No abstract

Modeling is one of the core scientific and engineering practices described in A Framework for K-12 Science Education. Students are expected to construct, use, evaluate, and revise their models to make sense of phenomena or to find solutions to problems. Technology tools can support the development of students' modeling practice when learning about environmental issues. This study investigates the incorporation of an online computational modeling tool in a middle school curricular unit focusing on ocean acidification. We present the advantages and challenges experienced by students and teachers while engaging in the unit and using the modeling tool. Our results indicate that integrating the modeling tool in the ocean acidification curricular unit facilitates students' interest and engagement in environmental responsibility and focused students' attention toward human involvement and impact on the environment. Students perceived the tool and the curricular unit to be relevant to their lives and important in promoting their content learning and modeling practice. However, students and teachers reported several challenges, mostly related to the complexity of using the modeling tool and working with the resulting graphs and charts. We discuss these advantages and challenges and suggest recommendations for supporting students' modeling practice when learning about environmental issues.

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. 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. However, studies show that engaging students in ST is challenging, especially concerning aspects like change over time and feedback. Using computational system models and a system dynamics approach can support students in overcoming these challenges when making sense of complex phenomena. 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. 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. 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. In addition, we describe specific challenges students encountered when evaluating and revising models. In particular, we show epistemological barriers to fruitful use of real-world data for model revision. 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.

This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patterns, and equilibrium. In contrast, ST has shown promise as an approach for making sense of complex problems. To facilitate ST, computational modeling tools have been developed, but it is not clear to what extent different approaches promote specific aspects of ST and whether scaffolding such thinking should start with supporting students first in linear causal reasoning before moving to more complex causal dimensions. This study compares two computational modeling approaches, static equilibrium and system dynamics modeling, and their potential to engage students in applying ST aspects in their explanations of the evaporative cooling phenomenon. To make such a comparison we analyzed 10th grade chemistry students’ explanations of the phenomenon as they constructed and used both modeling approaches. The findings suggest that using a system dynamics approach prompts more complex reasoning aligning with ST aspects. However, some students remain resistant to the application of ST and continue to favor linear causal explanations with both modeling approaches. This study provides evidence for the potential of using system dynamics models in applying ST. In addition, the results raise questions about whether linear causal reasoning may serve as a scaffold for engaging students in more sophisticated types of reasoning.