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Computing has become essential in modern-day problem-solving, making computational literacy necessary for practicing scientists and engineers. However, K-12 science education has not reflected this computational shift. Integrating computational thinking (CT) into core science courses is an avenue that can build computational literacies in all students. Integrating CT and science involves using computational tools and methods (including programming) to understand scientific phenomena and solve science-based problems. Integrating CT and science is gaining traction, but widespread implementation is still quite limited. Several barriers have limited the integration and implementation of CT in K-12 science education. Most teachers lack experience with computer science, computing, programming, and CT and therefore are ill-prepared to integrate CT into science courses, leading to low self-efficacy and low confidence in integrating CT. This theoretical paper introduces a novel instructional approach for integrating disciplinary science education with CT using unplugged (computer-free) activities. We have grounded our approach in common computational thinking in STEM frameworks but translate this work into an accessible pedagogical strategy. We begin with an overview and critique of current approaches that integrate CT and science. Next, we introduce the "Computational Thinking through Algorithmic Explanations" (CT-AE) instructional approach. We then explain how CT-AE is informed by constructionist writing-to-learn science theory. Based on a pilot implementation with student learning outcomes, we discuss connections to existing literature and future directions.

This paper reports on the first iteration of the Computational Thinking Summer Institute, a month-long programme in which high school teachers co-designed computationally enhanced mathematics and science curricula with researchers. The co-design process itself was a constructionist learning experience for teachers resulting in constructionist curricula to be used in their own classrooms. We present three case studies to illustrate different ways teachers and researchers divided the labour of co-design and the implications of these different co-design styles for teacher learning and classroom enactment. Specifically, some teachers programmed their own computational tools, while others helped to conceptualise them but left the construction to their co-design partners. Results indicate that constructionist co-design is a promising dual approach to curriculum and professional development but that sometimes these two goals are in tension. Most teachers gained considerable confidence and skills in computational thinking, but sometimes the pressure to finish curriculum development during the institute led teachers to leave construction of computational tools to their co-design partners, limiting their own opportunities for computational learning.

Computational thinking (CT) is a thought process composed of computer science ideas and skills that can be applied to solve problems and better understand the world around us. With the increase in technology and computing, STEM disciplines are becoming interwoven with computing. In order to better prepare students for STEM careers, computational literacy needs to be developed in K-12 education. We advocate the introduction of computational literacy through the incorporation of CT in core science courses, such as biology. Additionally, at least some of this integration should be unplugged, or without computers, so that all schools can participate in developing computational literacy. These lessons integrate unplugged CT and science content to help students develop CT competencies and learn natural selection content simultaneously through a series of lessons in which unplugged CT is leveraged for natural selection learning within varying contexts. In these lessons, students engage in the creation of handwritten algorithmic explanations of natural selection. Students build CT skills while making sense of the process, resulting in converged learning about CT and science. This article presents a description of CT, the specifics of the classroom implementation and lessons, student work and outcomes, and conclusions drawn from this work.

Using socio-scientific issues (SSI) in the science learning environment can promote student motivation to learn and make learning experiences more meaningful. Embedding model-based reasoning opportunities in science lessons can promote substantial science learning. However, how these both work together to promote science learning is a little studied area. We focused on this area by exploring if and how model-based reasoning supported 3rd-grade students in considering complex causal interaction patterns. We co-designed, with four 3rd-grade teachers, a unit on ecosystem interactions using an approach we call model-oriented issue-based learning. The modelling and issue lesson sequences varied across classrooms. In two classrooms, students wrote their positions on a prairie restoration issue, then model-based reasoned about interactions in an ecosystem before re-considering their position on the SSI. In the other two classrooms, students wrote their position on the SSI, participated in an SSI lesson, then rewrote their positions. Students from the two classrooms that model-based reasoned between SSI writings showed growth in their consideration of interaction patterns, while students from the other two classrooms did not. Implications suggest model-based reasoning may support students in considering complex causal interactions while the issue provided a context to reason about these issues in a familiar and relevant way.

