The Effect of Gloss Type and Mode on Iranian EFL Learners’ Reading Comprehension
Bibliographic record
Abstract
This study investigated the effects of three kinds of gloss conditions that is traditional non-CALL marginal gloss, computer-based audio gloss, and computer-based extended audio gloss, on reading comprehension of Iranian EFL learners. To this end, three experimental and one control groups, each comprising 15 participants, took part in this study. In order to ensure that the participants were from the right proficiency level, KET (Key Language Test) was used to select upper-intermediate proficiency learners. Participants in each group read two passages under one of the three mentioned conditions, with no gloss offered for control group. They all completed one pretest, one reading session, and one post-test. The data were analyzed using t-tests and one-way ANOVA. Statistical analyses of the results revealed that extended audio gloss group, who were provided with the voice of a speaker to read the meaning of the target word, as well as one example sentence, significantly outperformed the other groups. The results of this study provide some insights for teachers and administrators to review their curricula, approaches, and educational tools, and to consider the possibility of incorporating CALL technology into their teaching.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".