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Record W3155250354 · doi:10.21083/ajote.v10i1.6371

Modelling in Teacher Education: Beliefs of Teacher Educators in Rwanda

2021· article· en· W3155250354 on OpenAlexvenueno aff
Emmanuel Niyibizi

Bibliographic record

VenueAfrican Journal of Teacher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTeacher educationRelevance (law)PsychologyProfessional developmentPedagogyEmpirical researchMathematics educationQuality (philosophy)Exploratory researchQualitative researchTeacher qualityTeacher preparationProcess (computing)SociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The study aims to explore teacher educators’ beliefs about role modelling in teacher education in Rwanda. This study hopes to contribute to filling the gap created by limited empirical study available on teacher educators and the relevance of role modelling for high quality teacher training. The study was designed as an exploratory qualitative research using semi-structured interviews of 20 purposively and conveniently selected teacher educators. Content analysis was used to analyze collected data. The findings reveal that participating teacher educators believe that role modelling is an important component in the process of teaching teachers. Moreover, participants hold beliefs about role modelling as implicit exemplar practices and behavior. They think that teaching teachers involves not only providing knowledge but also serving as a good example in both teaching practices and behavior at training institutions as well as in the society. The study concludes that both implicit and explicit modelling should be included in the overall reflections of policy, research and practice of pedagogy of teacher education and especially in the professional development of teacher educators. Further empirical studies are recommended about the implications of implicit modelling on student teachers’ learning outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.371
Teacher spread0.297 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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