Adapting Experiential Learning in Times of Uncertainty: Challenges, Strategies, and Recommendations Moving Forward
Bibliographic record
Abstract
Experiential learning offers students the opportunity to gain practical experience with a community or industry partner in their field of study. During the Covid-19 pandemic, many workplaces transitioned from in-person to at-home work environments, and those that did not, often reduced or removed access for non-essential personnel. In this report from the field, multiple viewpoints are shared that emerged from an interdisciplinary panel on experiential learning in the June 2022 Spotlight Series hosted by Teaching and Learning Support Services at the University of Ottawa. These voices “from the field” shed light on the impact of uncertain times on experiential learning and, collectively, focus on identifying challenges, implementing strategies and good practices, and sharing recommendations moving forward.
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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 | 0.070 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.028 | 0.041 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".