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. © 2017 Wiley Periodicals, Inc. J Res Sci Teach 10: 1249–1273, 2017

The outcome of simultaneously increasing SPS and GS activities in transgenic tobacco, suggests that sucrose is the major determinant of growth and development, and is not affected by changes in N assimilation. Carbon (C) and nitrogen (N) are the major components required for plant growth and the metabolic pathways for C and N assimilation are very closely interlinked. Maintaining an appropriate balance or ratio of sugar to nitrogen metabolites in the cell, is important for the regulation of plant growth and development. To understand how C and N metabolism interact, we manipulated the expression of key genes in C and N metabolism individually and concurrently and checked for the repercussions. Transgenic tobacco plants with a cytosolic soybean glutamine synthetase (GS1) gene and a sucrose phosphate synthase (SPS) gene from maize, both driven by the CaMV 35S promoter were produced. Co-transformants, with both the transgenes were produced by sexual crosses. While GS is the key enzyme in N assimilation, involved in the synthesis of glutamine, SPS plays a key role in C metabolism by catalyzing the synthesis of sucrose. Moreover, to check if nitrate has any role in this interaction, the plants were grown under both low and high nitrogen. The SPS enzyme activity in the SPS and SPS/GS1 co-transformants were the same under both nitrogen regimens. However, the GS activity was lower in the co-transformants compared to the GS1 transformants, specifically under low nitrogen conditions. The GS1/SPS transformants showed a phenotype similar to the SPS transformants, suggesting that sucrose is the major determinant of growth and development in tobacco, and its effect is only marginally affected by increased N assimilation. Sucrose may be functioning in a metabolic capacity or as a signaling molecule.

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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.

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One of the eight Next Generation Science Standards (NGSS) scientific practices is using mathematics and computational thinking (CT). CT is not merely a data analysis tool, but also a problem-solving tool. By utilizing computing concepts, people can sequentially and logically solve complex science and engineering problems. In this article, we share a successful lesson using protein synthesis to teach CT. This lesson focuses primarily on modeling and simulation practices with an extension activity focusing on the computational problem-solving practices of CT. We identify and define five CT concepts within the aforementioned practices that form the foundation of CT: algorithm, abstraction, iteration, branching, and variable. In this lesson, we utilize a game to familiarize students with CT basics, and then use their new CT foundation to design, construct, and evaluate algorithms within the context of protein synthesis. As an optional extension to the lesson, students enter the problem-solving environment to create a program that translates mRNA triplet codons to an amino acid chain. We argue that biology classrooms are ideal contexts for CT learning because biological processes function as a system, and understanding how the system functions requires algorithmic thinking and problem-solving skills.

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