Professional Learning Experiences in a Field‐Based Course: Student Perceptions and Preferences
Bibliographic record
Abstract
Core Ideas Participants in this study placed high value in the professional learning experiences. The job market can quickly impact student perception of professional learning experiences. Professional learning experiences can give students a good sense of related careers. Timing and site location can impact student preferences for field course experiences. Two professional learning experiences were added to an existing field‐based soil science course. Including relevant professional learning experiences in field‐based courses for non‐soil science majors can provide the opportunity for these students to apply soil science skills and knowledge in the context of the profession most relevant to their area of study. As participation of government and industry requires additional logistics and resources, it is important to assess the added value to the student experience. Using an online survey, data was collected over 4 years. Students most preferred the professional–government (Pgv) learning experience followed by the academic experience (Acm) and the professional–industry (Pid) experience (55, 31, and 14%, respectively). Students perceived the Pgv experience as most relevant to their future career plans, followed by the Pid and Acm experiences (41, 32, and 27%, respectively). Student comments indicated that they valued both Pgv and Pid experiences for their authenticity. Yearly variation in the results indicated that the Pid experience was least preferred and considered least relevant to career plans in 2014 and 2015, coinciding with a crash in crude oil prices and economic downturn in western Canada. Inclusion of professional learning experiences added considerable value to the student experience, but student preferences and perceptions of these experiences can be altered by relevant employment market and economic forces.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".