Cooperating Teacher Mentorship Behaviors and Job Satisfaction as Predictors of Agricultural Education Interns’ Intent to Teach
Notice bibliographique
Résumé
Almost a quarter of agricultural education graduates do not teach SBAE upon graduating (Foster et al., 2024), however, this attrition has not been widely studied and reasons for this decision have not been identified. To increase the number of qualified teachers entering the classroom, the reasons for this attrition must be identified and addressed by universities. This study was conducted to investigate possible relationships between cooperating teacher mentorship behaviors, job satisfaction, and intent to teach in spring 2024 agricultural education interns. A survey, which combined the student teacher view part of Nesbitt and Barry’s (2023) cooperating teacher best practices instrument with Brayfield and Rothe’s (1951) index of job satisfaction, and items for intent to teach and demographics, was emailed to 355 agricultural education interns. The sample was identified through a random cluster sampling of universities offering an agricultural education major. A total of 106 interns responded to the survey, yielding 103 usable responses, constituting a response rate of 30.03%. Interns were surveyed on their perceptions of their cooperating teachers’ mentorship behaviors, job satisfaction, intent to teach, and demographics. Descriptive statistics, correlational analysis, and logistic regression were used to answer the six research objectives that guided this study. Most participants were Caucasian (93.2%, n = 96), and female (80.6%, n = 83). The average cooperating teacher was Caucasian (98.1%, n = 101), male (53.4%, n = 55), and had 15.75 (SD = 9.00) years of teaching experience. The average student teaching placement site was small and in a rural community (64.1%, n = 66). Most (77.7%, n = 80) interns had a job teaching or had intentions of getting a job teaching SBAE for fall 2024. Interns perceived social support behaviors 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). No demographics were related to intent to teach; however, all three mentorship behaviors and job satisfaction were related to intent to teach. A logistic regression revealed that role modeling and job satisfaction were the only variables that were predictive of intent to teach. Based on the results, the study makes recommendations for both practice and research.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».