Humanities Pedagogy in a Pandemic Context: Maintaining High-impact Practices in Virtual Classrooms
Bibliographic record
Abstract
The arrival of the COVID-19 pandemic and its disruption of post-secondary education turned future planning for online courses into an immediate reality. Given the in-person limitations, courses centred on experiential learning (EL) opportunities were challenging to offer without their curricula undergoing extensive reconsideration. This article highlights how two Italian Studies courses at the University of Toronto (U of T) and University of Toronto Mississauga (UTM), known for their in-person EL opportunities and study abroad, were able to provide highly interactive, global learning spaces online through the deployment of digital technologies and inclusion of redesigned high-impact practices (HIPs). What emerged from these new virtual spaces and adjacent components (e.g. virtual lectures, tours, workshops, assessments) were models for the preservation of academic integrity, frequent peer-to-peer interaction, and innovative ways to put learners into direct contact with Italian culture. Drawing from these successes and from current scholarship in teaching and learning, the courses at the centre of this article – Modern Italian Culture (ITA358/9Y0, U of T) and Italian Culture through Food (ITA235H5, UTM) – are presented as case studies which champion the inclusion of digital learning tools, open access and virtual opportunities across humanities curricula, regardless of delivery mode.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".