Translanguaging as an agentive pedagogy for multilingual learners: affordances and constraints
Bibliographic record
Abstract
Translanguaging offers a new perspective on language learning by affirming and leveraging the diverse language practices that make up learners’ unitary language repertoire as resources for their learning. Despite the potential pedagogical benefits of translanguaging, English-only policies are still prevalent in many language classrooms. Even when translanguaging is welcomed into the classroom, the conflicting attitudes of teachers, students and families pose ideological constraints on translanguaging which restrict learners from selecting and utilising features from their whole translanguaging repertoire. Guided by translanguaging and sociocultural theory, this study examines the tension between the affordances of student-led translanguaging in a Grade 5 Malaysian classroom with an English-only policy, and the constraints to learners’ use of translanguaging. This paper reports on the results of a sociocultural critical discourse analysis of learners’ peer interactions while engaged in collaborative learning, and interviews with 31 learners. The findings indicate that learners used translanguaging agentively to support one another’s language learning, build rapport, resolve conflict, assert their cultural identity, and draw on knowledge across languages. However, learners’ use of translanguaging was constrained to an extent by their teacher’s and peers’ language policies and practices, parental discourses about linguistic capital, and societal discourses on ethnicity, nationality, and marginalisation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".