Assessing students' understanding of computational modeling in physics

In secondary physics education, integrating computational modeling enhances students' understanding of physics principles, scientific inquiry, and complex phenomena. Our study adapts the Framework for Modeling Competence (FMC) for application in physics computational modeling, aiming to assess students' modeling competencies and meta-modeling knowledge about computational modeling processes. The adapted framework—the Framework for Computational Modeling Competence (FCMC)—categorizes meta-modeling competencies into five key aspects (<i>Nature</i>, <i>Multiple</i>, <i>Purpose</i>, <i>Testing</i>, and <i>Changing</i>), each with three levels of understanding. Through interviews with Dutch upper-level pre-university students, we identified students’ strengths in understanding the <i>Purpose</i> and <i>Testing</i> aspects of computational models. However, students displayed challenges in comprehending the highest level of understanding within the aspects <i>Nature</i>, <i>Multiple,</i> and <i>Changing</i> models. This study offers insights into how students at this level of education conceptualize computational models and their applications, which may inform improvements in teaching practices aimed at fostering a deeper understanding of computational modeling. Findings suggest that students can effectively use computational models for practical applications. Still, they often struggle with higher-level aspects that require abstract scientific reasoning and understanding models as evolving representations. Addressing these gaps can support the transition from learning computational modeling to understanding physics content and concepts through computational modeling. Our findings support the need for structured approaches in physics education that emphasize the conceptual mastery of computational modeling. This study contributes a refined framework that could help educators assess and foster computational modeling competencies, enabling students to engage more deeply with scientific processes and develop a more comprehensive understanding of computational modeling.