CI-TEAM Demonstration Project: TRENDS: Training the next generation workforce in Real-time Data and Simulation Technologies

CI-TEAM 示范项目：趋势：培训下一代劳动力的实时数据和模拟技术

• 批准号: 1135564
• 负责人: Mitchell Neilsen
• 金额: $ 25万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1135564&HistoricalAwards=false
• 关键词: CI; TEAM; Demonstration; Project; TRENDS

Advances in sensor and communication technologies and increased cyber-connectivity are enabling access to a vast amount of varied datasets. This access is interactive in that one can control what is being collected, how often it is sampled and the granularity at which it is being collected. Such cyber-enabled infrastructures are poised to revolutionize the way the physical world is monitored and field experiments are performed -- with automated, remote, real-time data collection and feedback replacing traditional manual, and time-consuming methods. Although web accessible information is being used in our daily decision making and has enabled exciting new research possibilities, it is very under-utilized in learning and classroom environments. It is not enough for the next generation workforce to be simply trained to view or utilize online data, rather they must be able to synthesize new solutions to emerging challenges by leveraging diverse cyber-infrastructure enabled data sources. The premise of this work is that training such a cyber-infrastructure literate citizen must start at the K-12 level. The focus of this project is to increase the exposure of K-12 teachers and student populations to sensor network and simulation based cyber-infrastructure tools. Research has shown that experiments and hands-on activities are an important part of the curriculum as they re-enforce textbook knowledge and lead to better retention. Currently, these activities often are restricted to in-class experiments subject to the available space (the classroom), instruments and resources, and are of limited duration (the span of a single class). Point data is used (e.g., holding temperature to a fixed value for the duration of the experiment) rather than time-varying input (which better models real-world systems). Further, for new, emerging areas of science and technology, students often have to rely on reading in textbooks. For example, a new Kansas Standards for Agricultural Education requires students to learn about sustainable agriculture practices, use of GPS/GIS, and the impact of fertilizers/pesticides on yield. However, due to lack of resources, students mainly rely on reading about sustainable agriculture from textbooks. The intellectual merits of the proposed project include developing sensor infrastructure for field experiments, simulation software, and curriculum and training material for K-12 education. These efforts include the following: (a) Developing software and training modules for sensor network based data collection technologies: The aim is that teachers will eventually be able to replace "in-class observing" with "real-time data" for experiments in any of their science and technology classes, and include interactive aspects involving reactions to real-time input, (b) Developing a "smart farm" simulation package in which students will manage the day-to-day operations of a virtual farm, and link it to diverse data sources including real-time data, (c) Promoting inquiry based education by developing lesson plans based on field experiments and simulation software, integrating them into curriculum and training teachers/students based on these lesson plans. The broader impacts of the project include reaching out to a diverse pool of students to broaden participation. The project builds upon past successful projects in real-time embedded sensor systems design funded by NSF RET (Research Experience for Teachers) and NSF CRCD (Combined Research-Curriculum Development) programs, and the NSF GK-12 STEM Fellows program. These have resulted in strong relationships with both rural and urban Kansas schools. This project leverages these connections to train a diverse population set. The project is reaching out to rural under-served school districts in Kansas and those with significant Hispanic population. The focus on agricultural applications via Smart Farm simulations enables the project to reach student populations that have historically been underserved. By making the software web-accessible, the project is working to minimize the "Technology Gap" between those students who have broadband access and state-of-the-art computing hardware in their homes, and those who have outdated or no equipment. The team is continuing to work with existing programs at Kansas State University such as GROW and EXCITE which bring middle and high school girls to campus, and with the Kan-Ed program whose purpose is to provide state-wide collaboration and information exchange opportunities among educational institutions in Kansas.

传感器和通信技术的进步以及增加的网络连接性正在使能够访问大量不同的数据集。这种访问是互动的，因为人们可以控制所收集的内容，采样的频率以及收集到的粒度。这种支持网络的基础架构有望彻底改变对物理世界的监视方式和实地实验的执行方式 - 自动化，远程，实时的数据收集和反馈代替传统手册和耗时的方法。尽管在我们的日常决策中使用了可访问的信息，并实现了令人兴奋的新研究可能性，但它在学习和课堂环境中的利用非常低。仅仅让下一代劳动力训练以查看或利用在线数据是不够的，而是必须能够通过利用启用各种网络基础结构的数据源来合成新的解决方案来实现新的挑战。这项工作的前提是，培训这样的网络基础设施识字公民必须从K-12级开始。该项目的重点是增加K-12教师和学生群体对传感器网络和基于模拟的网络基础结构工具的接触。研究表明，实验和实践活动是课程的重要组成部分，因为它们会加强教科书知识并带来更好的保留。当前，这些活动通常仅限于在可用空间（教室），仪器和资源的情况下进行的课堂实验，并且持续时间有限（单个类的跨度）。使用点数据（例如，将温度保持在实验持续时间的固定值），而不是随时间变化的输入（更好地建模现实世界系统）。此外，对于新兴的科学技术领域，学生通常必须依靠教科书中的阅读。例如，新的堪萨斯州农业教育标准要求学生学习可持续的农业实践，使用GPS/GIS以及肥料/农药对产量的影响。但是，由于缺乏资源，学生主要依靠阅读教科书的可持续农业。拟议项目的智力优点包括开发用于现场实验的传感器基础设施，模拟软件以及K-12教育的课程和培训材料。这些努力包括以下内容：（a）为基于传感器网络的数据收集技术开发软件和培训模块：目的是，教师最终将能够用“实时数据”代替“实时数据”，以实验其任何科学和技术课程中的实验，并包括涉及实时输入的互动方面的互动方面，以实时的反应以及在“智能农场”中进行“智能农场”（b）的“智能”式的工具（b），（b）在某种程度上管理“智能式”的工具，（b）在某种程度上管理一个智能的求职，该范围是一定的。数据源，包括实时数据，（c）通过基于现场实验和仿真软件制定课程计划来促进基于询问的教育，将其集成到课程中，并根据这些课程计划将其集成到课程和培训教师/学生中。该项目的更广泛影响包括与各种学生群体接触以扩大参与。该项目基于由NSF RET（教师研究经验）和NSF CRCD（研究合并研究）计划和NSF GK-12 STEM Fellows计划资助的实时嵌入式传感器系统设计的过去成功项目。这些导致与堪萨斯州的农村和城市学校建立了牢固的关系。该项目利用这些联系来训练多样化的人口集。该项目正在与堪萨斯州的农村不足的学区和西班牙裔人口大量的人联系。通过智能农场模拟对农业应用的重点使该项目能够吸引历史上服务不足的学生人群。通过使软件网络访问可访问，该项目正在努力最大程度地减少那些拥有宽带访问和最先进的计算机硬件的学生与已经过时或没有设备的学生之间的“技术差距”。该团队将继续与堪萨斯州立大学的现有计划合作，例如成长和激发，这些计划将中学和高中女生带到校园，并与堪萨斯州教育机构之间提供全州范围内的合作和信息交流机会的堪萨斯州的计划。