Translanguaging as an ESL Learning strategy: A case study in Kuwait
Bibliographic record
Abstract
In the domain of teaching bilingual students, the issue of using the first language in a second-language based class has been widely controversial. While some studies have questioned the method of moving between the two languages—Translanguaging, others found it highly beneficial. Here we aimed to investigate the effect of Translanguaging on the learner’s performance and language learning. 34 consenting female students of English participated in oral and written exercises pre-and-post the use of Translanguaging. A short questionnaire was answered afterwards to elicit the participants’ perception on the use of Translanguaging as part of their classwork. Even though students did not believe that their ability to alternate between the two languages has placed them in a significantly enhanced comfort zone, their higher grades post-Translanguaging indicate Translanguaging enhanced their understanding and enabled them to achieve higher levels of knowledge processing. Nevertheless, the participants’ language was not significantly affected by the process. Overall, we can conclude that Translanguaging in a bilingual classroom is effective in fully understanding the topic and the information provided, yet it does not help improve language proficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".