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Abstract: The study examines how Dynamic Equilibrium (DE) is represented in science national standards and textbooks for high-school biology, chemistry, and physics in the US and Israel. DE, a crucial concept in understanding dynamic systems, is inconsistently represented across educational materials and students encounter difficulties learning about DE. Analyzing 17 textbooks and national standards, the research combines quantitative and qualitative content analysis to assess the frequency and nature of presentation of DE-related phenomena. The study identifies 256 DE-related phenomena, comprising 14% of all phenomena that are studied in science. The primary systems approach used is System Dynamics, which focuses on stocks and rates of flow at one description level. The main representational format is verbal, and computational models are scarcely used. Differences across disciplines and between countries were found. These findings emphasize the need for powerful representations of DE to enhance students' understanding of dynamic systems and improve science education. 

The study investigates whether Dynamic Equilibrium (DE) can serve as a “powerful idea” (Papert, 1980) that bridges phenomena across STEM fields. Through semi-structured interviews with five scientists, we identified three themes: DE was rarely used explicitly but appeared across fields; reflection on DE prompted a more generalized framing; and DE spanned scales from molecules to human populations. These findings suggest DE’s potential as a cross-cutting concept that connects otherwise fragmented content in STEM education. 

Abstract Science educators are integrating more and more computational thinking (CT) activities into their curricula. Proponents of CT offer two motivations: familiarizing students with a realistic depiction of the computational nature of modern scientific practices and encouraging more students from underrepresented backgrounds to pursue careers in science, technology, engineering, and mathematics. However, some studies show that increasing exposure to computing may not necessarily translate to the hypothesized gains in participation by female students and students of color. Therefore, paying close attention to students' engagement in computationally intense science activities is important to finding more impactful ways to promote equitable science education. In this paper, we present an in‐depth analysis of the interactions among a small, racially diverse group of high school students during a chemistry unit with tightly integrated CT activities. We find a salient interaction between the students' engagement with the CT activities and their social identification with publicly recognizable categories such as “enjoys coding” or “finds computing boring.” We show that CT activities in science education can lead to numerous rich interactions that could, if leveraged correctly, allow educators to facilitate more inclusive science classrooms. However, we also show that such opportunities would be missed unless teachers are attentive to them. We discuss the implications of our findings on future work to integrate CT across science curricula and teacher education. 

Abstract Increasing access to computational ideas and practices is one important reason to integrate computational thinking (CT) in science classrooms. While integrating CT into science classrooms broadens exposure to computing, it may not be enough to ensure equitable participation in the science classroom. Equitable participation is crucial because providing students with an environment in which they are able to fully engage and participate in science and computing practices empowers students to learn and continue pursuing CT and science. To foreground equitable participation in CT‐integrated curricula, we undertook a research project in which researchers and teachers examined teacher conceptualizations of equitable participation and how teachers design for equitable participation by modifying a lesson that introduces computational modeling in science. The following research questions guided the study: (1) What are teachers' conceptualizations of equitable participation? (2) How do teachers design for equitable participation through co‐design of a CT‐integrated unit? Our findings suggest that teachers conceptualized and designed for equitable participation in the context of a CT‐integrated curriculum across three primary dimensions: accessibility, inclusion, and relevancy. Our contributions to the field of science teaching and learning are twofold: (1) obtaining an initial understanding of how teachers think about and design for equitable participation is crucial in order to support teachers in their pursuit of creating equitable learning experiences for CT and science learners, and (2) our findings show that we can study teacher conceptualizations and their design choices by examining specific modifications to a CT‐integrated science curriculum. Implications are discussed. 

Integrating computational thinking (CT) in the science classroom presents the opportunity to simultaneously broaden participation in computing, enhance science content learning, and engage students in authentic scientific practice. However, there is a lot more to learn on how teachers might integrate CT activities within their existing curricula. In this work, we describe a process of co-design with researchers and teachers to develop CT-infused science curricula. Specifically, we present a case study of one veteran physics teacher whose conception of CT during a professional development institute changed over time. We use this case study to explore how CT is perceived in physics instruction, a field that has a long history of computational learning opportunities. We also discuss how a co-design process led to the development of a lens through which to identify fruitful opportunities to integrate CT activities in physics curricula which we term computational transparency–purposefully revealing the inner workings of computational tools that students already use in the classroom. 

Teaching science inquiry practices, especially the more contemporary ones, such as computational thinking practices, requires designing newer learning environments and appropriate pedagogical scaffolds. Using such learning environments, when students construct knowledge about disciplinary ideas using inquiry practices, it is important that they make connections between the two. We call such connections epistemic connections, which are about constructing knowledge using science inquiry practices. In this paper, we discuss the design of a computational thinking integrated biology unit as an Emergent Systems Microworlds (ESM) based curriculum. Using Epistemic Network Analysis, we investigate how the design of unit support students’ learning through making epistemic connections. We also analyze the teacher’s pedagogical moves to scaffold making such connections. This work implies that to support students’ epistemic connections between science inquiry practices and disciplinary ideas, it is critical to design restructured learning environments like ESMs, aligned curricular activities and provide appropriate pedagogical scaffolds. 

Next Generation Science Standards foreground science practices as important goals of science education. In this paper, we discuss the design of block-based modeling environments for learning experiences that ask students to actively explore complex systems via computer programming. Specifically, we discuss the implications of the design and selection of the types of blocks given to learners in these environments and how they may affect students’ thinking about the process of modeling and theorizing. We conclude with a discussion of some preliminary findings in this design based research to inform design principles for block-based programming of science phenomena as a medium for learning to build theory. 

There has been a growing interest in the use of computer-based models of scientific phenomena as part of classroom curricula, especially models that learners create for themselves. However, while studies show that constructing computational models of phenomena can serve as a powerful foundation for learning science, this approach has struggled to gain widespread adoption in classrooms because it not only requires teachers to learn sophisticated technological tools (such as computer programming), but it also requires precious instructional time to introduce these tools to students. Moreover, many core scientific topics such as the kinetic molecular theory, natural selection, and electricity are difficult to model even with novice-friendly environments. To address these limitations, we present a novel design approach called phenomenological programming that builds on students' intuitive understanding of real-world objects, patterns, and events to support the construction of agent-based computational models. We present preliminary case studies and discuss their implications for STEM content learning and the learnability and expressive power of phenomenological programming.