Exploring the factors driving L1 use in Jordanian secondary EFL classrooms
Bibliographic record
Abstract
This qualitative study investigated factors contributing to the use of the first language (L1) in English as a Foreign Language (EFL) classrooms in Jordanian public secondary schools. Using convenience sampling, 25 participants were recruited: four EFL supervisors from an education directorate, 10 EFL teachers, and 11 students from Grades 11 and 12 across multiple public secondary schools. Data were collected through individual semi-structured interviews. Thematic analysis was employed, following open, axial, and selective coding to extract key themes. The findings reveal that L1 use is shaped by a constellation of interrelated factors: teacher-related (e.g. limited L2 proficiency and underuse of listening and speaking activities), student-related (e.g. low L2 proficiency and exam-oriented learning), institutional (e.g. overcrowded classrooms, lack of resources), and systemic (e.g. the high-stakes Tawjihi exam that sidelines communicative competence). A notable policy-practice gap emerged, where supervisors emphasized idealistic expectations for L2 use, while teachers and students described L1 reliance as a pragmatic response to contextual constraints. The findings underscore the need for policy reforms, targeted professional development, and assessment redesign that acknowledge on-the-ground realities and promote meaningful L2 engagement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".