Comparing the effectiveness of verb-focused and particle-focused exercise formats on the recall and recognition of phrasal verbs
Bibliographic record
Abstract
Phrasal verbs are important for successful communication and yet are incredibly challenging for language learners. The current study compared two exercise formats for the learning of phrasal verbs. One format draws attention to the verb, while the other brings into focus the particle. In the verb-focused format, students were asked to guess the missing verb before receiving feedback. In the particle-focused format, they were told to guess the missing particle before feedback was presented. The results of a cued-recall test showed that the recall of phrasal verbs was enhanced more effectively in the particle-focused format than in the verb-focused format, although this advantage diminished after one week. A multiple-choice test revealed no significant difference between the two methods in terms of their impact on the recognition of phrasal verbs. The current study also aimed to test the prediction of the episodic recollection hypothesis, which specifies that memory of the initial guess plays a critical role in the subsequent recall of the correct answer. It was also found that asking students to recall their initial guess moderated their performance in the posttest. Overall, the findings of the current study suggest that the particle-focused format boosts the memory of phrasal verbs and that to minimize the adverse effects of proactive interference, it is vital for students to remember their errors. This means that teachers would be advised to focus on exercises that provide the verb and encourage guessing of the particle.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".