Helping learners develop autonomy in acquiring multiword expressions
Bibliographic record
Abstract
Abstract Second language (L2) learners stand to gain substantially from mastering a wide range of multiword expressions (MWEs), and several studies have examined the benefits of language courses that regularly draw learners’ attention to MWEs. However, most of these studies focused on the learners’ retention of the MWEs included in the course materials and did not examine a potential broader and longer term effect. In the present study, upper‐intermediate students of English (N = 54) attended extracurricular classes over the course of 11 weeks (40 minutes per week) in which they either extracted MWEs from texts or engaged only in content‐related activities. Outside the context of the experiment, the students occasionally wrote essays as part of their regular L2 curriculum. One of these essays was collected before the intervention, another shortly afterward, and a third 5 months later. Three coders independently identified MWEs in these essays. Both postcourse essays written by the students who had focused on MWEs were found to be richer in MWEs than those written by the comparison group. The difference was only in part due to a greater use of items encountered in the course texts, suggesting a broader and longer term effect on the students’ autonomous acquisition of MWEs.
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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 | 0.008 |
| 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 | 0.001 |
| 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".