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Concepts covered in introductory electricity and magnetism such as electric and magnetic field vectors, solenoids, and electromagnetic waves are difficult concepts for students to visualize. Part of this difficulty may be due to the representation of three-dimensional objects on the two-dimensional planes of course textbooks and classroom whiteboards. The use of two-dimensional platforms limits the visualization of phenomena such as the vector field of a point charge or test charges traveling in the three-dimensional space of an electric field. In addition, working in two dimensions may add to students’ difficulties orienting their body correctly to use the right-hand rule when determining the direction of a magnetic field. These difficulties in visualization may limit the conceptual understanding of these fundamental topics. To promote conceptual understanding of electromagnetism we are cyclically developing and researching three spatial computing 3D environments covering electric fields, magnetic fields and electromagnetic waves. Each environment will be developed and tested in both augmented and virtual reality. The first of our environments, the electric field, has been built and tested in augmented reality (AR) with introductory physics students in the Fall 2023 semester. Our study is currently in phase IV of the National Science Foundation’s Design and Development Cycle. Data collected during phase II is being analyzed to support revision to the environment as well as data collection protocols. This article will outline findings from qualitative data gathered during the AR experience as well as during student post interviews following participation in the electric field space. These findings are characterized and then responded to with recommendations for the design team regarding content and testing procedures. In what follows, we first present a framework listing current knowledge regarding students' difficulties learning electric fields and how these guided our design of this electric field augmented reality environment. We next present themes that emerged from discussions during the experience as well as the post interviews. We conclude with suggestions to inform our second round of environmental design. 

We demonstrate how a chemistry unit on evaporative cooling with an embedded system modeling tool called SageModeler can help students in the critical work of exploring and understanding complex problems. The framework for scaffolding students in ST and CT through modeling can be applied to other disciplines—including climate change, ecosystems, population dynamics, forces and motion, and many other topics—in order to foster student participation in systems thinking and computational thinking through modeling. In doing so, they will build skills to help them solve complex problems of the future. 

We developed the Systems Thinking (ST) and Computational Thinking (CT) Identification Tool (ID Tool) to identify student involvement in ST and CT as they construct and revise computational models. Our ID Tool builds off the ST and CT Through Modeling Framework, emphasizing the synergistic relationship between ST and CT and demonstrating how both can be supported through computational modeling. This paper describes the process of designing and validating the ID Tool with special emphasis on the observable indicators of testing and debugging computational models. We collected 75 hours of students’ interactions with a computational modeling tool and analyzed them using the ID Tool to characterize students’ use of ST and CT when involved in modeling. The results suggest that the ID Tool has the potential to allow researchers and practitioners to identify student involvement in various aspects of ST and CT as they construct and revise computational models. 

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. 

This study explores how to support teachers in developing and implementing effective pedagogical strategies to promote students in making sense of phenomena through computational modeling in remote contexts. Qualitative analyses of eight teachers’ interviews were conducted to characterize their pedagogical strategies to achieve three-dimensional learning. Findings indicate that typical teacher strategies include the teacher and students co-constructing a model and using whole class or group discussions to support students’ modeling practices. 

Computational Thinking (CT) is increasingly being targeted as a pedagogical goal for science education. As such, researchers and teachers should collaborate to scaffold student engagement with CT alongside new technology and curricula. We interviewed two high school teachers who implemented a unit using dynamic modeling software to examine how they supported student engagement with CT through modeling practices. Based on their interviews, they believed that they supported student engagement in CT and modeling through preliminary activities, conducting classroom demonstrations of the phenomenon, and engaging students in model revisions through dialogue. 

Computational Thinking (CT) is increasingly being targeted as a pedagogical goal for science education. As such, researchers and teachers should collaborate to scaffold student engagement with CT alongside new technology and curricula. We interviewed two high school teachers who implemented a unit using dynamic modeling software to examine how they supported student engagement with CT through modeling practices. Based on their interviews, they believed that they supported student engagement in CT and modeling through preliminary activities, conducting classroom demonstrations of the phenomenon, and engaging students in model revisions through dialogue. 

As human society advances, new scientific challenges are constantly emerging. The use of systems thinking (ST) and computational thinking (CT) can help elucidate these problems and bring us closer to a possible solution. The construction and use of models is one of the most widely used tools when trying to understand systems. In this paper, we examine four case studies of student pairs who were engaged in building and using system models in an NGSS-aligned project-based learning unit on chemical kinetics. Using a theoretical framework that describes how CT and ST practices are manifested in the modeling process we examine the progression of students’ models during their model revisions and explore strategies they employ to overcome modeling challenges they face. We discuss some suggestions to scaffold students’ progression in constructing computational system models and prepare teachers to support their students in engaging in CT and ST practices.