices do learners gain in the course of designing, carrying out and interpreting a scientific data collection effort relevant to their community? And what ways can museums serve as springboards and touchstones for broader informal STEM learning experiences that expand into learners' homes and communities? Potential contributions include learning theory and design heuristics for informal STEM education programming that positions STEM inquiry and learning within participants' broader communities and sparks youth's recognition of the relevance of a data-oriented approach to understanding their day-to-day environments, spaces, and lives. As camp programming includes four diverse informal data contexts of increasing independence and complexity - (1) guided explorations; (2) 1-day projects; (3) multi-day projects; and (4) longitudinal, at-home, extension projects for a subset of participants (who take the hardware home) - anticipated contributions include comparisons of the varied settings in which informal data science learning occurs. The project uses a mixed-methods research approach that includes: 1) quantitative analyses of paired pre- and post-camp assessments to identify shifts in data science practices and perspectives; 2) qualitative thematic analyses of pre-, post-camp, and delayed interviews triangulated with interaction analysis of in-camp observations; and 3), and log file analyses identifying patterns in learners' data gathering behavior. Additionally, longitudinal analyses of assessment and interview data over the 2.5-year project timeframe will gauge the efficacy of camp curriculum and assessment revisions over the 6 implementations. The project centers equity and belonging in two primary ways: curriculum development efforts focus on inclusive and equitable informal science learning and a focus on Latine populations who are often underserved in STEM education.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.