The mobility of experiential learning pedagogy: transferring ideas and practices from a large- to a small-campus setting
Bibliographic record
Abstract
In this article, we examine the development of a new, experiential learning human geography and planning course at a smaller campus in Newfoundland, Canada. Our interest is twofold: to consider how pedagogical approaches can be transferred between a large urban campus and a small-town location; and to examine the benefits and complications of such transfers through a reflective examination of the resulting experiential learning program. The article captures the experiences of students, faculty, and university engagement staff in the deployment of the course. From these perspectives, we situate the decision to transfer an existing program across universities, the nuances of adapting such programs to the local context, and the challenge of meeting student desires for experiential learning amidst experimental pedagogical approaches. The paper concludes by suggesting that transferring pedagogical models across locations requires flexibility in terms of ensuring that new modules fit existing program constraints, and that such transfers have the potential to both challenge and positively transform experiential learning processes.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".