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Record W4403293638 · doi:10.31464/jlere.1510804

Professional Development to Support French as a Second Language Teachers: Preparation to Advance Students’ Reading Skills

2024· article· en· W4403293638 on OpenAlexaffabout
Callie Mady, Stephanie Underwood

Bibliographic record

VenueDil Eğitimi ve Araştırmaları Dergisi · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsNipissing University
Fundersnot available
KeywordsReading (process)Professional developmentSituatedCurriculumPedagogyMathematics educationFocus (optics)PsychologyLanguage developmentFaculty developmentComputer scienceLinguisticsDevelopmental psychology

Abstract

fetched live from OpenAlex

This study examines the impact of four full-day professional development sessions on French as a second language teachers’ confidence to use strategies to support their students’ reading development. Situated in Ontario, Canada where the science of reading has become a topic of focus influencing the development of new English language curriculum, the French as a second language teachers in this study requested such a focus for their professional development. The teachers completed a pre- and post-questionnaire that included a focus on the science of reading and transfer between languages. They also participated in post-professional development semi-structured interviews. The results showed that teachers gained confidence in supporting their students’ reading development and in particular using science of reading approaches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.370
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes2
Has abstractyes

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