Stakeholder Perspectives of Experiential Education in Tertiary Institutions and Learning From COVID-19
Bibliographic record
Abstract
Background Many universities in Canada offer experiential education (EE) opportunities for students that are both field-based and on-campus. Despite a commitment to EE, there is a paucity of information about various stakeholder perspectives of EE and the equity implications of the different approaches to EE. Furthermore, it is unclear how EE programs at universities changed in response to the COVID-19 pandemic and related restrictions. Purpose This study aims to explore stakeholder perspectives of EE experiences and understand the shifts to EE, perhaps towards more equitable and accessible EE opportunities, prompted by the COVID-19 pandemic. Methodology/Approach We used an exploratory case study approach involving a survey with university students, interviews with university instructors and community organizations, and a document review, to understand stakeholder perspectives and shifts to EE due to COVID-19. Findings/Conclusions Findings suggest that there are consistent benefits of EE, and barriers to EE, across student, instructor, and organization perspectives, where learning from changes prompted by the pandemic could be beneficial to increase equity in EE. Implications We recommend that instructors and institutions continue to work in partnership with students and community organizations to build virtual, on-campus, and local field-based EE (FBEE) opportunities that aim to increase equitable access and impact.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".