An Initial Exploration of Code Diagram Query Effectiveness

In introductory programming, students must develop an accurate mental model of how programming languages work. This model, often called a 'notional machine,' is essential for understanding how a machine interprets and executes code. Existing research highlights the importance of building effective and accurate mental models through code-tracing activities and tools like code visualizations. However, effectively integrating such tools into post-secondary classes remains challenging, especially in large classroom settings. To address this, we have developed Code Diagram Queries (CDQs) for introductory programming courses to help students build mental models of programming language notional machines. CDQs are questions incorporating diagrammatic representations of code at various execution stages to foster student engagement and comprehension of how the code is executed. CDQs were designed to challenge and refine student mental models of code execution. The effectiveness of these CDQs was assessed in an introductory Python programming course, where students in one section engaged with CDQ-based normative assessments (n=94) and students in a control section engaged with non-CDQ normative assessments (n=82). Through comparative evaluations of course performance and visualization engagement, as well as qualitative interview responses, we found preliminary evidence that CDQs helped identify and clarify misconceptions around abstract programming concepts.