Computational thinking for using models of water flow in environmental systems: Intertwining three dimensions in a learning progression

Nearly a decade ago, the Framework for K-12 Science Education argued for the need to intertwine science and engineering practices, disciplinary core ideas, and crosscutting concepts in performance expectations. However, there are few empirical examples for how intertwining three dimensions facilitates learning. In this study, we used a learning progressions approach to examine how student engagement in computational thinking (science and engineering practice) intertwines with learning about the flow of water through environmental systems (disciplinary core ideas) and understanding of systems and system models (crosscutting concept). We developed three secondary-level curriculum units situated in current groundwater contamination and urban flooding contexts. Units included specially designed NetLogo computational models. Post-assessments measured student performances in computational thinking processes and understanding of hydrologic systems. Using item response theory in our analysis, we identified distinct levels of performance on a learning progression. At the lower end, literal model users interacted with models and manipulated model interfaces to achieve a specified goal. In the middle, Model Technicians used computational models to solve real-world problems. At the upper end, principle-based model users used computational thinking processes and principles related to systems modeling and hydrology to explain how the models worked to predict water flow. Differences between performances of literal model users, model technicians, and principle-based model users reflected shifts in how students made sense of the systems and system models crosscutting concept. These shifts in performances aligned with progress in computational thinking practices and finally with use of hydrology disciplinary core ideas. These findings contribute to understanding of how science and engineering practices, disciplinary core ideas, and crosscutting concepts intertwine during learning; how computational thinking practices develop; and how computational thinking about system models facilitates learning for environmental science literacy.