An Exploration of the Relationships Among Task Repetition, Formulaic Language, and Perceived Fluency in Chinese L1 EAP Students' Oral Presentations
Bibliographic record
Abstract
This study explored the impact of the task repetition (TR) -"repetitions of the same or slightly altered tasks" (Bygate & Samuda, 2005, p. 43) -on the development of formulaic language (FL) -prefabricated sequences that are "stored and retrieved whole from memory" (Wray, 2002, p. 9) -and perceived L2 speech fluency gains.While there is a consensus that FL facilitates fluent speech (Wood, 2015) and that TR can lead to L2 fluency development (Bygate, 1996), it is not clear whether TR alone can yield gains in the FL use and fluency.This is especially the case with investigations in the EAP context that tend to focus on the development of writing and grammar abilities, not on speaking skills (Barnard & Scampton, 2008).To investigate the effectiveness of TR in the development of FL use and perceived fluency gain, two versions of oral presentations delivered by 10 Chinese L1 EAP students were studied for instances of FL use and rated for fluency.Students repeated the task twice, four weeks apart.While the FL analysis was done using the Wray and Namba's (2003) checklist, perceived fluency judgments (e.g., speech rate, comprehensibility, pauses and hesitations, language proficiency) were provided by three independent raters who assessed a representative portion of the presentations.The results indicate that TR led to gains in fluency and promoted an increased use of and variety in FL.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".