HOW DO DIFFERENT FORMS OF GLOSSING CONTRIBUTE TO L2 VOCABULARY LEARNING FROM READING?
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 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it