HOW DO DIFFERENT FORMS OF GLOSSING CONTRIBUTE TO L2 VOCABULARY LEARNING FROM READING?
Bibliographic record
Abstract
Abstract This meta-analysis investigated the overall effects of glossing on L2 vocabulary learning from reading and the influence of potential moderator variables: gloss format (type, language, mode) and text and learner characteristics. A total of 359 effect sizes from 42 studies ( N = 3802) meeting the inclusion criteria were meta-analyzed. The results indicated that glossed reading led to significantly greater learning of words (45.3% and 33.4% on immediate and delayed posttests, respectively) than nonglossed reading (26.6% and 19.8%). Multiple-choice glosses were the most effective, and in-text glosses and glossaries were the least effective gloss types. L1 glosses yielded greater learning than L2 glosses. We found no interaction between language (L1, L2) and proficiency (beginner, intermediate, advanced), and no significant difference among modes of glossing (textual, pictorial, auditory). Learning gains were moderated by test formats (recall, recognition, other), comprehension of text, and proficiency.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".