Pandemic as a Catalyst For More Inclusive Pedagogy in Field-Based Disciplines
Bibliographic record
Abstract
The rapid switch to alternative modes of delivery at the onset of the Covid pandemic in 2020 left Ontario college faculty scrambling to engage students and provide experiential learning opportunities to satisfy course and vocational learning outcomes. This paper presents case studies from Fleming College that illustrate the ways in which curriculum developed for remote, emergency delivery was situated within the framework of Universal Design for Learning (UDL). Using case studies from Fleming College, we demonstrate that efforts to embrace the culturally responsive and inclusive pedagogy that UDL models during the pandemic will remain relevant after the Covid-19 pandemic has subsided. What follows is a description and analysis of the pedagogical strategies and technology-enhanced techniques employed by Fleming faculty to adapt their curriculum and teaching practice to meet the needs of variant learners by incorporating the guidelines of UDL, including multiple means of engagement, representation, and action and expression (Wakefield, 2018). We suggest that these examples from the pandemic supported alternative learning and can continue to do so in ways that enrich the educational culture in Ontario’s post-secondary system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".