Bibliographic record
Abstract
This study aims to identify functions (Note 1) of teachers’ first language (L1) use in English as a foreign language (EFL) classes in public schools in Jordan based on students’ observations. The questionnaire items were selected based on the most common uses of L1 in second language (L2) classrooms as identified in the literature. The questionnaire was designed to elicit students’ observations to identify functions of EFL teachers’ L1 in English classes. The participants were 104 EFL students in Grades 10 and 11 in four Jordanian public schools. Participants’ responses to the questionnaire were analysed quantitatively using SPSS, a statistical software package. The study found that the students observed that their teachers shared their L1 with them in English classes to: 1) Explain complex grammar points, 2) Define some new vocabulary items, 3) Explain difficult concepts or ideas, 4) Give instructions, 5) Praise the students, 6) Translate the reading texts, and 7) Explain the similarities and differences between Arabic and English in terms of grammar, structure or pronunciation. However, the students did not observe that their teachers used the L1 in order to maintain discipline in the class as previous studies have found. The findings suggest that teachers’ L1 use in the L2 classroom may indicate the usefulness of this practice and call to license EFL teachers to use their L1 in English classes in public school in Jordan and other 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".