Bibliographic record
Abstract
The first step in writing code is understanding the problem to be solved. When this step is not properly completed, students can waste time developing a solution to the wrong problem. Arguably, this tendency is exacerbated by online automatically-tested code submission systems where students work in isolation and sometimes appear to focus more on passing the instructor testcases than on understanding the problem or its solution. We report on an randomized A/B test with 831 CS1 students using an online submission system. Students in the control group wrote small Python functions based on a written description including a docstring with one example. Before the treatment-group students solved the same exercise, they were given a description of the same functions and were asked to provide the corresponding output for three sets of input. We hypothesized that this would decrease the time and attempts required to correctly write the code because students in the treatment group would not waste time on an incorrectly-conceived problem. We found support for this hypothesis on one of the problems but not on the other, and we offer some suggestions as to how this might be explained.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.043 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".