Relationship Between Cognitive Types Of Teacher Content Knowledge And Knowing-To Act: A Mixed Methods Study Of Mexican Borderland Middle School Teachers
Notice bibliographique
Résumé
This study analyzed middle school mathematics teachers' content knowledge and its relationship with teachers' "knowing-to act" ability. Understanding what kinds of knowledge has a direct influence on teaching practices and student learning is critical in order to improve teacher education programs and professional development. An Explanatory sequential mixed methods design was used in the study. It involved collecting quantitative data and explaining the quantitative results with in-depth qualitative data. In the quantitative phase of the study, two surveys were administered to N=70 middle school mathematics teachers in the Mexican borderland to assess whether their mathematical content knowledge was related to their "knowing-to act". The correlational analysis of these surveys showed no statistically significant correlation between overall mathematical teacher content knowledge (total score on TCKS) and the "knowing-to act" ability (KtAS). However, a statistically significant correlation between the specific cognitive type of teacher knowledge - models and generalizations - and the "knowing-to act" was reported. The qualitative phase provided a deeper understanding of the quantitative results: the exploration of the "knowing-to act" enacted during mathematics instruction with four middle school mathematics teachers from the quantitative sample was conducted using a specifically designed classroom observation protocol. The analysis of the observation together with the results of the KtAS provided revealing differences among teacher's actions observed and the teacher's responses on the survey. Overall, the analysis of the qualitative data reflected findings from the quantitative phase of the study. Two main findings were reported in the study: (a) the lack of correlation between the mathematical teachers content knowledge and their "knowing-to act" during teaching mathematics, which was reflected by the data collected from the case studies; (b) a statistically significant correlation between knowledge of models and generalizations (T3), which added to the discussion that teachers who performed higher on the cognitive type 3 items of the TCKS were able to know how to act at the moment more frequently than teachers with a limited T3. This research provided in-service teachers and other participants in the education field with awareness about the active knowledge that is needed to enact the teachers' knowing-to act in teacher preparation programs in Mexico that can be used to support teachers and students in the United States. Further studies are needed in which the association and exploration of other kinds of knowledge for teaching mathematics and students learning can be analyzed. For instance, research on "knowing-to act" in the United States or other countries can also be worthy of a study; how would teachers act in KtA situations during their mathematics instruction in the USA, Canada, or Russia? In addition, this study allows comparisons among Mexico and countries where data is already collected in regards to teacher knowledge in the area of Mathematics, such as Russia, the U.S., Latin American countries, and other countries that participated in the TEDS-M Study 2012.
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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 | 0,006 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».