Experiential Education at UBC: Diverse Perspectives on Challenges, Opportunities, and Institutional Support
Bibliographic record
Abstract
Experiential education (EE) is understood and expressed in diverse ways across the University of British Columbia (UBC), including through community service-learning, co-ops, and applied research, among others. UBC’s new Strategic Plan emphasizes experiential education in two of its four core areas of focus: Transformative Learning and Local and Global Engagement. Given the growing emphasis on EE in higher education institutions, this symposium is a timely dialogue from diverse UBC stakeholder perspectives. We frame our session in relation to experiential education as opposed to experiential learning because “education is a teleological practice, that is a practice framed and constituted by purposes…The educational demand is not that students learn, but that they learn something and that they do so for particular reasons, that is, with reference to particular intended ‘outcomes’ (Biesta, 2012, p. 583). In this way, learning and education are closely intertwined, but EE supersedes the techniques and mechanisms that constitute the act of “learning by doing,” and takes up questions pertaining to any combination of ethical imperatives, outcomes, intention, identity, philosophy, and values. Each presenter will share unique situated perspectives and following the presentations, the discussant will facilitate an interactive question and dialogue period with audience members.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.048 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.007 | 0.012 |
| 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".