Flexible deadline policies and practices to support mental health and academic success
Bibliographic record
Abstract
Flexibility in post-secondary education has been gaining traction for years; however, the COVID-19 pandemic accelerated its adoption and visibility, particularly through policies such as extended deadlines and student self-reported absences. Despite the growing presence of these approaches, little is known about the specific skills students need to successfully navigate flexible deadline policies. In this study, we used quantitative and qualitative approaches to investigate student and educator perspectives and experiences with flexible deadline policies at a large Canadian institution. Both surveys and semi-structured interviews were conducted to collect data from consented participants. Quantitative data were analyzed using descriptive statistics and qualitative data were thematically analyzed using NVivo. This study received ethics approval. Findings revealed both shared and divergent perspectives. Students and educators agreed that flexibility reduced stress and improved the quality of submitted work. However, the success of these policies often depended on how, when, and by whom they were used. Students emphasized the need for time management, emotional regulation, and prioritization skills, whereas educators focused on organization and self-motivation. Although students expressed a desire for support and resources to build these skills, educators noted increased workloads and questioned whether this responsibility should fall on them. Participants in this session will gain insight into how flexible deadline policies influence student mental health, what specific skills support effective use of flexibility, and where misalignment exists between student and educator expectations.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".