Assessment of Experiential Education in Prescribed Burning for Current and Future Natural Resource Managers
Bibliographic record
Abstract
Abstract Acquiring experiential prescribed fire education is difficult for college students. In order to evaluate the effects of instruction on students, we surveyed those who were taking or had completed Oklahoma State University’s (OSU) prescribed fire courses since 2000. Of those surveyed, 32 were current students and 99 were former students. We assessed changes in their perception, knowledge, skills, abilities, the total area that they have prescribed burned since leaving OSU, their career trajectories, and how they rated the importance of different types of instruction. One third of the current students had never participated in a prescribed burn before the course; however, after the course, they had participated in seven burns on average. Current students had increased confidence in planning prescribed fire, operating a drip torch, and leading a prescribed fire program. Former students were employed in 20 US states and one Canadian province. Only one third of former students had participated in a prescribed burning association (or similar local cooperative), of which two thirds became federal government employees. Former students had conducted or assisted with 6247 prescribed fires on a total of 803 252 ha after taking the courses, from 2000 to 2013. Experiential learning such as conducting prescribed burns, writing burn plans, and spot fire and equipment training ranked higher in utility than passive types of instruction such as lectures. Of the 37 universities assessed, only eight offer any courses explicitly focused on prescribed fire. Based on our results that demonstrate that both current and former students value experiential fire ecology educational training, we recommend that university curricula should increase the focus on prescribed fire, emphasize experiential learning, and facilitate greater interaction between student and instructor.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".