Learning Computational Thinking Through Unplugged Algorithmic Explanations of Natural Selection

Computational thinking (CT) is becoming increasingly important for K-12 science education, thus warranting new integrations of CT and science content. This intervention study integrated CT through unplugged, or handwritten, algorithmic explanations of natural selection. As students investigated natural selection in varying contexts (specific and context-general), students created explanations based on evidence of natural selection by using algorithm concepts and engaging in CT practices. Students' CT learning over time was analyzed through algorithmic explanations created during the unit. Research questions guiding the investigation were: (1) How do students learn CT over the course of a CT and science integrated unit? (2) What are students' perspectives of learning CT in an integrated unit? (3) How do students come to think about CT and its applications? Students' CT competencies significantly increased from pre- to post-unit. Students indicated creating algorithmic explanations helped them learn natural selection and develop CT competencies. At the end of the unit, students recognized the universal application of CT as a way to logically and clearly explain processes. Implications of this work are that CT can be used as a science practice that helps students simultaneously learn science and CT practice competencies. Moreover, these student learning outcomes can be achieved with unplugged, or computer-free, CT.