Students’ Preferences on the Use of Mother Tongue in English as a Foreign Language Classrooms: Is it the Time to Re-examine English-only Policies?
Bibliographic record
Abstract
In literature on bilingual teaching, different perspecttives exist for and against the use of first language (L1) in English as a Foreign Language (EFL) classrooms. There is a continuing matter of debate on whether L1 contributes to or precludes the learning of a second language (L2). Numerous studies have been conducted on this topic, but no clear consensus exists on whether L1 should be banned or its inclusion in EFL classrooms should be allowed. A significant body of literature has explored this phenomenon from teachers’ perspectives, and an adequate number of studies have explored the phenomenon from the students’ perspectives. However, few studies have identified the reasons for which students need such an inclusion. More importantly, no studies seem to have considered demographic variables that may directly influence students’ perceptions of the use of L1 in their (L2) classrooms, that is; educational background and their language proficiency level. This study primarily investigated whether students’ educational background and their L2 proficiency influenced their perceptions of the use of Turkish in their L2 classrooms as well as their perceptions and needs for the use of L1 in their classrooms. The study was conducted on a sample of 303 Turkish learners of EFL at English Preparatory School of European University of Lefke in Northern Cyprus. Data were collected using a questionnaire. Analysis of the data indicated that EFL students had high positive perceptions toward the inclusion of L1 in their L2 classes and that their perceptions were affected by their demographic characteristics. Students with lower level of L2 proficiency were also found to have more positive perceptions toward the use of L1. Furthermore, the particular issues where students needed L1 were also identified. Implications for language teachers and policy makers are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".