Decoding agent-based models supports students’ mechanistic and causal reasoning about scientific phenomena

A common rationale for integrating computational thinking (CT) in science curricula has been the opportunity to increase learning outcomes in both CT and science. While evidence shows that learning to code to create computer models of scientific phenomena improves students’ CT, few studies have demonstrated equivalent increases in science learning. This study aims to investigate the impact of a CT integration curriculum featuring “decoding”, or explicitly mapping between mechanisms in code and p