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Record W2923392955

Faculty Supervisors’ Conceptions of Clinical Supervision in Early Childhood Teachers’ Education

2019· article· en· W2923392955 on OpenAlexaff
Mariel Gómez

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPracticumTeacher educationContext (archaeology)PedagogyMedical educationPsychologyEarly childhood educationClinical supervisionMedicine
DOInot available

Abstract

fetched live from OpenAlex

Over the past 30 years, studies of teaching and learning in schools have demonstrated that, to improve our educational systems, there is no better strategy than a significant and sustained investment in well-prepared teachers. Within the landscape of teacher development, a critical part of the terrain is clinical experience, which refers to supervised, direct experiences in teaching provided for pre-service teachers. Clinical experiences have been identified as crucial to strengthen teacher education becausethey offer students an essential bridge between the conceptual tools gained in a university classroom and the realities that occur in the social and physical context of schools. However, despite the growing recognition of the importance of clinical experiences on teachers’ preparation, supervision of practicum remains an underutilized resource in teacher education and the work of faculty supervisors is still understudied. The paucity of studies examining and documenting the work of faculty supervisors is a reflection of the weak visibility that this key actor has in teachers’ education. This paper presents the preliminary results of a study using phenomenograpic inquiry to identify the different ways in which faculty supervisors of Chilean early childhood teachers experience and understand supervision of pre-service teachers during practicum.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.114
GPT teacher head0.398
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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