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Record W3000094376 · doi:10.35362/rie8213702

Fazer investigação self-study na formação inicial de professores: A importância de ouvir os alunos futuros professores

2020· article· pt· W3000094376 on OpenAlexaff
Tom Russell, María Assunção Flores

Bibliographic record

VenueRevista Iberoamericana de Educación · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsHumanitiesPhilosophyPsychology

Abstract

fetched live from OpenAlex

Os dois self-studies da prática de formação de professores que se descrevem neste artigo foram conduzidos de forma colaborativa em dois países diferentes – Portugal e Canadá. O enfoque principal deste artigo reside na importância de ouvir os alunos futuros professores e no modo como os autores interpretaram e responderam ao que ouviram. De um modo geral, ouvir e responder proporcionou respostas positivas por parte dos alunos futuros professores. Este artigo ilustra as várias maneiras de ouvir os alunos e os seus efeitos positivos quer nos alunos futuros professores, quer nos formadores. A pedagogia da formação de professores constitui uma preocupação central para os que se encontram a aprender a ensinar: o modo como são ensinados conta, pois eles estão sempre a pensar sobre o modo como eles próprios irão ensinar. Os autores defendem a importância de tornar transparente a pedagogia dos formadores de professores, o que passa pela explicitação da nossa racionalidade e da explicação do como e do porquê inerentes às mudanças decorrentes dos comentários dos alunos futuros professores. Este artigo inclui o contexto da investigação no âmbito do self-study da prática dos formadores de professores, ilustra o self-study com dados recolhidos junto dos alunos nas aulas dos autores e sintetiza o que os autores aprenderam em termos de mudança de pressupostos e de novas práticas pedagógicas.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.402
Teacher spread0.265 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevista Iberoamericana de EducaciónSame topicTeacher Education and Leadership StudiesFrench-language works237,207