Piloting a Subtractive Grading Scheme in Engineering
Bibliographic record
Abstract
Many grading schemes in post-secondary engineering education utilize an additive grading scheme, where each student starts with a grade of 0% and slowly builds up towards 100%. This study looks to examine the opposite, using a so-called subtractive method wherein each student would start at a grade of 100% and then, for each assessment, would aim to maintain that grade. Nineteen (19) students participated in the study across three courses, with 15 and 4 students in the subtractive and additive schemes, respectively. Survey data showed that students feel that the subtractive grading scheme is fair, and there has been no observed statistically significant difference in the overall performance of students in the additive vs. subtractive grading schemes. This pilot study has shown that the student perspective on differing grading schemes are similar and that these schemes could be broadly implemented as an option for students when enrolling in a course.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".