Hesitation Strategies in an Oral L2 Test among Iranian Students Shifted from EFL Context to EIL
Bibliographic record
Abstract
English as an international language emphasizes on learning different major dialect forms; in particular, it aims to equip students with the linguistic tools to communicate internationally. English is no longer merely used by native speakers but by all those who come to use it.The study reported in this paper was conducted in the population of Iranian students in the academic context of Malaysia who have learned English as a foreign language in their home country, but after immigrating to the multi lingual country of Malaysia have to use it as an International language to communicate not only with the academician but also with the common people. This shift of English language application had led them to a confusion, which reveals in in their performance, although they are not quite aware of the involving reasons.Therefore, this study examined this mismatch between EFL and EIL oral performance from the angle of hesitation, and investigated the hesitation strategies Iranian university students use while they are speaking English. It focused on the frequency and distribution of pauses, pauses and fillers, and fillers in the speech of 12 Persian speakers of English, students in a public university in Kuala Lumpur, Malaysia, participating in an oral test consisting of three parts to study whether the type of questions affect the hesitation strategies they employ or not. The data collected was collected and analyzed qualitatively and quantitatively, and the results indicated that Persian speakers of English follow different pausing conventions which varied by the change in the context of the questions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".