On Translanguaging and Learner Affect: An Action Research Study on ESL Secondary Classrooms Through the Use of a Multilingual Presentation Project
Bibliographic record
Abstract
Amid controversies surrounding multilingual policies in a classroom, something that has become a key component of education in recent years is student’s emotional well-being and how emotions influence learning (Song, Howard, Olazabal-Arias, 2022). This action-research study examines the impact of translanguaging on student engagement and affect in a secondary-level English as a Second Language (ESL) classroom in Quebec. While traditional ESL instruction emphasizes English-only policies, translanguaging encourages students to use their full linguistic repertoire, supporting comprehension and participation. Conducted in a French-medium high school, this study involved multilingual presentations where students used their first languages (L1s) alongside English. Teacher-researcher observations through field notes were analyzed to assess student collaboration, participation, and emotional responses. The findings of this research suggest that the use of translanguaging can foster engagement, reduce presentation anxiety and promote inclusivity. This research contributes to multilingual education by highlighting translanguaging as a strategy to enhance student confidence and learning in diverse, multilingual classrooms. Keywords: Translanguaging, ESL, affect, multilingual education, student engagement, collaboration, emotions
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".