Implementing a Flexible Delivery Model at a Large Canadian Polytechnic During the COVID-19 Pandemic: Examining the Faculty Perspective
Bibliographic record
Abstract
The COVID-19 pandemic may irreversibly leave its mark on education around the globe. As Canada’s post-secondary institutions pivoted to online learning in March of 2020, faculty and administrators struggled to meet the needs of a new reality. The speed at which schools moved to remote learning was unprecedented (Hodges et al., 2020). Faculty adapted their lessons, administrators adapted their policies, and support staff compiled and created resources.\nRed River College of Applied Arts and Sciences in Winnipeg, Manitoba, was just one institution that struggled to adhere to the ever-changing realities of the health orders the Province of Manitoba implemented. How did we do? This research seeks to analyze instructor feedback, from their perspective, on how they viewed the rollout of the flexible online delivery model and the supports and resources provided to faculty.\nPragmatism guided the philosophical approach of this study, which examined the individual perceptions of faculty as they navigated the move to online and blended learning. The CIPP framework (Stufflebeam, 1971) provided the steps and guidance of the evaluation process. The data collection included 1) an online survey which was offered to all faculty, and 2) one-on-one interviews with volunteer participants. Key themes were analyzed, coded, and then compared between the two instruments.\nThe findings suggest that, while the work of administration and support staff was appreciated by faculty, room remains for improvement to staff resources and the continuation of quality professional development. Central to that, the flexible online delivery model should be adapted and simplified. In addition, the resources to support it should be focused, streamlined, and reorganized to improve accessibility.\nFinally, RRC may consider re-examining its crisis management and emergency management policies. While policies exist for sudden and short-term natural disasters, they were not prepared for an extended disruption of services. If Red River College embedded mentorships and support networks into their future crisis plans, this would facilitate the formal reconnection of managers, faculty, and staff to provide a safety net for wellness and professional development. Participants indicated that the pacing of resource offerings to faculty was intense and overwhelming due to a lack of cohesive leadership and oversight. Addressing this issue in\niv\nRRC’s crises policies could clarify how the administration would, in the future, communicate instructions and designate who would oversee resource development and ensure accountability.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".