This is a collaborative project involving investigators from Michigan State University (Award DUE-1711260), the American Museum of Natural History (Award DUE-1711411), and Rutgers University-New Brunswick (Award DUE-1712034). Understanding complex interdisciplinary issues such as large-scale ecosystem change, food security, and the decline in global fisheries requires that the next generation of scientists develop Systems Thinking (ST) skills that will allow them to understand the structure and function of coupled human and natural systems and identify the leverage points for addressing problems most effectively and efficiently. Students who are able to think deeply about the complex dynamics of a system are better prepared to predict the system's behavior, engineer more favorable outcomes, and evaluate the trade-offs between different policy and management decisions. However, even though ST skills are often the foundation of sustainability science curricula at universities nationwide, few resources are available for instructors to promote and assess ST skills in their classrooms. In this project, the investigators will test a method for teaching, assessing, and improving ST skills in STEM classrooms using cognitive mapping software called Mental Modeler (www.mentalmodeler.org/), which allows students to represent their understanding of a problem through easy-to-use system-modeling tools and to rearrange components of the problem to analyze scenarios and revise their understanding. This approach will be tested in several environmental science, environmental studies, and sustainability-related courses focusing on problems related to food systems.
Building on previous NSF-funded research, this project will (1) produce tools and modules that support and measure ST learning in integrated STEM disciplines and (2) further develop and test the Mental Modeler software for visualizing complex systems. Mental Modeler is a multidimensional visualization and modeling software platform that allows users to identify the structure, function, and leverage points of a system and to run system-level scenario analyses. The software will help educators determine aspects of ST assessment that are applicable across STEM fields and measure students' ST learning over time. Specifically, the project will address three questions: (1) What are the standardized ST assessment dimensions that are applicable across STEM fields? (2) Can students' ST learning progressions be measured over time through structural network metrics, functional scenario analyses, and qualitative descriptions defined within semi-quantitative cognitive maps (a) within a specific course and (b) across courses in a curriculum? (3) Are there predictable student learning trajectories or progressions with regard to ST?
