Air Traffic Communication in a Second Language: Implications of Cognitive Factors for Training and Assessment
Bibliographic record
Abstract
This study investigated the effects of second language (L2) proficiency and task‐induced cognitive workload on participants' speech production and retention of information in an environment designed to simulate the demands faced by pilots receiving instructions from air‐traffic controllers. Three groups of 20 participants (one native‐English‐speaking group, two native‐Mandarin‐speaking groups of relatively high and low levels of English proficiency) played the role of pilots. Participants listened to, repeated, and responded to simulated air‐traffic controller messages (in English) under conditions of low and high workload. In the high workload condition, participants performed a concurrent arithmetic task while repeating the messages. The dependent variables were message repetition accuracy and speech production (accentedness, comprehensibility, fluency, as perceived by 10 native‐English‐speaking raters). The native English speaker group repeated messages more accurately than both L2 groups, and the low‐proficiency group repeated messages less accurately in the high workload condition than in the low workload condition. The native speaker and the low‐proficiency groups were perceived as less fluent in the high than in the low workload condition, and only the low‐proficiency group's speech was perceived as more accented in the high than in the low workload condition. Implications for language training and assessment for English for specific purposes are discussed.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".