A Longitudinal Study of ESL Learners' Fluency and Comprehensibility Development
Bibliographic record
Abstract
This longitudinal mixed-methods study compared the oral fluency of well-educated adult immigrants from Mandarin and Slavic language backgrounds (16 per group) enrolled in introductory English as a second language (ESL) classes. Speech samples were collected over a 2-year period, together with estimates of weekly English use. We also conducted interviews at the last data collection session. The participants’ fluency and comprehensibility at three points over 22 months were judged by 33 native speakers of English. We examine the learners’ progress in light of their exposure to English outside of their ESL class. The Slavic language speakers showed a small but significant improvement in both fluency and comprehensibility, whereas the Mandarin speakers’ performance did not change over 2 years, although both groups started at the same level of oral proficiency. These differences may be attributable in part to degree of exposure to English outside the ESL courses. Neither group had extensive exposure outside of their classes because of employment and familial responsibilities (although the Slavic language speakers reported more opportunities). Thus both groups may have been disadvantaged by a lack of oral fluency instruction. The findings, both quantitative and qualitative, are interpreted using the Willingness to Communicate framework; we also discuss implications for the language classroom.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".