Building a Youth-Led Learning Community through Automating Hydroponic Systems

在这个项目中，青年将应用科学和计算科学的概念和做法，学习创建和建立自动化系统，在那里他们可以种植自己的粮食。这些专门的种植系统将利用水培技术，即在没有土壤的情况下种植植物。计算科学是一个跨学科的领域，它结合了科学、计算、计算机科学、工程和数学的技能和知识来解决问题。计算科学在工业和日常生活中的普及要求培养一支具有解决复杂现实问题知识的STEM员工队伍。研究人员将培训高中生对低成本微控制器进行编程，以收集数据并使用这些数据来实现水培系统的自动化。为了支持高中生获得大专学位的兴趣，他们将接受波士顿学院大学方向项目和拉塞尔大学多元化之路校友的第一代大学生的指导。这个项目将研究如何设计一个内部学习环境，促进年轻人的技能和追求STEM职业生涯的兴趣。利用基于设计的研究框架，并利用包括观察、调查、访谈和青年产生的人工制品在内的混合方法数据收集方法，项目团队将探索如何以及以何种方式使用物理计算和计算科学来提高青年追求STEM职业生涯的兴趣和知识。对调查数据的分析将首先使用验证性因子分析评估潜在变量的测量质量，然后使用潜在变量增长模型评估四个时间点重复测量的变化。这项研究和评估工作将随着时间的推移跟踪500名青年及其家人，并评估该计划的哪些方面对青年成功和STEM职业自我效能和意识至关重要。这项工作提供了一种方法，以支持年轻人审查跨农业、计算和科学教学领域的职业。该项目由学生和教师创新技术体验计划(ITEST)资助，该计划支持建立对实践、计划要素、背景和过程的理解的项目，这些项目有助于提高学生对科学、技术、工程和数学(STEM)以及信息和通信技术(ICT)职业的知识和兴趣。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In this project, youth will apply scientific and computational science concepts and practices by learning to create and build automated systems where they can grow their own food. These specialized growing systems will utilize hydroponics where plants are grown without soil. Computational science is an interdisciplinary area that combines skills and knowledge from science, computation, computer science, engineering, and mathematics to solve problems. The prevalence of computational science in industry and everyday lives necessitates the development of a STEM workforce knowledgeable to solve complex real-world problems. Researchers will train high school youth to program low-cost micro-controllers to collect data and use that data to automate hydroponic systems. To support the high school youth's interest in obtaining a post-secondary degree, they will be mentored by first-generation college students from alumni of Boston College's College Bound program and Lasell University's Pathways to Diversity.This project will study ways to design an internal learning environment that promotes youth skills and interest in pursuing STEM careers. Utilizing a design-based research framework and drawing upon a mixed-method data collection approach including observations, surveys, interviews, and youth generated artifacts, the project team will explore how and in what ways the use of physical computing coupled with computational science improves youth interest and knowledge to pursue STEM careers. Survey data will be analyzed by first evaluating the measurement quality of the latent variables using confirmatory factor analysis and then using latent variable growth modeling to assess changes in repeated measures for four time points. The research and evaluation work will track 500 youth and their families over time and evaluate what aspects of the program are critical to youth success and STEM career self-efficacy and awareness. This work offers an approach to support youth in examining careers across fields of agriculture, computation, and science teaching. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.