Facing failure: unveiling challenges in implementing plurilingual pedagogy through teacher-researcher collaboration
Bibliographic record
Abstract
Language instruction continues to be shaped by a dominant monolingual ideology (Cummins, Citation2007), despite increasing academic advocacy for plurilingual pedagogy (e.g. Galante et al., Citation2022). Bridging this gap remains a significant challenge. This article reports on a teacher-researcher initiative that involved two online workshops designed to introduce plurilingualism and form-focused instruction to five German and three French language teachers in higher education. The workshops focused on theoretical concepts and the adoption of a plurilingual teaching model, supported by material adaptation facilitated by the researchers. Data from workshop interactions between teacher participants and researchers reveal persistent challenges in aligning theoretical objectives with practical implementation. The teachers demonstrated varied understandings of key concepts such as metalinguistic reflection and crosslinguistic comparison, often defaulting to existing pedagogical beliefs. Additionally, the researchers’ facilitation approaches, including dominating interaction and demonstrating limited responsiveness, constrained engagement. This article examines these challenges and proposes strategies to better support the co-construction of knowledge in future professional development initiatives.
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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 | 0.249 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.008 | 0.046 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".