The Impact of Computational Modeling on Students' Systems Thinking in Science Education: A Meta‐Analysis in K‐16

Systems thinking (ST), a key component of higher‐order thinking, is important in all areas of science education to support scientifically literate citizens who make informed decisions based on science in a global society. Computational modeling (CM) is the practical process of constructing, applying, debugging, and evaluating models by using computational tools. The close mapping of CM processes to ST processes helps students develop ST skills. Yet, there is an absence of research examining the overall impact of CM on students' ST in science education. To fill this gap in the literature, the study used meta‐analysis to analyze 62 effect sizes from 25 studies between 2009 and 2024. The results of the random effects model revealed that CM had a significant positive effect on K‐16 students' ST ( g = 0.470), with the largest effect on applying and evaluating systems ( g = 0.617), followed by identifying system structure ( g = 0.447), and a smaller effect on analyzing system behavior ( g = 0.318). The main findings of the moderator analysis were that CM had a larger impact (a) in biology and ecology disciplines; (b) when using low‐complexity CM tools such as agent‐based block modeling and window‐based applications; (c) when English was the language of instruction; and (d) for the non‐control group and the control group with normative instruction compared to the control group for complex instruction. The study presents specific suggestions for designers of instruction/technology, science teachers, and researchers to better improve students' ST through CM.