Replacing Power with Flexible Structure: Implementing Flexible Deadlines to Improve Student Learning Experiences
Bibliographic record
Abstract
Traditional course deadline policies uphold the myth of the “normal” student, assuming students face few and equal barriers to completing work on time. In contrast, flexible deadline policies acknowledge that students face unequal barriers and seek to mitigate them. Flexible deadline policies maintain structure while transferring some decision-making power from the instructor into the hands of the student. These practices align with current pedagogical movements in higher education that seek to empower all students to meet learning goals. This study explores student perspectives on, and use of, proactive extensions built into a recent university course. We compare extension use in low-stake, high-stake, individual, and team assignments; observe how extension use changed over the term; and examine student self-reported responses about the policy. Students unanimously agreed that the proactive extension policy was valuable to their learning. They reported that the proactive extensions enabled them to improve the quality of their work and to better manage their academic workloads, acting as self-regulated learners. They also frequently described reduced stress as a benefit. Extensions generally appeared to be used as needed rather than encouraging procrastination. Students also identified that the need to request extensions in other courses was a barrier. The instructor of this course also benefitted from implementing this policy. Faculty should consider implementing flexible deadline policies to improve student learning experiences and to contribute to a more equitable and inclusive learning environment.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".