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Record W4403222989 · doi:10.47862/apples.130459

Teaching materials for use in French classes for immigrants enrolled in Literacy Education and Second Language Learning for Adults in Quebec

2024· article· fr· W4403222989 on OpenAlexaffabout
Vincent Bédard, Véronique Fortier, Valérie Amireault

Bibliographic record

VenueApples - Journal of Applied Language Studies · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsImmigrationLiteracyMathematics educationPedagogyPsychologySociologyMedical educationMedicineGeographyArchaeology

Abstract

fetched live from OpenAlex

Le personnel enseignant travaillant avec les personnes adultes immigrantes en apprentissage de la langue et de la littératie (PAIALeL) est confronté à plusieurs défis concernant la disponibilité et l’utilisation de matériels didactiques. Pour dresser le portrait du matériel utilisé dans les classes de français pour PAIALeL au Québec (Canada) et mieux comprendre les différents enjeux liés à l’utilisation de ce matériel, nous avons interrogé des enseignant·e·s en utilisant un questionnaire en ligne (n=53) et des entrevues individuelles (n=7). Nos données, analysées à l’aide de la théorie de l’activité (TA) révèlent un manque de matériel adéquat ainsi que des enjeux liés à l’adaptation et à la création de matériel ainsi qu’à l’hétérogénéité des profils des PAIALeL. Des implications pédagogiques sont aussi présentées. Teachers working in the field of literacy education and second language learning for adults (LESLLA) face several challenges related to the availability and use of appropriate teaching materials. To provide an overview of the materials used in Quebec’s French L2 classes and to better understand the challenges related to these materials, we collected data from teachers both through an online questionnaire (n=53) and individual interviews (n=7). Analysed through the lens of Activity Theory (AT), our data highlight a lack of suitable materials and issues related to the heterogeneity of students’ background. Pedagogical implications are also presented.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.019
GPT teacher head0.367
Teacher spread0.348 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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