Decoding the Myths of the Native and Non-Native English Speakers Teachers (NESTs & NNESTs) on Saudi EFL Tertiary Students
Bibliographic record
Abstract
Many people believe the myth that being taught by a native speaker is the best way to learn a language. This belief has influenced many Saudi schools, language institutes, and universities to include the nativeness factor as part of a language instructor’s job requirements. Using an open ended questionnaire, this study aims to investigate the impact of native English speaking teachers (NESTs) and non-native English speaking teachers (NNESTs) on EFL university Saudi students. It also explores how the teachers’ background and accents influence the students’ achievement in terms of the development of their language skills. The participants are students who are in their preparatory year program at King Abdulaziz University in Jeddah taught by NEST and NNEST. The findings of the study indicate that teachers’ nativeness and backgrounds have no significant effects on the EFL Saudi students’ learning processes. However, a few factors have been detected that play roles in supporting EFL learning, which can be summarized as follows: 1) Teachers’ competence and experience are what make the teachers qualified, regardless of their nationalities. 2) Teachers sharing the students’ L1 play positive roles in the EFL learning process. 3) The teacher’s accent has an effect on students, which might hinder the learning process in the case of an unfamiliar accent. 4) The teacher’s personality is more involved in the classroom communications and interactions than is the teacher’s nativeness. Based on the findings of this study, implications are made on the topic of the effect of NEST and NNEST on EFL learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".