Prior Programming Experience: A Persistent Performance Gap in CS1 and CS2
Why this work is in the frame
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Bibliographic record
Abstract
Previous work has reported on the advantageous effects of prior experience in CS1, but it remains unclear whether these effects fade over a sequence of introductory programming courses. Furthermore, while student perceptions suggest that prior experience remains important, studies have reported that a student's expectation of their performance is a more accurate predictor of outcome. We aim to confirm if prior experience (formal or informal) provides short-term and long-term advantages in computing courses or if the advantage fades. Furthermore, we explore whether the expectation of performance is a more accurate predictor of student success than informal and formal prior experience. To explore these questions, we deployed surveys in a CS1 course to gauge students' level of prior experience in programming, prediction of final exam grades, and self-efficacy to succeed in university. Grades from CS1 and CS2 were also collected. We observed a persistent (1-letter grade) gap between the performance of students with no prior experience and those with any experience, but we did not observe a noteworthy gap when comparing student performance based on formal or informal experience. We also observed differences in self-efficacy and retention rates between different levels of prior experience. Lastly, we confirm that success in CS1 can be better reflected and predicted by some controllable factors, such as students' perceptions of ability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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