USING A TRANSLANGUAGING LENS TO IMPROVE LITERACY INTERVENTION FOR MULTILINGUAL LEARNERS IN FRENCH IMMERSION
Bibliographic record
Abstract
The purpose of this action research study was to provide insights for more equitable outcomes for multilingual learners by improving literacy and language intervention teaching practices. Literacy is a basic human right (United Nations, 2024) and in Canada that right to literacy extends to both our official languages in English and French. Unfortunately for multilingual learners, literacy support is often delayed until adequate language is developed (Gandara & Santibanez, 2016; Helman et al., 2016). Once literacy support is provided, it is unsuited for literacy and language development (Mady & Masson, 2018). Multilingual learners do not receive in-time literacy intervention that supports language development (Arnett & Mady, 2017; Cummins, 2000; Gibbons, 2015). This study aimed to provide insights for more equitable outcomes for multilingual learners by improving teacher practices in the delivery of literacy and language interventions in French immersion. This study examined how teacher practices were influenced when the English Language Development (ELD) framework components (Morita-Mullaney et al., 2023) were used as a lens within the Reading Inspires Students to Excel (RISE) literacy intervention (Richardson & Lewis, 2018a) with multilingual learners in French immersion. The action research process was explored as a method of professional development for the reading interventionist teachers who were involved in all aspects of the study. The ELD framework included the following components immersive writing, oral language, and vocabulary in use. Together, the participants and the researcher agreed to explore ways to enhance oral language. Data were gathered through two group interviews, electronic teaching reflective journals, individual interviews and researcher field notes obtained through observations. The findings highlighted how interconnected the ELD components were and when used as a translanguaging lens within the RISE intervention along with the action research process it improved teacher practices for multilingual learners in French immersion. The process emphasized modeling, pre-teaching vocabulary, and the importance of ongoing reflection to promote more student-centered teaching and learning. The study also underscored the benefits of incorporating teaching strategies such as sentence stems, visuals, and partner talks to introduce new vocabulary and support student’s literacy progression from oracy to writing.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".