Building institutional capacities for students as partners in the design of COVID classrooms
Bibliographic record
Abstract
The COVID-19 pandemic in 2020 posed several challenges to post-secondary institutions, including the move to online learning in a short amount of time. In June 2020, Bishop’s University hired 23 students as online learning and technology consultants (OLTCs) to help faculty prepare for Fall 2020. They underwent training about Students-as-Partners literature, empathetic design, pandemic pedagogy, high-impact practices, and authentic learning design. After their training—which included online modules, simulations, faculty mentorship, and technology training—the program launched in July 2020. In this case study, we deploy SaP literature to solve pedagogical challenges posed by the pandemic, analyze the data collected in the program’s developmental assessment, and share the program’s impact on students, faculty, and the institution more broadly. This program is a key intervention in building institutional capacities for SaP work in a post-COVID higher education context. The outcomes of this case study demonstrate that working with students as partners in the design of COVID classrooms increases students’ social and emotional intelligence, technical and digital literacy skills, critical thinking, project management skills, and other significant learning gains.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".