Self-Perceptions of Non-Native Students in an Undergraduate TESOL Program
Bibliographic record
Abstract
While debate continues as to the efficacy of Native English-speaking teachers (NESTs) and non-native English-speaking teachers (NNESTs), little research has been conducted to analyze how these teachers impact communicative competence in an EFL context. Research on NNESTs’ self-perceptions has been done in many different contexts (for examples, America, Europe, East Asia), but rarely in Thailand. This paper reports on a mixed data collection study which examines the self-perceptions of undergraduate TESOL students in a Thai university. Data were collected through an online questionnaire responded to by 39 undergraduate TESOL students studying in a B.Ed. English program. Seven of these students were selected for semi-structured individual interviews. Findings show that the undergraduate TESOL students were aware of differences between NESTs and NNESTs and perceived both NESTs and NNESTs to have unique linguistic, cultural, and teaching strengths and weaknesses. The finding also shows that the undergraduate TESOL students had positive perceptions towards their non-native status and perceived themselves to be qualified and successful English teachers after graduation. This paper has implications for language teaching expertise and suggestions for developing TESOL degree curriculum and teacher preparation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".