SBIR Phase II: A STEM toolkit enabling global air quality experiments

SBIR第二阶段项目旨在开发一个物联网（IoT）数据收集和分析平台，用于协作STEM和大数据研究和教育，该平台能够从互联网支持的科学仪器中协作收集地理上分散的数据。STEM（科学、技术、工程和数学）工作正在增加：美国商务部预测，从2014年到2024年，这些领域的职业将增长8.9%。然而，美国目前面临着精通数学和科学学科的工人和学生的严重短缺。在某种程度上，这种短缺是由于少数民族和妇女对STEM相关领域缺乏兴趣。需要有参与性、相关性和亲身体验，以鼓励这些人群的兴趣。该项目满足了联邦政府和前沿STEM教育工作者的要求，即中学和中学后教育机构都以让学生参与现实世界问题的方式教授科学。预计该项目将使大数据可供使用，同时提供有益和有吸引力的实践学习机会，以提高数据素养;加强跨地理和跨学科的教育科学合作;并提高科学素养和对人口统计的兴趣，从而增加了学生继续从事科学事业的可能性。这项技术将是第一个合作项目，教育物联网STEM平台正在开发中，在教育技术领域具有创新性，该领域尚未采用可在全球范围内生成大数据的网络连接传感器。目前，学校还没有一个机制，能够有组织地在教室和学校之间收集和共享真实的数据。拟议的创新允许用户通过全球网络进行通信，并能够与各种科学仪器和数据源配对，以支持多功能，引人入胜，协调，多学校实验和数据共享。数据科学现在几乎以某种方式影响着每一个职业，该平台将以一种引人入胜和相关的方式让学生接触到大数据分析。在第1阶段，方法的可行性得到了牢固的确立。第二阶段的目标是扩大第一阶段开发的教育平台，以优化国家/全球影响，并支持大数据研究和一系列传感器的适用性。目标包括扩展工具来查看和分析数据，完善和扩展课程，开发应用程序编程接口，并创建软件工具来操作和共享数据/课程。该奖项反映了NSF的法定使命，并被认为值得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。

This SBIR Phase II project seeks to develop an internet-of-things (IoT) data collection and analysis platform for collaborative STEM and big data research and education, that enables collaborative, geographically dispersed collection of data from internet enabled scientific instruments. STEM (science, technology, engineering, and math) jobs are on the rise: the U.S. Department of Commerce predicts that occupations in these sectors will grow by 8.9 % from 2014 to 2024. Yet the U.S. currently faces a critical shortage of workers and students who are proficient in math and science subjects. In part, this shortage is due to a lack of interest in STEM-related fields by minorities and women. Engaging, relevant and hands on experiences are needed to encourage interest amongst these populations. This project fulfills the requests of the federal government and leading-edge STEM educators that both secondary and post-secondary institutions teach science in a way that engages students with real-world problems. The expectation is that this project will make big data accessible, while providing rewarding and appealing hands-on learning opportunities that will increase data literacy; increase scientific collaboration in education across geographic and interdisciplinary lines; and increase scientific literacy and interest across demographics, thus increasing the likelihood that students will continue to pursue scientific careers.The proposed technology will be the first collaborative educational IoT STEM platform to be developed, and is innovative in the field of Educational Technology, which has yet to adopt web-connected sensors that generate big data on a global scale. At present, there is no mechanism for schools to collect and share real data between classrooms and schools in an organized way. The proposed innovation allows users to communicate via a global network and is capable of being paired with an unlimited variety of scientific instruments and data sources, to support versatile, engaging, coordinated, multi-school experiments and data sharing. Data science now impacts virtually every profession in some way, and the platform will uniquely expose students to big data analytics in an engaging and relevant way. In Phase 1, feasibility of approach was firmly established. Phase II objectives will be to expand the educational platform developed in Phase I to optimize national/global impact and support applicability to big data research as well as a range of sensors. Goals include to expand tools to view and analyze data, refine and expand curriculum, develop an Application Programming Interface and create software tools to manipulate and share data/curriculum. The platform's ability to promote greater learning will also be evaluated.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.