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Enregistrement W3081743109 · doi:10.1080/17408989.2020.1812558

A collaborative approach to teaching about teaching using models-based practice: developing coherence in one PETE module

2020· article· en· W3081743109 sur OpenAlexaffabout
Mats Hordvik, Anders Lund Hage Haugen, Berit Engebretsen, Lasse Møller, Tim Fletcher

Notice bibliographique

RevuePhysical Education and Sport Pedagogy · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiquePhysical Education and Pedagogy
Établissements canadiensBrock University
Organismes subventionnairesnon disponible
Mots-clésNorwegianTeacher educationFriendshipMathematics educationNegotiationPedagogyTeaching methodStudent teachingMicroteachingProfessional developmentPsychologySociologyStudent teacher

Résumé

récupéré en direct d'OpenAlex

Background: The current interest in models-based practice (MBP) as an innovation and framework has necessitated deeper understanding of both what MBP is and how teacher educators teach pre-service teachers about innovative approaches such as MBP. Despite several studies of individual teacher educators enacting MBP, there are few examples of how several teacher educators might go about implementing MBP in physical education teacher education (PETE) programmes.Purpose: The purpose of this study was to develop an understanding of how a collaborative approach to teaching pre-service teachers MBP can support coherence in PETE modules. The study was guided by the question: How do teacher educators teaching in one PETE module negotiate their experience of teaching about teaching as they implement MBP?Method: This collaborative self-study of teacher education practices was conducted in a Norwegian PETE department and involved five teacher educators. The particular setting for the study was one module (what might be described elsewhere as a course or unit of study) that Lasse, Berit, Anders, and Mats were to teach to first year pre-service teachers in one PETE programme (13 females and 37 males). In addition to teaching the module, the four teacher educators acted as critical friends to one another, while Tim (who was based in Canada) offered a second layer of critical friendship to group members both individually and collectively. Data generation included two primary sources: audio records of our meetings in different configurations (21 meetings and approximately 35 hours audio) and our reflective diaries (total of 10 entries and 20 pages). Data analysis involved a five-step dialogic process of ‘thinking with' Loughran’s (2006. Developing a Pedagogy of Teacher Education: Understanding Teaching and Learning About Teaching. London: Routledge) concept of developing a pedagogy of teacher education (Jackson, A. Y., and L. A. Mazzei. 2012. Thinking with Theory in Qualitative Research: Viewing Data Across Multiple Perspectives. London: Routledge).Results: This study provides insights into the affordances of taking a collaborative approach to teaching about teaching MBP and how such a collaborative approach facilitated implementation individually and collectively. Furthermore, the study highlights the ways the several collaborative processes and structures produced the development of a shared language and vision for teaching about teaching MBP. This shared vision led to coherence in how we talked and taught about MBP with each other and with pre-service teachers. These visions helped make our individual and collective practices and their articulation coherent to ourselves and to one another, and also to the pre-service teachers whom we taught.Conclusion: Our understanding is that the development of coherent PETE programmes and the modules within those programmes requires at least: (i) a professional group of teacher educators who are willing to share their understanding, challenges, and uncertainties with one another and with pre-service teachers, (ii) an inquiry-oriented stance towards researching group and departmental beliefs and practices, and (iii) a desire to better understand and share the development of new understandings with colleagues at departmental, national, and/or international levels.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,576
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,144
Tête enseignante GPT0,505
Écart entre enseignants0,361 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2020
Routes d'admission2
Résumé présentoui

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