The Effects of Repetition on Incidental Vocabulary Learning: A Meta‐Analysis of Correlational Studies
Bibliographic record
Abstract
This meta‐analysis aimed to clarify the complex relationship between repetition and second language (L2) incidental vocabulary learning by meta‐analyzing primary studies reporting correlation coefficients between the number of encounters and vocabulary learning. We synthesized and quantitatively analyzed 45 effect sizes from 26 studies (N = 1,918) to calculate the mean effect size of the frequency–learning relationship and to explore the extent to which 10 empirically motivated variables moderate this relationship. Results showed that there was a medium effect (r = .34) of repetition on incidental vocabulary learning. Subsequent moderator analyses revealed that variability in the size of repetition effects across studies was explained by learner variables (age, vocabulary knowledge), treatment variables (spaced learning, visual support, engagement, range in number of encounters), and methodological differences (nonword use, forewarning of an upcoming comprehension test, vocabulary test format). Based on the findings, we suggest future directions for L2 incidental vocabulary learning research. Open Practices This article has been awarded an Open Data badge. All data are publicly accessible via the Open Science Framework at https://osf.io/rmnk2 . Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki .
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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 | 0.040 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".