An investigation of students' and graduates' perspectives on experiential learning in undergraduate environmental programs
Bibliographic record
Abstract
The central argument underlying this research is that experiential learning (EL) can strengthen environmental university programs. Its goal is to demonstrate the benefits of EL from students' and graduates' perspectives and to provide recommendations for its effective implementation into a program. The research utilized a qualitative case study (Environmental Sciences/Studies (ESS) programs at the University of Manitoba, Canada) through focus groups and individual interviews with students and graduates. The results indicate that EL helps develop an understanding of environmental complexities; motivates students to engage at all levels of their ability; is decisive in skill development; engages students in environmental issues with diverse stakeholders; is important in obtaining employment; and it is imperative to connect EL activities to concepts taught in class. The data however, did not show EL to be a significant factor in fostering pro-environmental behaviours in post-secondary environmental students. Overall, the research shows that provided effective implementation, EL can play a significant role in enhancing ESS curriculum and that ESS students place a great value on EL in their education.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".