Digital plurilingual pedagogies in foreign language classes: empowering language learners to speak in the target language
Bibliographic record
Abstract
While studies have shown benefits of plurilingual pedagogies on students’ experiences learning languages, more research is needed to examine how these pedagogies can be enacted in foreign language programmes in digital environments. Moreover, prioritising oral engagement has been an urgent need among teachers who use synchronous platforms such as Zoom to teach languages. This article reports on a multiple case study with three teachers – English, Spanish, and French – and 17 students in Brazil. Five plurilingual strategies were implemented in their language courses: cross-linguistic comparisons, cross-cultural comparisons, translanguaging, translation for mediation, and pluriliteracies. Inductive analysis of weekly classroom observations (N = 15) and deductive analysis of individual teacher interviews were conducted to find similarities across the three language courses. Results show that digital plurilingual pedagogy mobilised students’ entire repertoire (not L1 only), encouraged them to speak in the target language, awakened nonlinguistic semiotic resources, and enhanced plurilingual and pluricultural awareness beyond geographical boundaries. Given its multimodal nature, digital plurilingual pedagogy can facilitate oral engagement differently compared to face-to-face instruction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".