Evaluating the Application of a Gap‐Fill Exercise on the Learning of Phrasal Verbs: Do Errors Help or Hinder Learning?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it