Collaborative Research: Developing a Modeling Orientation to Science: Teaching and Learning Variability and Change in Ecosystems

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There is an ongoing need to find ways to make science relevant for school students and an increasing need to support student interpretation of large data sets. This project addresses these needs by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts. Students construct and critique models that they and peers invent and – through the lens of models –develop foundational knowledge about the roles of variability and change in ecosystem functioning, as well as the roles of models and argumentation in scientific practice. The context for students’ work is a set of citizen science-based investigations of changes in ecosystems in Maine conducted in twelve collaborating classrooms. The project studies how and to what extent students’ use of different forms of modeling emerges from and informs how they investigate ecosystems. A parallel research effort investigates how and to what extent the development of teachers’ comfort and proficiency with modeling changes students’ engagement in these forms of modeling and students’ understandings of ecosystems. A key contribution of the project is capitalizing on the Gulf of Maine Research Institutes’s Ecosystem Investigation Network's citizen science field research to ground for middle school students the need to invent, revise, and contest models about real ecosystems. The understandings that result from the project’s research provide evidence toward first, scaling the learning experiences to the network of 500+ teachers who are part of the Ecosystem Investigation Network, and, second, replication by programs nationally that aim to engage students in data-rich, field-based ecological investigations.The investigation takes place in twelve collaborating middle-school classrooms, drawn from the network of 500+ Maine teachers trained in Maine's Ecosystem Investigation Network. Over the course of their field investigations, students engage in the construction, critique, and revision of three forms of modeling that play central roles in ecology: microcosms, system dynamics, and data modeling. Two innovations are introduced over the course of the project. The first is focused on enriching classroom supports for engaging in multiple forms of modeling. The second involves enhancing middle school teachers’ learning about modeling, especially in the context of large data citizen science investigations. The study uses a mixed methods approach to explore the impact of the innovations on the experiences and understandings of both teachers and students. Instruments include teacher interviews and questionnaires, student interviews, and classroom observation. The understandings that result from the project’s research will inform the design of professional development for teachers around data analysis and interpretation, and around how student understanding of modeling develops with sustained support, both of which are practices at the heart of scientific literacy. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects. This project is jointly funded by DRK-12 and the Established Program to Stimulate Competitive Research (EPSCoR).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.