Students and the English Language: Perceptions About Native, Arab Non-Native, and Non-Arab Non-Native Teachers
Bibliographic record
Abstract
In this study, the perceptions of students of native English teachers (NETs), Arab non-native English teachers (Arab NNETs) and non-Arab non-native English teachers (non-Arab NNETs) were assessed. The general perceptions of the students regarding the learning strategies, teaching skills, strengths and weaknesses of the three groups of English teachers were explored. The study had a mixed-methods approach, applying closed-ended and open-ended questions as the quantitative and qualitative methodologies. The data were collected via an online questionnaire from Saudi students who attended Taif University. It was revealed that students preferred NETs concerning some aspects, whereas they exhibited a positive attitude towards Arab NNETs about other elements. The perceptions of the students were similar in terms of their general attitudes towards the behaviours of teachers in the classroom and learning strategies. Furthermore, most students perceived NETs to be the best at teaching English skills, while one-third trusted more in Arab NNETs, especially concerning grammar skills. The minority of students preferred non-Arab NNETs. In the results of open-ended questions, the perceptions of the students regarding the strengths and weaknesses of each group of teachers were disclosed.
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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 | 0.003 |
| 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) | 0.003 | 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".