Indeed, modeling is one of the key science and engineering practices promoted in A Framework for K-12 Science Education (NRC, 2012) and the Next Generation Science Standards (NGSS Lead States, 2013) for K-12 science education. Modeling is a critical practice used by scientists in their everyday work to make sense of the world and produce new knowledge. Building off that work, we broaden several of the ST aspects that are aligned with and informed by the modeling process and deepen the examination of opportunities and challenges of using computational system models. The authors’ work revealed opportunities and challenges about aspects of ST students encountered during the modeling process as they use computational system modeling to make sense of a phenomenon. In particular, computational models provide a promising avenue that supports students’ ST to make sense of various phenomena, given the affordances that allow the computation of a web of interactions that would be very different to predict and interpret otherwise (Mandinach, 1989 Richmond, 1993). Modeling has been advocated as a promising practice that supports students in applying ST to make sense of a phenomenon (Arndt, 2006 Eilam, 2012 Yoon & Hmelo-Silver, 2017), and previous work has demonstrated how modeling practices are aligned with aspects of systems thinking (ST) (Shin et al., 2022). Science educators have been advocating in recent years for the integration of systems thinking in science education as part of an endeavor to prepare scientifically literate citizens who are equipped with thinking skills that would support them in making sense of the complex phenomena they experience in everyday life (Arndt, 2006 Assaraf et al., 2005 National Research Council, 2012). Systems thinking, an approach used in many fields and across disciplines, supports understanding complex phenomena and solving challenging problems.

Our findings provide insights into the opportunities of a system dynamics approach and the challenges that remain in supporting students to make sense of complex phenomena and nonlinear mechanisms. In particular, we show epistemological barriers to fruitful use of real-world data for model revision. In addition, we describe specific challenges students encountered when evaluating and revising models. However, student models and their accompanying explanations were limited in scope as students did not address feedback mechanisms as part of their modeling and explanations. We show students’ increased capacity to explain the underlying mechanism of the phenomenon in terms of change over time that goes beyond linear causal relationships. In this paper, we describe an empirical study that examines how 10th grade students engage in aspects of ST through computational system modeling as part of a Next Generation Science Standards-aligned project-based learning unit on chemical kinetics. Using computational system models and a system dynamics approach can support students in overcoming these challenges when making sense of complex phenomena. However, studies show that engaging students in ST is challenging, especially concerning aspects like change over time and feedback. Systems thinking (ST) is a promising approach for developing solutions to various problems that society faces and has been acknowledged as a crosscutting concept that should be integrated across educational science disciplines.

Understanding the world around us is a growing necessity for the whole public, as citizens are required to make informed decisions in their everyday lives about complex issues.

Ngss phenomena