Effects of feedback timing on second language vocabulary learning: Does delaying feedback increase learning?
Bibliographic record
Abstract
Feedback, or information given to learners regarding their performance, is found to facilitate second language (L2) learning. Research also suggests that the timing of feedback (whether it is provided immediately or after a delay) may affect learning. The purpose of the present study was to identify the optimal feedback timing for L2 vocabulary learning. This study differs from previous feedback timing studies in two important respects. First, unlike some previous studies, feedback timing was not confounded with lag to test (interval between the last encounter with a given item and the posttest). Second, in order to test the view that delayed feedback may be particularly effective when learners make few errors during learning, the present study manipulated the frequency of practice to influence learning phase performance. In this study, 98 Japanese college students studied 16 English–Japanese word pairs. Immediate feedback was given immediately after each response, whereas delayed feedback was withheld until all target items were practised. Learning was measured by posttests administered immediately, 1 week, and 4 weeks after the treatment. Results suggested that when lag to test is controlled, feedback timing may have little effect on L2 vocabulary learning regardless of the frequency of errors during learning.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".