![]() ![]() We also explored the characteristics of CT interventions that aid the transfer of learning by qualitatively assessing the identified studies. A meta‐analysis of these studies identified a generally significant effect of the transfer of CT skills to other subject areas. We identified and summarized these effects in the fields of mathematics, science, engineering and the humanities. We carefully selected and reviewed 55 empirical studies from leading bibliographic databases and examined the transfer of CT using a meta‐analysis and a qualitative synthesis. Our goal is to investigate the transfer effects of CT in different subject areas and examine the educational characteristics of CT intervention approaches that promote the transfer of learning. ![]() Instructional approaches to CT development and assessment in the field of computer science have attracted global attention, but the influence of CT skills on other subject areas is under‐researched. Implications for the field are discussed.Ĭomputational thinking (CT) is regarded as an essential 21st‐century skill, and attempts have been made to integrate it into other subjects. This study shows unplugged CT can be used to teach students science content, and it provides an example for further CT and science integrations. Students indicated the use of CT in unplugged algorithmic explanations in different contexts helped them learn natural selection. 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' 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. 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 learned CT principles and practices and applied them to learn and explain the natural selection process. In this mixed methods study, secondary honors biology students learn natural selection through CT by engaging in the design of unplugged algorithmic explanations. Prior work in natural selection education indicates students struggle to explain natural selection in different contexts and natural selection misconceptions are common. ![]() Although CT and science integrations have been called for in the literature, empirical investigations of such integrations are lacking. Computational thinking (CT) is a way of making sense of the natural world and problem solving with computer science concepts and skills. ![]()
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