Openness and Development or Self-Criticism of Preservice Teachers Watching Videos of Themselves Teaching a Lesson
Notice bibliographique
Résumé
The goal of videotaping lessons in a teacher training process is to bring about change in the behavior of preservice teachers to promote self-awareness, openness, and development. The research question was whether preservice teachers, after watching the video recordings of the lessons they taught, would undergo a process of development and increased openness, or remain on a plane of self-criticism. The present qualitative discourse analysis study examined the data based on the principles of social and emotional learning (SEL). The data were organized according to SEL categories: self-criticism, self-awareness, self-management, social awareness, interaction management, and accepting responsibility. Participants in the study were 100 preservice teachers studying in teacher training institutions in Israel. Most preservice teachers (93%) were found to be critical of themselves, but they also tried to open up to a process of self-awareness (82%) and to change their behavior. Some participants (11%) remained “shackled” to their critical attitude and failed to break through the “wall of self-criticism” and change their behavior. Most participants (76%) showed social awareness and mindfulness of their feelings and of those of others; empathy and ability to read signals sent by their students; listening to others as opposed to being emotionally deaf; attention to the nature of interactions with students during the lesson, to how they moved about the classroom and used their voice, and whether they made eye contact with learners. To bring about change, the teacher must watch, observe, and investigate, and be able to better understand emotional and social situations. Teachers who had experienced social and emotional learning are likely to develop self-awareness and the ability to bring about openness, development, and change in their own behavior and in the behavior of their students.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».