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.

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.

Diffusion and osmosis are important biological concepts that students often struggle to understand. These are important concepts because they are the basis for many complex biological processes, such as photosynthesis and cellular respiration. We examine a wide variety of representations used by experienced teachers to teach diffusion and osmosis. To help teachers select appropriate representations for their students, we briefly describe each representation and discuss its pros and cons. After teachers select representations, we offer recommendations for sequencing them. We recommend beginning with macroscopic-level representations that easily allow students to visualize the phenomenon, then moving to microscopic-level representations (cell-level), and finally exploring the phenomenon at the molecular level using virtual representations.