Influence of Teacher‐Contact Time and Other Variables on ESL Students' Attitudes Towards Native‐ and Nonnative‐English‐Speaking Teachers
Bibliographic record
Abstract
Although several studies have been conducted that investigated the attitudes of English as a second language (ESL) students towards their nonnative‐English‐speaking (NNES) ESL teachers, few scholars have explored the influence of teacher‐contact time and other relevant variables on students' responses. This article reports on a study conducted in 22 intensive English programs throughout the United States, which compared students' attitudes towards both their native‐ and nonnative‐English‐speaking (NES and NNES) ESL teachers at the beginning and at the end of a given semester. This study also investigated whether variables such as students' first languages, English proficiency level, and expected grades influence their answers. Results show that students' attitudes towards both NES and NNES ESL teachers were sometimes unexpectedly positive but could also be predictably negative in some instances. Additionally, some variables such as the students' first language significantly influenced their attitudes towards both NES and NNES ESL teachers. Finally, students' attitudes towards both NES and NNES ESL teachers changed over time. These results suggest that the linguistic background of ESL teachers is only one among numerous variables influencing students' attitudes towards their teachers. Consequently, English proficiency and teaching skills should no longer be defined by the ambiguous notion ofnativeversusnonnative speakerbut, instead, should take into consideration the multilayered context in which the teaching is taking place.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".