Efficacy of Multimodal Glossing on Second Language Vocabulary Learning: A Meta‐analysis
Bibliographic record
Abstract
This meta‐analysis examined the effectiveness of an additional gloss mode in single versus dual and dual versus triple glossing on second language (L2) learners’ word learning. In total, 22 studies, providing 26 independent effect sizes, were coded, and 11 moderator variables including quality of data sample, learner variables, gloss features, text features, and methodological features were examined. The results show that the overall effect of an additional gloss mode was medium (g = 0.46) for immediate posttests and small (g = 0.28) for delayed posttests. However, analyses of moderator variables indicated that the effect of additional gloss modes is influenced by a range of variables related to learner (e.g., proficiency), gloss (e.g., language), text (e.g., narrative vs. expository), and research design (e.g., test format). Importantly, adding an additional mode to single textual gloss enhances vocabulary learning, whereas adding a mode to dual glossing does not result in significantly better vocabulary learning. The findings suggest that using more than two gloss modes is not necessary because it does not always lead to better learning of new words.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".