Computational Thinking

The role of computation in science is ever-expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real-world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10-lesson instructional units for middle-school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model’s implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model’s core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers.

Contemporary science is a field that is becoming increasingly computational. Today’s scientists not only leverage computational tools to conduct their investigations, they often must contribute to the design of the computational tools for their specific research. From a science education perspective, for students to learn authentic science practices, students must learn to use the tools of the trade.

Abstract: In order to expand opportunities to learn computer science (CS), there is a growing push for inclusion of CS concepts and practices, such as computational thinking (CT), in required subjects like science. Integrated, transdisciplinary (CS/CT+X) approaches have shown promise for broadening access to CS and CT learning opportunities, addressing potential self-selection bias associated with elective CS coursework and afterschool programs, and promoting a more expansive and authentic contextualization of CS work.