Science and Engineering Education for Infrastructure Transformation

探索研究K-12计划(DRK-12)旨在通过研究和开发创新资源、模型和工具(RMT)，显著提高K-12学龄前学生和教师在科学、技术、工程和数学(STEM)方面的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发努力的基础上，这些研究和开发工作为拟议的项目提供了理论和经验证明。协和联盟将与普渡大学合作，研究和开发工程教育技术和教学方法，支持以项目为基础的科学、工程和计算概念和技能的学习，这些概念和技能是基础设施具有战略重要性的“智能”和“绿色”方面的基础。该项目将开发变革性的技术和课程材料，将高中校园或地理信息系统(如谷歌地图)变成一个工程实验室，几乎有无限的学习和探索机会。该项目将带来两项创新：1)智能高中是一个设计物联网系统的工程平台，用于管理学校的资源、空间和流程，基于对学生在校园部署的各种传感器收集的数据进行实时分析；以及2)虚拟太阳能世界是一个计算建模平台，供学生为他们的家庭、学校和地区设计、部署和连接虚拟太阳能解决方案。将根据这些技术开发六个符合标准的课程单元，以指导学生学习和支持教育研究。来自印第安纳州、马萨诸塞州、新罕布夏州和俄亥俄州农村、郊区和城市高中的大约2000名学生将参与这项研究。通过互联网、会议、出版物和合作伙伴网络项目产品和成果。该研究旨在确定技术增强的教学策略，这些策略可以同时促进在科学推理、设计思维和计算思维方面的技能和自我效能的增长，所有这些都是建设未来基础设施所必需的。对基础设施转型的关注与NSF关于智能和互联社区的愿景保持一致。虽然这个项目将使用智能和绿色基础设施的背景来吸引学生解决现实世界的问题，但他们通过迎接这个项目的挑战而获得的科学推理、设计思维和计算思维的技能可以转移到其他主题和领域。数据来源包括前测/后测、过程数据、自我报告、观察、调查、访谈和参与者信息。

The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. The Concord Consortium in collaboration with Purdue University will research and develop an engineering education technology and pedagogy that will support project-based learning of science, engineering, and computation concepts and skills underlying the strategically important "smart" and "green" aspects of the infrastructure. This project will develop transformative technologies and curriculum materials to turn the campus of a high school or a geographical information system such as Google Maps into an engineering laboratory with virtually unlimited opportunities for learning and exploration. The project will deliver two innovations: 1) The Smart High School is an engineering platform for designing Internet of Things systems for managing the resources, space, and processes of a school based on real-time analysis of data collected by various sensors deployed by students on campus; and 2) the Virtual Solar World is a computational modeling platform for students to design, deploy, and connect virtual solar power solutions for their homes, schools, and regions. Six standards-aligned curriculum units based on these technologies will be developed to guide student learning and support educational research. Approximately 2,000 students from rural, suburban, and urban high schools in Indiana, Massachusetts, New Hampshire, and Ohio will participate in this research. project products and findings through the Internet, conferences, publications, and partner networks.The research is designed to identify technology-enhanced instructional strategies that can simultaneously foster the growth of skills and self-efficacy in scientific reasoning, design thinking, and computational thinking, all of which are needed to build the future infrastructure. The focus on infrastructure transformation is aligned with NSF's vision of smart and connected communities. Although this project will use the context of smart and green infrastructure to engage students to solve real-world problems, the skills of scientific reasoning, design thinking, and computational thinking that they will acquire through meeting the challenges of this project can be transferrable to other topics and fields. Using a design-based research approach, a rich set of formative and summative data will be collected from these students for probing into three research questions: 1) To what extent does the integrated learning model help students develop and connect scientific reasoning, design thinking, and computational thinking skills?; 2) To what extent is students' interest in cognate careers affected by the authenticity of engineering design challenges?; and 3) How do the variations in the solutions to overcome the cognitive and practical difficulties of real-world problems impact learning outcomes and career interest? The data sources include pre/post-tests, process data, self-reports, observations, surveys, interviews, and participant information.