Computational Thinking for Science Positions Youth to Be Better Science Learners

Abstract

Computational thinking plays a central and ubiquitous role in many science disciplines and is increasingly prevalent in science instruction and learning experiences. This study empirically examines the computational thinking skills that are particular to engaging in science and science learning and then tests if these skills are predictive of science learning over the course of one semester. Using a sample from 600 middle school science students, we provide the psychometric properties of a computational thinking for science assessment and demonstrate that this construct is a consistent predictor of science content learning. The results demonstrate that the relationship between computational thinking for science and science content learning is consistent across variations in students and classrooms, above and beyond other demonstrated predictors—STEM fascination or scientific sensemaking. Further, the analysis also showed that experience with computer programming languages, especially block languages, is associated with higher levels of computational thinking. The findings reveal implications for research, teaching, and learning, including some implications for advancing equitable opportunities for students to develop computational thinking for science. This paper advances knowledge about how to ensure that students have the dispositions, skills, and knowledge needed to use technology-enabled scientific inquiry practices and to position them for success in science learning.

Keywords

• computational thinking
• activation
• STEM learning
• K-12 learning
• instrument design
• science learning