Algorithms, Abstractions, and Iterations: Teaching Computational Thinking Using Protein Synthesis Translation

Abstract

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.

Selecting Socio-scientific Issues for Teaching

“…This study answers Vangrieken et al's call for research on the actual process of collaboration. In addition, SSI research has focused on teachers' use of prepared SSI-based curricular materials and/or teachers under close guidance or collaboration with researchers (e.g., Klosterman and Sadler 2010;Marks and Eilks 2009;Peel and Friedrichsen 2018;Presley et al 2013). In contrast, we highlighted what teachers did and what they did together when challenged to develop SSI teaching materials for their own use, beginning to fill a gap in scholarship, as well as highlight their processes from the teachers' points of view.…”

“…When teachers implement SSI-based curricula, those curricula tend to be designed by researchers (e.g., Eastwood and Sadler 2013;Eilks 2002;Klosterman and Sadler 2010;Peel and Friedrichsen 2018) or generated through close collaboration between researchers and a few teachers to design SSI units, e.g., climate change (Zangori et al 2017), antibiotic resistance (Friedrichsen et al 2016), shower gels, and musk fragrances (Marks and Eilks 2010). Although these studies provide important insights on teacher implementation of SSIbased curricula, they shed little light on how teachers grapple with the process of SSI selection.…”

Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

“…Prior to any instruction students were asked to create an algorithm, or sequence of steps, explaining natural selection to establish a baseline and engage students in nonspecific transfer. Students were introduced to the CT principles of branching, iteration, methods, and variables with Lightbot, as described in Peel and Friedrichsen (). Lightbot is a digital drag and drop game where students create sequences of steps for a robot to follow.…”

Building a computational model of food webs: Impacts on middle school students' computational and systems thinking skills

“…They found significant scientific concept use gains along with a significant decrease in misconceptions. Regarding the relationship between CT and natural selection factors, they found a strong correlation between the uses of CT‐related branching or conditional logic, that is “[c]hoosing a path, if/then/else statements … example: Driving through traffic lights: IF the light is green, THEN you go, ELSE you slow down and stop” (Peel & Friedrichsen, 2018, p. 22) to explain differential survival. Last, the authors also found that students expressed that the generalized algorithmic explanation helped them make sense of natural selection.…”