Increasing use of multi-word expressions in conversation through a fluency workshop
Bibliographic record
Abstract
This quasi-experimental study investigated the extent to which explicit classroom intervention helped English as a Foreign Language (EFL) students in Japan to use multi-word expressions (MWEs) fluently in conversation. Over six weeks, an experimental group (n = 65) was encouraged to notice, practise, and produce 30 MWEs (e.g., I think I will, would you like to) through fluency workshop activities including shadowing, dictogloss and role-play. A control group (n = 51) followed normal linked skills classes without any planned exposure to the MWEs. Results showed increases in written production of MWEs in a cued recall test and in the use of MWEs in self-generated conversation. Both effects were significantly stronger for the experimental group than for the control group, and participants with higher vocabulary scores showed greater uptake of MWEs in the cued recall test. Individual differences in rate of MWE use did not predict fluency, as measured through speech rate, phonation time ratio and mean length of run. Teaching implications for promoting uptake of MWEs among language learners include explicit noticing and encouraging use of MWEs through a variety of classroom activities such as role-play.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".