The Power of Prediction: Assessing the Impact of Initial-Letter Guessing Strategies on Phrasal Verb Retention
Bibliographic record
Abstract
There is growing interest in the effects of the strategies for learning phrasal verbs found in mainstream English-as-a-foreign-language textbooks. However, the only exercise format examined involves filling in particles in gapped spaces next to a verb. The present study examined and compared two strategies for learning phrasal verbs and assessed whether one constituent word is more likely to be remembered. One hundred and thirty-four Japanese university students learning EFL were asked to study and remember 24 phrasal verbs. One group was asked to guess the phrasal verb when shown the definition along with the initial letter of the verb before it was revealed. The other group was asked to study the phrasal verb and its definition before being asked to recall it when only the definition and the initial letter of the verb were shown. The impact of the guessing and retrieval procedures was measured shortly after and one week later in a cued-recall test of productive knowledge. Results from mixed effects logistic regression modelling showed that guessing was more effective than retrieval, even when the guesses were wrong. It was also found that verbs were remembered better than particles, regardless of the learning strategy. Although guessing is a common strategy for learning phrasal verbs in mainstream English-as-a-foreign-language textbooks, research on its effectiveness is limited. The findings show that making and correcting errors is not as detrimental as previously thought.
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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 | 0.020 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".