Perceptions of Non-native EFL Teachers’ on L1 Use in L2 Classrooms: Implications for Language Program Development
Bibliographic record
Abstract
The study of L1 (first language) use in L2 (second language) classrooms has long received attention in the literature. Despite the considerable amount of research that has been conducted on the phenomenon, the focus has often been on the advantages and disadvantages. Considerably, less research has been conducted regarding the non-native L2 teachers’ perceptions of when L1 use is required. More importantly, there has been little research on the limitations faced by non-native EFL (English as a Foreign Language) teachers because of the strong English-only policies they have to follow. The present study explored the perceptions of non-native EFL teachers’ towards the existing English-only policies in their institutions. The teachers’ perceptions of when L1 should or should not be used in L2 classrooms were also of interest. Fifty-four non-native teachers of EFL from English preparatory schools of four universities in Northern Cyprus participated in the study. Data were collected through semi-structured interviews. Analysis of the data indicated that all of the participating teachers were inclined to use L1 in their L2 classrooms for a variety of reasons. Further, it is found that teachers were affected negatively and were restricted in certain issues as a result of having to follow strict English-only policies at their institutions. Implications for program development are discussed here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".