Weighing Up Exercises on Phrasal Verbs: Retrieval Versus Trial‐and‐Error Practices
Bibliographic record
Abstract
Abstract English‐as‐a‐foreign‐language (EFL) textbooks and internet resources exhibit various formats and implementations of exercises on phrasal verbs. The experimental study reported here examines whether some of these might be more effective than others. EFL learners at a university in Japan were randomly assigned to 4 treatment groups. Two groups were presented first with phrasal verbs and their meaning before they were prompted to retrieve the particles from memory. The difference between these 2 retrieval groups was that 1 group studied and then retrieved items 1 at a time, while the other group studied and retrieved them in sets. The 2 other groups received the exercises as trial‐and‐error events, where participants were prompted to guess the particles and were subsequently provided with the correct response. One group was given immediate feedback on each item, while the other group tackled sets of 14 items before receiving feedback. The effectiveness of these exercise implementations was compared through an immediate and a 1‐week delayed posttest. The best test scores were obtained when the exercises had served the purpose of retrieval, although this advantage shrank in the delayed posttest (where scores were poor regardless of treatment condition). On average 70% of the posttest errors produced by the learners who had tackled the exercises by trial‐and‐error were duplicates of incorrect responses they had supplied at the exercise stage, which indicates that corrective feedback was often ineffective.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".