NSF-BSF: Learning the concept of Dynamic Equilibrium across disciplines with SystEms Augmented Mechanistic Representations

Learning the concept of Dynamic Equilibrium across disciplines with SystEms Augmented Mechanistic Representations A fascinating aspect of systems in science is dynamic equilibrium (DE). DE describes situations, in which changes continually happen, but they balance each other out. Examples include our body maintaining a constant temperature while the surroundings can be hot or cold, or an ecosystem maintaining cyclical population levels even as predators and prey die and reproduce. Understanding how such dynamical systems remain stable is fundamental to understanding order and pattern in the world. Thus, it is crucial that students of science come to understand this concept, helping them develop science proficiency. Despite the importance of the concept of DE, it is hard for many people to understand. The contrast between stability and change is confusing so people tend to think of the system as static. The PI’s work has shown that by introducing visual models and simulations, students get a deeper understanding and are more engaged when learning science. Yet, there still remains a problem with connecting the phenomenon to the way the elements interact. The project addresses this problem by conducting a series of studies and software design iterations. The project develops a range of possible computer-based representations, which can support a more coherent understanding of DE. These candidate representations are tested, and a winner chosen. The winner becomes a new tool added to the models and simulations. These augmented simulations are then be used to create science units. The team works with science teachers to build learning units on DE phenomena. A cross-cultural comparison will explore how the winning representation is differently learned in US and Israeli schools. The proposal has two main objectives: (1) construct a theoretically and cognitively viable framework for learning DE that balances mechanism and causality with robustness and generality, and (2) develop an augmented representation that overlays systemic features upon mechanistic processes, what the investigators call SEAM (SystEms Augmented Mechanistic) representations. Creating the DE framework involves four studies: (1) analysis of science education standards related to DE; (2) analysis of the research literature on students’ ideas regarding DE; (3) investigating scientists’ explicit and implicit communication regarding DE; (4) research into students’ intuitions regarding DE. Developing the SEAM representation involves 3 studies: (5) design of three candidates with experienced science teachers; (6) learning research with the three representations, culminating in a cross-cultural comparison of students’ learning in the US and in Israel; (7) deliberative selection of the final SEAM representation. 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.