Evaluating the Application of a Gap‐Fill Exercise on the Learning of Phrasal Verbs: Do Errors Help or Hinder Learning?
Bibliographic record
Abstract
Abstract In recent years, there has been considerable interest in how to maximize learners' retention of multiword expressions. One technique that has been shown to be highly effective is the use of exercises such as those found in mainstream English as a second language textbooks. In the present study, we investigated how the execution of a gap‐fill exercise impacts the learning of phrasal verbs with 118 learners studying English as a foreign language. Participants completed a gap‐fill exercise by referring to the answer key, or they received the answer key only after completing the exercise. The effects of the learning conditions were assessed with tests for measuring productive and receptive knowledge at two retention intervals. The results from mixed‐effects logistic regression modeling showed that both executions of the gap‐fill exercise led to similar rates of retention. The findings largely challenge previous research. We also explored how to minimize proactive interference when participants make errors in gap‐fill exercises by asking them to recollect their initial guesses during the posttests. The results showed that when the initial guess was produced, correct recall of the target phrasal verbs was much greater than when the guess was not recollected. The finding indicates that memory for the initial guess may play a vital role in how participants learn from their errors. The pedagogical implications of the findings are discussed, and future areas of research are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".