An Initial Exploration of Code Diagram Query Effectiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it