The Effect of Aural and Visual Storytelling on Vocabulary Retention of Iranian EFL Learners
Bibliographic record
Abstract
EFL learners at all ages and proficiency levels are usually confronted with various problems in vocabulary learning and retention. This study sought to introduce strategies for improvement of vocabulary learning and retention. Therefore, the effects of using aural/visual storytelling on Iranian EFL learners’ vocabulary learning and retention were investigated. To do so, 50 intermediate female EFL learners were randomly assigned to two groups. After the administration of teacher made English Vocabulary Test as the pre-test, aural storytelling method was used for the control group, and visual storytelling method was used for the experimental group. After three months of instruction, the aforementioned teacher made English Vocabulary Test, as the post-test, was given to the students of both groups to assess their improvements. Two weeks after post-test, they were given a delayed post-test to measure their retention of English vocabulary knowledge. The reliability of the English Vocabulary Test using Cronbach's Alpha was estimated equal to 0.80. Finally, Using ANCOVA, the results revealed that, the experimental group’s participants outperformed those of control group in both learning and retention of English vocabulary. So, it can be noted that the training program according to visual could have impressive impact on the learning and retention of vocabulary knowledge.
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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 | 0.004 |
| 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) | 0.002 | 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".