Supporting the Scientific Practice of Data Analysis through Creative Investigations of Long-Term Ecological Datasets

通过长期生态数据集的创造性研究支持数据分析的科学实践

• 批准号: 2049061
• 负责人: Steven McGee
• 金额: $ 142.49万
• 依托单位: The Learning Partnership
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049061&HistoricalAwards=false
• 关键词: Supporting; Scientific; Practice; Data; Analysis

This project will focus on research and development for a new professional development model to support teachers in engaging middle school and high school students in independent data analysis research projects, using local ecological data. While there has been a tremendous explosion of publicly available datasets on all facets of environmental science, teachers and students often have limited access and exposure to learning opportunities in data analysis to make good use of them. This project seeks to address this challenge through a professional development model that engages teachers as apprentices to ecological scientists and provides the teachers with the support they need to engage their students in data-based ecological research. The dissemination through the partnership network will extend the reach of the program to thousands of students from a diversity of racial and socioeconomic contexts. The Learning Partnership, in collaboration with Technical Education Research Centers (TERC), Forward Learning, and the Luquillo Long-Term Ecological Research (LTER) program at the University of Puerto Rico, will build on the foundation of the Data Jam model, which has been applied across the LTER network. This project will engage students in the process of exploring, analyzing, and summarizing long-term data about El Yunque rainforest in Puerto Rico. The goal of this project is to understand how best to help teachers develop the capacity to mentor students to be able to ask their own scientific questions and engage in data analysis to develop evidence for their questions. The project will directly impact a total of 100 teachers and 2500 students from rural regions of Puerto Rico over a four year period. Each summer, 25 teachers will have extended opportunities to explore data trends, develop visualizations, set up datasets, and ask research questions using long-term data from the Luquillo LTER. During the school year, teachers will be mentored remotely by a Luquillo LTER scientist to help teachers address scientific issues that emerge from the students’ projects. The scientists will also serve as role models for the students. Teacher and student outcomes will be measured using observations, surveys, case studies, and rubrics that measure the quality of the resulting student Data Jam projects. 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.

该项目将侧重于研究和开发一种新的专业发展模式，以支持教师利用当地生态数据，让初中和高中学生参与独立的数据分析研究项目。虽然关于环境科学各个方面的公开数据集出现了巨大的爆炸，但教师和学生在数据分析方面的学习机会和机会往往有限，无法充分利用它们。该项目旨在通过一种专业发展模式来应对这一挑战，该模式使教师成为生态科学家的学徒，并为教师提供所需的支持，使他们的学生参与基于数据的生态研究。 通过伙伴关系网络的传播将扩大该方案的覆盖面，使来自不同种族和社会经济背景的数千名学生受益。 学习伙伴关系与技术教育研究中心（TERC），向前学习和波多黎各大学的卢基略长期生态研究（LTER）计划合作，将建立在数据堵塞模型的基础上，该模型已应用于整个LTER网络。 这个项目将让学生参与探索，分析和总结有关波多黎各埃尔云克雨林的长期数据的过程。该项目的目标是了解如何最好地帮助教师培养指导学生的能力，使他们能够提出自己的科学问题，并进行数据分析，为他们的问题提供证据。该项目将在四年期间直接影响波多黎各农村地区的100名教师和2 500名学生。每年夏天，25名教师将有更多的机会探索数据趋势，开发可视化，设置数据集，并使用卢基略LTER的长期数据提出研究问题。在学年期间，教师将由卢基略LTER科学家远程指导，帮助教师解决学生项目中出现的科学问题。这些科学家也将成为学生的榜样。教师和学生的成果将使用观察，调查，案例研究和衡量学生数据堵塞项目质量的规则来衡量。该项目由学生和教师创新技术体验（ITEST）计划资助，该计划支持建立对实践，计划元素，背景和过程的理解的项目，有助于增加学生对科学，技术，工程，信息和通信技术（ICT）该奖项反映了NSF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。