A tour of wood product manufacturing facilities in British Columbia as an example of experiential learning
Bibliographic record
Abstract
Abstract The provision of meaningful experiential learning opportunities for undergraduate students in natural resources‐based programs is particularly important. In 2017, the Department of Wood Science at the University of British Columbia reintroduced a dormant, 1‐week tour of manufacturing operations for students in the Wood Products Processing undergraduate program. This article describes the background to the reinstatement of the tour and some of the important logistical factors involved, including the timing of the tour in the academic calendar, the scheduling of manufacturing facilities visited, transport and accommodation considerations, and planning time at an outdoor education camp. The desired learning outcomes are discussed together with some of the challenges developing and enacting appropriate evaluations connected to them. An initial single, written technical report was later supplemented by evaluation components better connected to the desired experiential learning outcomes. Aspects of safety, participation, and punctuality were added, as were daily quizzes. The daily quizzes have evolved from paper‐based questions requiring statement responses to online, objective quizzes, which provided more timely feedback to students. The pros and cons of additional overview and/or summary sessions are discussed in relation to encouraging greater student engagement in achieving the desired learning outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".