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

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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.