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Record 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 on OpenAlexaffabout
Mats Hordvik, Anders Lund Hage Haugen, Berit Engebretsen, Lasse Møller, Tim Fletcher

Bibliographic record

VenuePhysical Education and Sport Pedagogy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock University
Fundersnot available
KeywordsNorwegianTeacher educationFriendshipMathematics educationNegotiationPedagogyTeaching methodStudent teachingMicroteachingProfessional developmentPsychologySociologyStudent teacher

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.144
GPT teacher head0.505
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2020
Admission routes2
Has abstractyes

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