Language Instructors’ Perceptions on the Utilization of English as an International Language in English-Speaking Instruction
Bibliographic record
Abstract
This study examined the attitudes of English as an International Language (EIL) instructors in Saudi Arabia and its impact on their beliefs. A blend of qualitative and quantitative approaches was used. The collection of quantitative data was done through a Likert scale questionnaire, while the collection of qualitative data was done through semi-structured interviews. For the quantitative data, participants responded to around 50 % of the questions, viewed a short film, and then responded to the remaining questions. The short film introduced the participants to the notion of EIL. The sample comprised 66 university-level English as a Foreign Language (EFL) teacher teaching in a Saudi university. Then, to get a deeper comprehension of the responses, four participants, consisting of two male and two female EFL instructors, were interviewed. The quantitative data were analyzed by using SPSS. The qualitative data were analyzed manually. Findings from this study indicated that most participants had positive views about EIL, with 74.2% agreeing they received enough English instruction for EIL training and 68.3% considering EIL in classroom activities. In addition, academics appeared receptive to EIL speaking training, arguing that English is a communicative language regardless of the accent. The study suggests that exposure to EIL-speaking instruction positively affects teaching practices and increases students' confidence. The findings suggest that instructors should be introduced to the EIL concept to enhance pedagogy, encouraging students to focus on developing their speaking skills in general.
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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 | 0.003 | 0.008 |
| 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".