The DARE pedagogical model: innovating language teaching in the Global South through plurilingual, decolonial, and digital pedagogy
Bibliographic record
Abstract
Despite the need for inclusive multi/plurilingual and decolonial approaches in language education, an integrated pedagogical approach remains underexplored. Moreover, investigating the implementation of such pedagogy in digital environments is needed. Following a concurrent transformative mixed methods design, this collaborative intervention study explored an integrative approach in language teaching: plurilingual, decolonial, and digital (PluriDigit). The study was conducted with nine teachers in Brazil, who co-designed tasks in English, French, Spanish and Arabic and taught them online. The research questions were twofold: (1) To what extent do teachers’ identities inform the implementation of PluriDigit?, and (2) What are teachers’ understandings of affordances of the PluriDigit approach? Data was collected through demographic questionnaires, the Plurilingual and Pluricultural Competence scale, the Plurilingual and Pluricultural Identity questionnaire, and semi-structured interviews. Findings from descriptive statistical and inductive content analyses reveal that while teachers claimed plurilingual identities, integrating plurilingual and decolonial pedagogies, especially in a digital environment, was a challenge. Despite this challenge, teachers’ participation in the study resulted in DARE – Decolonial, Agentive, Relational and Empowering –, a pedagogical model that fosters the implementation of an integrated plurilingual and decolonial approach, which is particularly relevant in Global South contexts.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".