Notice bibliographique
Résumé
Today, education is arguably one of the most important facets used to prepare and train students for the future. Society expects that students will acquire the requisite knowledge and competence in their respective fields to prepare them to successfully navigate the demands of today's competitive markets. This expectation has consequences on teachers at all levels of education across many domains. Teachers have a significant role: to prepare students for the future. Competent teachers spend a great deal of time reflecting on their own practices and beliefs, reviewing their teaching goals and evaluating if students have met these goals effectively. The process of reflection in teaching is vital in the preparation and training of students. The purpose of this dissertation therefore was to investigate how statistics professors reflect on their practice. The research questions were designed to access what statistics teachers thought about before giving their courses and before giving two of their classes (hypothesis testing, t-tests). Post class evaluation interviews were conducted to determine where professors thought they were effective and whether they considered a need for change based on student understanding. More specifically, the questions asked: 1) What are the main themes in teacher reflection? 2) How is the content of reflection similar or different between statistics teachers? 3) How is the content of teacher reflection defined in statistics? The design was based on a grounded theory approach whereby data collection consisted solely of interviews conducted throughout the semester: one pre-course interview and two sets of pre-class and post-class interviews. There were 13 participants in total. Participants were either statistics teachers from Quebec Cegeps or university professors. Participants were from the following departments: anthropology, economics, psychology, sociology, education, math, and biology. The analyses dealt with three data sources: pre class reflection, in class reflection, and post class reflection. Data analysis focused on defining the main themes of teacher reflection that emerged from the data, identifying the content of reflection between and within participants in terms of similarities or differences. The pre course interview revealed five main themes: the course (logistics), the teacher as 'self, teaching approaches (what do they say they do in the classroom?), teaching and learning influences, and evaluation of teaching. The pre and post class interviews addressed class planning. What did the professors foresee as any issues students might have in understanding hypothesis testing and t-tests? What changes would they make the next time they taught these concepts? Results showed that the focus of professor reflection centered around three main categories: the class, the student, and the teacher. For the main category, class, some professors reviewed lecture notes, added examples that emphasized authentic statistical problems, and others did no preparation. Student related themes addressed issues students had with understanding statistical content, learning associated difficulties, and student affect. The last category, the teacher, looked at self evaluation, their in-class strategies, methods of promoting and gauging student understanding, and decisions made in class and for future classes. Recommendations for future research include examining the role of experience in professor's level of reflection as well as defining the process of decision making and its role in reflection.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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) | 0,001 | 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 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 ».