This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

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.

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.

Many scholars advocate for indigenous knowledge (IK) to be integrated into science education to foster a more inclusive educational environment. Informal science learning spaces provide opportunities for encountering science phenomena and broader perspectives. Zoos have the potential to include IK into their informational signage due to having collections of native plants and animals that are significant to Indigenous communities. The history of zoos being platforms for colonialist ideologies also necessitates acknowledgement of how White Settler colonialism has negatively impacted Indigenous peoples. We therefore investigated how two zoos in North America portray IK and perspectives in zoo sections centering native animals. We found that while both zoos provide strong examples of how IK can be included in zoo signage, certain aspects of IK (particularly that of climate patterns), were largely absent. Additionally, zoo signage largely ignored the impact of settler colonialism on both the natural environment and indigenous cultures.

In our interconnected world, Systems Thinking (ST) is increasingly being recognized as a key learning goal for science education to help students make sense of complex phenomena. To support students in mastering ST, educators are advocating for using computational modeling programs. However, studies suggest that students often have challenges with using ST in the context of computational modeling. While previous studies have suggested that students have challenges modeling change over time through collector and flow structures and representing iterative processes through feedback loops, most of these studies investigated student ST through pre and post tests or through interviews. As such there is a gap in the literature regarding how student ST approaches develop and change throughout a computational modeling unit. In this case study, we aimed to determine which aspects of ST students found challenging during a computational modeling unit, how their approaches to ST changed over time, and how the learning environment was supporting students with ST. Building on prior frameworks, we developed a seven-category analysis tool that enabled us to use a mixture of student discourse, writing, and screen actions to categorize seven ST behaviors in real time. Through using this semi-quantitative tool and subsequent narrative analysis, we found evidence for all seven behavior categories, but not all categories were equally represented. Meanwhile our results suggest that opportunities for students to engage in discourse with both their peers and their teacher supported them with ST. Overall, this study demonstrates how student discourse and student writing can be important evidence of ST and serve as a potential factor to evaluate ST application as part of students’ learning progression. The case study also provides evidence for the positive impact that the implementation of a social constructivist approach has in the context of constructing computational system models.