Vaping (the use of an e-cigarette) is a common practice among teenagers; 26.7% of high school students report vaping in the last 30 days (Johnston, Miech, O'Malley, Bachman, Schulenberg, and Patrick 2019), making this issue highly relevant. In order to make an informed decision about the use of e-cigarettes, students need to understand the impacts of vaping on their body, which makes the science content engaging and meaningful. Socio-scientific issues (SSI) are real-world issues with clear ties to science ideas and societal concerns, such climate change, resource management, and antibiotic resistance (Sadler, 2004). Using an SSI approach to teaching science can result in increased student learning in science content, nature of science ideas, and understanding of science practices (Eastwood, Sadler, Zeidler, Lewis, and Applebaum, 2012; Klosterman and Sadler, 2010; Zangori, Peel, Kinslow, Friedrichsen, and Sadler, 2017). This article describes a high school biology unit which uses the issue of e-cigarette regulation to teach homeostasis, feedback loops, body system hierarchy, and macromolecules. In order to make an informed decision about the use of e-cigarettes, students need to understand the impacts of vaping on their body, which makes the science content engaging and meaningful.

Computational thinking (CT) is a way of making sense of the natural world and problem solving with computer science concepts and skills. Although CT and science integrations have been called for in the literature, empirical investigations of such integrations are lacking. Prior work in natural selection education indicates students struggle to explain natural selection in different contexts and natural selection misconceptions are common. In this mixed methods study, secondary honors biology students learn natural selection through CT by engaging in the design of unplugged algorithmic explanations. Students learned CT principles and practices and applied them to learn and explain the natural selection process. Algorithmic explanations were used to scaffold transfer of natural selection knowledge across contexts through investigation of three organisms and the creation of generalized natural selection algorithms. Students' pre- and post-unit algorithmic explanations of natural selection were analyzed to answer the following research questions: (a) How do students' conceptions of natural selection change over the course of a CT focused unit? (b) What is the relationship between CT and natural selection in students' algorithmic explanations? (c) What are students' perspectives of learning natural selection with CT? Results indicate students' conceptions of natural selection increased and natural selection misconceptions decreased over the course of the unit. Within their post-unit algorithmic explanations, students used specific CT principles in conjunction with natural selection concepts to explain natural selection, which helped them to learn the details of the natural selection process and correct their natural selection misconceptions. Students indicated the use of CT in unplugged algorithmic explanations in different contexts helped them learn natural selection. This study shows unplugged CT can be used to teach students science content, and it provides an example for further CT and science integrations. Implications for the field are discussed.

This autobiographical piece of collaborative discensus (Gershon, 2008; Gershon, Peel & Bilinovich, 2009) presents the authors' interwoven perspectives about the challenges they faced as they talked about race in a pre-service social studies class. Their work here serves two main purposes. First it is an opportunity for a teacher and two former students to think about the difficulties pre-service social studies teachers had in conceptualizing and talking about the relationship between race and social studies content. Second, this transparent discussion also serves as an opportunity to consider what such understandings of social studies might mean in practice.

Engaging learners in modeling practices can promote substantial science learning, and anchoring science teaching in societal issues can promote student motivation to learn and make learning experiences more meaningful. However, elementary teachers have had few prior experiences with either modeling or socioscientific issues (SSI) and so are unfamiliar with how either construct supports student learning. We created the model-oriented issue-based (MOIB) teaching approach to support teachers in building knowledge of both modeling and SSI and considering how they work together to support student learning. Here we explore how 4 third-grade teachers perceived and characterized MOIB teaching in their classrooms. The teachers were a professional learning community with varying levels of teaching experience. All 4 participated in a professional development workshop to learn MOIB and then taught a MOIB ecosystem curriculum in their classrooms. Each teacher was interviewed individually before and after the curriculum enactment and participated in focus group interviews with her professional learning community at the beginning and conclusion of the curriculum enactment. The teachers' perceptions of SSI were that issue-based instruction was familiar, cross-curricular, and a productive way to contextualize science for their students. They perceived modeling as a way to support equitable learning across their classrooms. Overall, the teachers characterized MOIB teaching as a path to deeper student learning. However, their teaching experience and their own personal passions served to mediate their understanding of and enthusiasm for MOIB. These findings provide important evidence for how elementary teachers perceive and navigate new pedagogical approaches to science teaching.

