Investigating the Progression of the Mental Models Formed by Programmers Learning Parallel Programming
Why this work is in the frame
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Bibliographic record
Abstract
Research on mental model representations developed by programmers during parallel program comprehension is important for informing and advancing teaching methods including model-based learning and visualizations. The goals of the research presented here were to determine: how the mental models of programmers change and develop as they learn parallel programming, the quality of their mental models after learning parallel programming, and what type of information is part of their mental models when examining code for the presence of data races. Participants were experienced C programmers and included both university students and professionals. The mental models of participants were analyzed by having them perform a code tracing task where they externalized their mental models by drawing diagrams while tracing the execution of parallel code. We also analyzed their mental models by having participants determine the presence of data races in parallel code and then answer multiple choice and open-ended questions related to the code. The results presented in this article indicate that programmers’ mental models progress from a weaker execution model and a stronger situation model before learning parallel programming, to a stronger execution model and a weaker situation model after learning parallel programming. The thematic analysis of the open-ended responses that indicate what components of code programmers used to determine whether or not a data race was present provides insight into the topics that should be emphasized when teaching parallel programming.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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