How effective is second language incidental vocabulary learning? A meta-analysis
Bibliographic record
Abstract
Abstract There is a great deal of variation in gains found between studies of second language (L2) incidental vocabulary learning, as well as many factors that affect learning. This meta-analysis investigated the effects of exposure to L2 meaning-focused input on incidental vocabulary learning with an aim to clarify the proportional gains that occur through meaning-focused learning. Twenty-four primary studies were retrieved providing 29 different effect sizes and a total sample size of 2,771 participants (1,517 in experimental groups vs. 1,254 in control groups). Results showed large overall effects for incidental vocabulary learning on first and follow-up posttests. Mean proportions of target words learned ranged from 9–18% on immediate posttests, and 6–17% on delayed posttests. Incidental L2 vocabulary learning gains were similar across reading (17%, 15%), listening (15%, 13%), and reading while listening (13%, 17%) conditions on immediate and delayed posttest. In contrast, the proportion of words learned in viewing conditions on immediate posttests was smaller (7%, 5%). Findings also revealed that the amount of incidental learning varies according to a range of moderator variables including learner characteristics (L2 proficiency, institutional levels), materials (text type and audience), learning activities (spacing, mode of input), and methodological features (approaches to controlling prior word knowledge).
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".