Translanguaging as an Instructional Strategy in Adult ESL Classroom
Bibliographic record
Abstract
The use of the first language (L1) in adult second (L2/SL) or foreign language (FL) classrooms has always been a bone of contention over the past few decades. Many are in favor of L1 use terming it as constructive and facilitating for language learning while some disapprove that practice and identify it as a hindrance to the teaching and learning of a language. Of late, the concept of translanguaging has added a new dimension to this long-standing debate of using L1 in teaching/learning L2 since it basically insists on viewing languages as a single unitary system as opposed to the traditional linguistic perception of L1 versus L2. However, there have only been a very few studies on translanguaging with particular emphasis and attention given to ESL/EFL adults at the college/university level. This research study thus attempts to shed light on the theoretical underpinnings of this L1-L2 dichotomy and discuss how translanguaging differs from the customary notion of using L1 in the adult L2 classroom. This study uses a qualitative research method that exclusively uses the relevant secondary references/works available on the topic. The results demonstrated that both translanguaging and the notion of L1use in the L2 classroom are pedagogically similar as both allow the use of L1 in L2 classrooms at varying degrees though theoretically, they are different.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".