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

Abstract

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.

The Development of CT-S Learning Module on The Linear Motion Topic to Promote Computational Thinking Thinking

“…Some studies used programming to teach science content but did not explicitly teach the skills or concepts of computational thinking (Lopez et al, 2015;Mariano et al, 2019). In conclusion, there are still limited efforts in integrating computational and science thinking (Peel et al, 2019).…”

The Code-Centric Nature of Computational Thinking Education: A Review of Trends and Issues in Computational Thinking Education Research

“…As noted by Moreno-León (2018), precious little work has been conducted to understand the affordances of CT unplugged. Nevertheless, our review points to exciting examples of unplugged activities as effective tools to promote student CT and to integrate CT with subjectarea content (e.g., Seoane Pardo, 2018;Peel et al, 2019). Additionally, the studies included in our analysis show that unplugged CT activities can both ease integration of CT with disciplinary content and provide learning experiences that are especially computationally rich (i.e.…”

The Code-Centric Nature of Computational Thinking Education: A Review of Trends and Issues in Computational Thinking Education Research

“…Third, while a handful of studies proposed activities for integrating CT and core content without asking students to write code, only three (Dasgupta et al, 2017;Peel et al, 2019;Sabitzer et al, 2018) actually examined the effectiveness of unplugged approaches to CT instruction. This finding points to the large literature gap surrounding unplugged approaches to CT identified by Moreno-León et al (2018) and reveals a significant research opportunity for those interested in pursuing alternative forms of CT instruction.…”

“…Vakil (2014) uses analysis of systems that create socioeconomic inequalities to engage minority youth in computational thinking while learning social studies content. While Peel, Sadler, and Friedrichsen (2019) report the efficacy of their CTUPDATE framework for teaching natural selection through students creating unplugged algorithms, Niemelä, Partanen, Harsu, Leppänen, and Ihantola (2017) use data drawn from teacher artifacts to propose a CT learning trajectory in mathematics classrooms. Manuscripts within this category also included exemplars of CTCI through mathematics in elementary school (Falloon, 2016) and one of the few examples of CT/Art integration (Rode et al, 2015).…”