Carbon cycling is a key natural system that requires robust science literacy to understand how and why climate change is occurring. Studies show that students tend to compartmentalize carbon movement within plants and animals and are challenged to make sense of how carbon cycles on a global scale. Studies also show that students hold faulty models of climate change which thwart their reasoning about how and why climate change occurs. Very few studies have examined how to support students in understanding carbon cycling and reasoning about the relationships between carbon cycling and climate change. To support secondary students in making these connections, we developed a modeling-centered socio-scientific issue (SSI) based curriculum unit taught by the same teacher across three sections of a secondary biology class. At three time points within the 2-week unit, 50 students developed, used, evaluated, and revised their own carbon cycling models to use as sense-making tools for how individual biological processes create a global carbon system and the relationship between carbon cycling and climate change. A small subset of students (n = 16) were also interviewed about their models. We constructed holistic scoring rubrics to document students' model-based reasoning associated with each model and then compared rubric scores across time points to examine potential progression of model-based reasoning over the course of the unit. Results suggest that students' must hold a robust understanding of causal mechanisms for transfer and transformation of carbon in order to make connections between carbon cycling and climate change. Once their understanding of carbon cycling becomes robust, their reasoning shifts in complexity to understand interrelationships between carbon cycling and climate change. Implications from this study suggest that embedding the practices of modeling within a SSI unit supported secondary students in building robust understanding of carbon cycling and the interrelationship to climate change.

The global scale of climate change may seem beyond many high school students' comprehension. To complicate matters, climate change has emerged as a political issue that pits candidates, neighbors, and sometimes teachers and students against each other (Kahan 2015). The "Next Generation Science Standards" (NGSS Lead States 2013) call on science teachers to incorporate climate change concepts into their lessons, which can be a daunting task. This article describes one way to do so: an ecology unit that uses a socio-scientific issue (SSI) approach.

This qualitative study (N==8) reports on high school STEM teachers' perceptions of the contribution of computational thinking (CT) to their classrooms. The participants were part of a four-week professional development program, CT-STEM Summer Institute (CTSI), in which they-together with educational researchers and computational experts-0-designed curricular units to be used in their classrooms. Analyzing exit interviews with the participants, we demonstrate how the program provided an environment for holistic and meaningful teacher growth regarding content, technology. and pedagogy. By positioning teachers first as CT learners and then as equal collaborators to design CT-enhanced curricula for their own contexts, we see evidence of teachers re-evaluating and expanding their ideas of how technology and CT positively impact them and their students in ways that can transform their classrooms. We highlight the ways by which the program design is linked with this change of perceptions. [ABSTRACT FROM AUTHOR]

Antibiotic resistance (ABR) is a significant contemporary socio-scientific issue. To engage in informed reasoning about ABR, students need to understand natural selection. A secondary science unit was designed and implemented, combining an issues-based approach and model-based reasoning, to teach students about natural selection and ABR. This sequential explanatory mixed methods study explored students' explanations of natural selection. Students created model-based explanations (MBEs) about ABR and verbally explained generalised natural selection during semi-structured interviews. Students' MBEs significantly increased in natural selection content, and misconceptions about natural selection and ABR significantly decreased after the unit. However, students' explanations of generalised natural selection differed from ABR explanations. Students struggled to include mutation as the cause of initial variation when explaining generalised natural selection, whereas students included mutation when explaining ABR but often did so after selection pressure. Qualitative analysis indicated students correctly explained ABR or correctly explained generalised natural selection, but none correctly explained both. Students who did understand ABR struggled to apply their understanding to a context other than ABR. This study demonstrates contextual differences in students' natural selection ideas and provides implications for natural selection instruction. While ABR is a compelling issue to contextualise natural selection instruction, it may be problematic. [ABSTRACT FROM AUTHOR]

Alfalfa, like other legumes, establishes a symbiotic relationship with the soil bacteria, Sinorhizobium meliloti, which results in the formation of the root nodules. Nodules contain the bacteria enclosed in a membrane‐bound vesicle, the symbiosome where it fixes atmospheric N2 and converts it into ammonia using the bacterial enzyme, nitrogenase. The ammonia released into the cytoplasm from the symbiosome is assimilated into glutamine (Gln) using carbon skeletons produced by the metabolism of sucrose (Suc), which is imported into the nodules from the leaves. The key enzyme involved in the synthesis of Suc in the leaves is sucrose phosphate synthase (SPS) and glutamine synthetase (GS) is the enzyme with a role in ammonia assimilation in the root nodules. Alfalfa plants, overexpressing SPS or GS, or both showed increased growth and an increase in nodule function. The endogenous genes for the key enzymes in C/N metabolism showed increased expression in the nodules of both sets of transformants. Furthermore, the endogenous SPS and GS genes were also induced in the leaves and nodules of the transformants, irrespective of the transgene, suggesting that the two classes of plants share a common signaling pathway regulating C/N metabolism in the nodules. This study reaffirms the utility of the nodulated legume plant to study C/N interaction and the cross talk between the source and sink for C and N. [ABSTRACT FROM AUTHOR]