A Multiple‐Choice Exercise on Collocations: What Do Learners Actually Remember?
Bibliographic record
Abstract
ABSTRACT Contemporary materials for second language (L2) learning feature exercises on collocations (i.e., word partnerships such as catch fire ), many of which require learners to select correct word combinations from two or more candidates. A few studies of the effectiveness of these selected‐response exercises, which are essentially multiple‐choice exercises, have found that learners later reproduce wrong collocations that they were exposed to in the exercise. However, it is unclear if this is a side effect of the exercises or if the re‐emergence of wrong candidate responses is just accidental. The present study examines if wrong candidate responses that learners see in collocation exercises interfere with learners’ recall of the correct responses by having learners of L2 English tackle multiple‐choice items on verb‐noun collocations and verbally report two weeks later in a post‐test what they remember about them. The verbal reports revealed that the learners recalled reading and responding to most of the exercise items, but for only one‐ third of them did they also recall which candidate response had turned out to be correct according to the feedback they received. Furthermore, for close to one‐ fifth of collocations that learners said they already knew at the start of the exercise, these participants mistook a wrong candidate response for the correct one when they revisited the exercise two weeks later. The findings call for a cautious approach to the implementation of selected‐response exercises for collocation learning.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".