Bibliographic record
Abstract
This qualitative study explored English as a foreign language (EFL) teachers’ perspectives on first language (L1) use in the second language (L2) classroom. The study focused on Jordanian public secondary school EFL teachers and drew on Macaro’s (2001) three codeswitching positions—optimal (i.e., L1 use can enhance L2 learning), maximal (i.e., L1 use should be minimized in L2 learning), and virtual (i.e., L1 should never be used in L2 learning). Data were collected through a classroom observation and two rounds of interviews, one pre- and one post-observation. The findings suggested that teachers’ views on L1 use varied depending on two main factors: 1) students’ L2 proficiency, and 2) type of lesson. In terms of Macaro’s (2001) framework, teachers held an optimal view toward L1 use with low-proficiency students, yet a maximal view with higher-proficiency students. Similarly, teachers held an optimal position toward L1 use in grammar classes, yet a maximal position in reading classes and a virtual position in listening and speaking classes. The findings of this study are unlike Macaro’s (2001) results, which found that teachers hold a static position toward L1 use regardless of the proficiency of learners or lesson type. Finally, the present study found that teachers were aware of L1 overuse ramifications. The findings of this research may help L2 scholars, policy makers, and teacher-practitioners to understand the role of the L1 in the L2 classroom, particularly in the context of Jordan and similar EFL contexts.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".