The Value of Time Extensions in Identifying Students Abilities
Bibliographic record
Abstract
Instructors often grant students extensions or grace days to relax deadline constraints. However, researchers have yet to investigate the value of time extensions in identifying students' abilities and why students use them in computing education. Our study shows that scheduling conflicts and underestimation of the coursework were the top two reasons why students were late, providing the very first qualitative analysis results. By categorizing students who used and did not use cost-free time extensions, we found that students who used time extensions had a significantly lower assignment and exam grades than those who chose not to use them. We first observed this phenomenon when looking at grace day usages in a final-year programming course and validated the result by looking at the usage of extended lab time in a first-year programming course. This result suggests that offering a cost-free mechanism such as grace days or a time extension can provide a very early indicator of student abilities and those likely to need assistance.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".