Puffins: Exploring how narrative, data science, and artificial intelligence enhance the study of ecology in middle school

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The study of puffins can provide a fascinating subject area for the integrated learning of data and ecosystems. Once hunted to local extinction, puffins have made a dramatic yet fragile comeback and been re-established to historic nesting islands in mid-coast Maine. This project combines a scientific adventure story about puffin restoration with student-directed data investigations about the relationships between puffin health and environmental factors. Students will examine trends and correlations in several decades of curated National Audubon Society data about puffins, using an accessible open-source education data tool to examine relationships among variables including sea surface temperature, hatch island, fish in the puffin diet, fledgling weight, and survival to breeding age. They will use present-day data from puffin webcams and sound recordings to supplement their work with historical datasets. Students will train an artificial intelligence (AI) system to distinguish among puffin vocalizations, learn concepts of data biases, and examine how patterns of puffin calls associate with other aspects of puffin behavior and health. Students also will install their own webcams at school to generalize their study on the behavior of local birds and learn how scientists use webcams. Finally, students will interact with former interns from diverse backgrounds who have worked on Maine’s puffin islands to learn how data are being used to study ornithology and climate change. The project will enable Maine’s rural and urban students, students whose first language is not English, new immigrants, and students who do not consider themselves science-oriented to experience place and community-based authentic data and perform their own research on a charismatic species. 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.The goal of this project is to explore how data science and AI, combined with the scientific narrative of a nonfiction book about puffins, enhances middle school students’ learning about an ecological system. The project addresses three research questions: 1) How do students use data tools to make sense of large ecological datasets?; 2) How do students organize and use data gathered from webcams, such as images of puffins engaged in various behaviors or the sounds puffins make, and how do they use AI to extend their observations?; and 3) What is the impact of using data tools, combined with a nonfiction book, on students’ learning about puffin ecology and ecological systems more generally? The project will use a design-based research approach in which several middle school teachers co-design a three-week curriculum module in conjunction with project staff. These teachers will also provide input to the research plan, which is based on classroom observations as well as case studies of a small number of students from each of seven classrooms. The case study research will provide a deeper analysis of students’ understanding of puffin data, and will involve the collection of student work as well as individual interviews in which students will be asked to explain their findings. In addition, we will collect survey data from all 500 students who participate in the project to determine how their overall STEM interest, engagement, career knowledge, and interest in data science and ecology have been impacted. Project deliverables include the puffin curriculum module and a modified version of an Audubon dataset about puffins that will be made freely available for all educators. We will publish the project’s findings for researchers, educators, and the general public. Teacher workshops, including one focused on attracting under-represented students to data science, will be presented to national audiences.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.