Teaching materials for use in French classes for immigrants enrolled in Literacy Education and Second Language Learning for Adults in Quebec
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".