The impact of guessing and retrieval strategies for learning phrasal verbs
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Previous research on phrasal verbs has focused on the effectiveness of exercises requiring learners to provide the missing particle for a given verb. However, this research does not address other common exercise formats, such as those requiring learners to complete entire phrasal verbs. This study aims to bridge this gap by exploring such an exercise format and its two principal implementations. The participants were 134 Japanese EFL learners. Both exercise setups present the definition and initial letter of a phrasal verb as a prompt. In the guessing method, students attempt to fill in the missing phrasal verb based solely on the prompt and then receive corrective feedback. In contrast, in the error-free retrieval method, students study the phrasal verb and its definition before attempting the same gap-fill exercise. Retention of phrasal verbs improved more with the guessing method. Further, across both methods, participants struggled more with recalling particles than verbs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it