The Effect of Working Memory on EFL Learners’ Oral Fluency
Bibliographic record
Abstract
Speaking is the primary objective of most L2 instructional programs and stands as one of the major factors in the evaluation of L2 learners’ competence. The present study sought to investigate how preparation and task complexity can affect L2 learners’ oral fluency in speech production with respect to individual differences in working memory capacity. The participants of the study consisted of 61 advanced L2 learners. The data collection consisted of two phases: a working memory test (reading span test) and a picture description task. Speaking was elicited through speech generation task in which the individuals were asked to discuss four topics emerging in two pairs. Also, each topic was accompanied by several relevant pictures. L2 fluency was assessed based on task complexity and preparation. The data were then analyzed in terms of the number of syllables, the number of silent pauses, and the mean length of pauses produced per minute. The statistical findings revealed that working memory, as a cognitive factor, played a significant role in L2 oral fluency accounting for variation in L2 performance. In addition, the variables Complexity and Preparation turned out to have a significant effect on the span groups’ L2 oral performance. The study offers implications on strategies to improve learners’ both fluency and working memory.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".