Predicting Computational Thinking in Elementary Science Lessons Using a Multilevel Model Approach

Abstract

Computational thinking (CT) is an essential problem-solving skill that students need to successfully live and work with developing technologies. There is an increasing call in the literature by researchers and policy leaders to integrate CT at the elementary level into core subjects to provide early and equitable access for all students. While some critics may claim the concepts and skills of CT are developmentally advanced for elementary age students, subjects such as science can provide real-world and relevant problems to which foundational CT components can be applied. By assessing how CT concepts and approaches integrate authentically into current science lessons, policymakers, and district leaders can be more intentional in supporting implementation efforts. This research used an exploratory survey design to examine the frequencies of CT concepts (decomposition, algorithms, abstraction, and pattern recognition) and approaches (tinkering, creating, debugging, perseverance, and collaboration) that exist in science in K–5 schools in a northeast state in the United States as reported by elementary science teachers (n = 259). Hierarchical linear modeling was used to analyze the influence of teacher and district factors on the amount of time CT concepts and approaches were integrated in the science lessons. Experience, grade level, confidence, and participation in a research–practice partnership were found to be significant predictors of CT. This study contributes to a better understanding of variables affecting CT teaching frequency that can be leveraged to impact reform efforts supporting CT integration in science.