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

该项目将重点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，让初中生和高中生参与独立的数据分析研究项目。虽然环境科学方方面面的公开数据集已经出现了巨大的爆炸性增长，但教师和学生在数据分析中获得和接触到的学习机会往往有限，无法很好地利用它们。该项目试图通过一种专业发展模式来应对这一挑战，该模式让教师成为生态科学家的学徒，并为教师提供必要的支持，让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把该计划的覆盖范围扩大到来自不同种族和社会经济背景的数千名学生。学习伙伴关系与技术教育研究中心(TERC)、前进学习和波多黎各大学的卢奎罗长期生态研究(LTER)方案合作，将建立在数据堵塞模型的基础上，该模型已在整个LTER网络中应用。该项目将使学生参与探索、分析和总结波多黎各厄尔云克热带雨林的长期数据。这个项目的目标是了解如何最好地帮助教师发展能力，指导学生提出他们自己的科学问题，并参与数据分析，为他们的问题找到证据。该项目将在四年内直接影响波多黎各农村地区的100名教师和2500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，建立数据集，并使用Luquillo LTER的长期数据提出研究问题。在学年期间，卢基洛LTER的一名科学家将远程指导教师，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们的榜样。教师和学生的成绩将使用观察、调查、案例研究和标准来衡量由此产生的学生数据堵塞项目的质量。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。

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.