Bibliographic record
Abstract
Instructors’ course policies have an important impact on student success in our courses, as well as their perceptions of instructors. One such course policy, which is the focus of this descriptive study, is that of assessment deadlines, more specifically, the various permutations of flexible deadlines. These might include automatic extensions, short or long extensions with or without a penalty, open deadlines for submissions, or a bonus point as an incentive for meeting the deadline. In the present study, we asked students to evaluate these submission deadline policies and how they might affect their wellbeing, procrastination, and perceptions of their instructor. Although they report encountering them most frequently, students don’t perceive extensions with a reduction in grade (e.g., 10% per day) as helpful for their learning and would prefer automatic non-punitive extensions to help support their success and wellbeing. Additionally, students reported that they would have a more positive view of their instructor (nicer and cares about their success) if they had a flexible deadline policy and that it would increase their satisfaction with both the course and instructor. Surprisingly, the largest number of students indicated that their preferred flexible deadline policy would be to receive a bonus for submitting it on time. Implications for policy and student success are discussed. The authors recommend that faculty who use hard/rigid deadlines consider adopting flexible deadlines to better support student success.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".