We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.

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.

As consensus towards teaching science for citizenship grows, so grows the need to prepare science teachers to pursue this goal. Implementation of socioscientific issues (SSI) is one of the most prominent theoretical and practical frameworks developed to support scientific literacy and preparing students as informed citizens. However, implementation of SSI holds great challenges for science teachers. Longitudinal professional development (PD) programs were designed to overcome these barriers, yet at the same time many educational systems lack the resources, both in terms of budget and time to meet such intense programs. In this paper, we introduce a design of a short-term PD course that was conducted in Israel. The PD was specifically tailored for secondary school science teachers, with the goal to support them in implementing SSI. Employing an educational design research framework, we tested our PD design over a span of three consecutive years. Through an iterative design process, we were able to make modifications to the program based on data collected and analyzed from the previous year. The structure of the PD is based on four SSI aspects: (a) introduction to SSI, (b) argumentation in SSI context, (c) SSI operationalization, and (d) science communication. In this paper, we provide detailed explanations for each of these aspects, justify the changes made to the PD design, and highlight both promising and less effective strategies for engaging teachers in SSI. Ultimately, we propose a comprehensive SSI PD model that can effectively prepare teachers to take their initial steps in implementing SSI, while remaining adaptable to diverse educational systems.

Interpreting and creating computational systems models is an important goal of science education. One aspect of computational systems modeling that is supported by modeling, systems thinking, and computational thinking literature is “testing, evaluating, and debugging models.” Through testing and debugging, students can identify aspects of their models that either do not match external data or conflict with their conceptual understandings of a phenomenon. This disconnect encourages students to make model revisions, which in turn deepens their conceptual understanding of a phenomenon. Given that many students find testing and debugging challenging, we set out to investigate the various testing and debugging behaviors and behavioral patterns that students use when building and revising computational system models in a supportive learning environment. We designed and implemented a 6-week unit where students constructed and revised a computational systems model of evaporative cooling using SageModeler software. Our results suggest that despite being in a common classroom, the three groups of students in this study all utilized different testing and debugging behavioral patterns. Group 1 focused on using external peer feedback to identify flaws in their model, group 2 used verbal and written discourse to critique their model’s structure and suggest structural changes, and group 3 relied on systemic analysis of model output to drive model revisions. These results suggest that multiple aspects of the learning environment are necessary to enable students to take these different approaches to testing and debugging.

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.