Formats and implementations of exercises for collocation learning: Learning outcomes and students’ beliefs
Bibliographic record
Abstract
Abstract Collocation‐focused exercises in language courses often require students to choose the right words from a given set of candidates to reassemble broken‐up collocations. Other exercises invite the students themselves to supply the missing words to complete collocations. One would expect exercises to serve the purpose of retrieval practice after students are first exposed to the collocations, but textbook analyses have revealed that they are also used in a spirit of trial and error. Collocation exercises thus show variation in format and in implementation, the combinations of which may yield different learning outcomes. The present study applied a 2 × 2 counterbalanced within‐participant design, where 56 ESL learners (international students enrolled in a TESOL program) tackled multiple‐choice or gap‐fill exercises on 32 verb–noun collocations (e.g., “catch fire”), half of which they had first been given a chance to study. This initial exercise was followed in the same session by a practice test to remind the participants of the correct collocations. One week later, they sat a delayed posttest. The gap‐fill format implemented as retrieval practice produced the best learning outcomes whereas the multiple‐choice format implemented in a trial‐and‐error fashion produced the poorest, despite feedback on both the initial exercise and the practice test. Despite its poorer effectiveness, one third of the participants expressed a preference for the multiple‐choice format in retrospective interviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".