How effective is second language incidental vocabulary learning? A meta-analysis
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 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).
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.121 | 0.002 |
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