Understanding teacher well-being and motivation : measurement, theory, and change over time
Notice bibliographique
Résumé
Teacher well-being and motivation play important roles in teacher and student experiences at school. When teachers are faring well and feeling motivated to teach, they are more effective in their teaching, leave the profession less often, and promote motivation and achievement among their students. In this dissertation, three studies that investigated teacher well-being and motivation were conducted with the aim of advancing our understanding of the two constructs, as well as how they can be promoted among teachers. Study 1 involved conceptualising, developing, and testing the Teacher Well-Being Scale, which measures three factors of teacher well-being: workload well-being, organisational well-being, and student interaction well-being. Among a sample of 603 practicing teachers, results revealed that the new measure functioned similarly across the different demographic groups in the sample and that the three factors of well-being related as expected with other constructs (stress, job satisfaction, and flourishing). Study 2 involved elaborating and testing an explanatory model of teacher well-being, motivation, job satisfaction, and affective organisational commitment that was based in self-determination theory (Deci & Ryan, 1985, 2002). Using the same sample as Study 1, structural equation modelling provided support for the model’s main relationships. In addition, there were some unexpected findings that provide directions for future research (e.g., a double-sided view of autonomy revealing that it can be associated with positive and negative types of motivation). Study 3 involved examining growth curve models of change in teacher well-being and self-efficacy for teaching over two to three months. Among a sample of 71 practicing teachers, the findings showed that teacher well-being was stable over time, whereas self-efficacy for classroom management increased (the other two types of self-efficacy that were examined, self-efficacy for student engagement and instructional strategies, did not change over time). Findings also revealed the significance of the basic psychological needs (autonomy, competence, and relatedness) in predicting teacher well-being and self-efficacy. Taken together, the three studies help to improve our understanding of the highly important variables of teacher well-being and motivation. Implications of the findings for both research and practice are discussed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».