Implementing Land-Based Learning in Engineering Education: Insights from Land-Based Facilitators
Bibliographic record
Abstract
This paper explores experiences of land-based learning facilitators from Canadian post-secondary institutions to identify strategies for program implementation in engineering education. Land-based learning is rooted in Indigenous pedagogies and recognizes the Land as a teacher. In engineering education curricula, land-based learning is infrequently supported, limiting students’ exposure to non-dominant perspectives and hindering efforts toward reconciliation in the profession. This research involved a qualitative analysis of three articles identified through a scoping review, followed by narrative interviews with their co-authoring program facilitators. The study asked the following: 1) what can we learn from facilitators of land-based learning in Canadian post-secondary institutions? and 2) how can these lessons inform bringing land-based learning into engineering education? Five interconnected themes related to program viability were identified, and two student reflections illustrate the pedagogy’s value. Findings are connected to the TRC Calls to Action, and opportunities for land-based learning in engineering education are noted.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".