Cooperating Teacher Mentorship Behaviors and Job Satisfaction as Predictors of Agricultural Education Interns’ Intent to Teach
Bibliographic record
Abstract
School-based Agricultural Education has experienced a shortage of qualified teachers, and almost a quarter of agricultural education graduates do not teach upon graduating. To increase the number of qualified teachers entering the classroom, the reasons for this must be identified and addressed. A possible factor contributing to agricultural education interns’ decision to not teach may be the mentoring provided by cooperating teachers. This study sought to explore cooperating teacher mentorship and job satisfaction factors’ ability to predict spring 2024 interns’ intent to teach. A survey instrument using the student teacher view portion of the Cooperating Teacher Best Practices instrument and the Index of Job Satisfaction was administered to agricultural education interns. Interns perceived social support as the highest type of mentorship behavior (M = 4.43, SD = 0.82) and were satisfied with teaching as a job (M = 3.89, SD = 0.58). Three mentorship behavior types and job satisfaction were related to intent to teach. A logistic regression revealed that role modeling and job satisfaction were predictive of intent to teach. We recommend cooperating teachers involve their interns in various tasks beyond classroom teaching and they should be trained on effective mentorship, including role modeling, social support, and professional support behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".