Native and Non-native English Speaking Teachers’ Teaching Styles and Their Effect on Their EFL Saudi Students’ Achievement and Enjoyment of Learning English at Taif University
Bibliographic record
Abstract
The present research is going to assess the discrepancies between native and non-native instructors working at Taif University. The subjects have been 609 Saudi men and women EFL learners presenting themselves in a great English language plan at the preparatory year Science, Humanities and Health at Taif University. Moreover, 51 teachers (20) males and (31) females who are teaching staff members of the Taif University English Language Centre (TUELC) participated during the research. The research followed a descriptive analytical method. The Conti (1990) Principles of Adult Learning Scales (PALS) was used. Learning English Enjoyment questionnaire (LEEQ) that was developed by the researcher was used, too. Primary areas of investigation were teaching styles, students’ achievement and students’ enjoyment of learning English. Collectively, results provide some strong evidence that show a positive connection between native English speaking teachers’ styles and the students’ achievement and enjoyment. The effect of instruction experience, like the periods of instructing was considered in the present research. In addition, native and nonnative instructors who speak English are regarded also various in such domains as instruction strategies in the classes, levels of teaching tactical effectiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".