To make sense of our interconnected and algorithm driven world, students increasingly need proficiency with computational thinking (CT), systems thinking (ST), and computational modeling. One aspect of computational modeling that can support students with CT, ST, and modeling is testing and debugging. Testing and debugging enables students to analyze and interpret model output to identify aspects that need improvement. Students can subsequently revise their own models or provide meaningful feedback to their peers. Testing and debugging has long been identified as a key learning goal in both science education and computer science. However, current evidence suggests that students have limited opportunities to engage in testing and debugging in K-12 science classrooms. Additionally, both curricular and teacher supports for testing and debugging remain understudied. As such I set out to investigate how students test and debug computational models within a supportive learning environment and how two teachers supported students with testing and debugging in the context of a high school chemistry unit. Through this research, I developed the ST and CT Identification Tool to categorize student testing and debugging behaviors during computational modeling. Using this tool, I identified that students implemented a variety of different pattens of testing and debugging during computational modeling. This suggests that teachers and curricular designers should embrace a diversity of testing and debugging pathways when supporting students with testing and debugging. Likewise, my analysis of pedagogical strategies provides evidence that using synergistic scaffolding and presenting students with clear rationales for engaging with different aspects of testing and debugging encourages students to utilize testing and debugging as a means of improving their computational models.
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