Multimedia Glosses for Enhancing EFL Students’ Vocabulary Acquisition and Retention
Bibliographic record
Abstract
The present study attempted to investigate the effect of multimedia glosses on EFL students’ vocabulary acquisition and retention. Forty-five EFL students were divided into two groups: control (n=22) and experimental (n=23). A vocabulary test was administered to both groups in order to ensure that they were equivalent. Then, all participants attended 12 weekly reading sessions where participants of the experimental group practiced reading computerized texts that included multimedia glosses while participants of the control group practiced reading the same texts but without any glosses. Immediately after the treatment was over, the vocabulary test was administered to both groups in order to evaluate the differences between the two groups in vocabulary acquisition. Moreover, the same test was administered to both groups two weeks after the administration of the posttest with the purpose of evaluating the differences between the two groups in vocabulary retention. The statistical analysis revealed a significant difference between the two groups in both the immediate and the delayed administrations of the vocabulary test. Therefore, it was concluded that multimedia glosses enhanced both vocabulary acquisition and retention among EFL students.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".