Effects of Audiovisual, Audio, and Visual Presentations on EFL Learners’ Writing Skill
Bibliographic record
Abstract
This study was designed to find whether three different presentations, i.e. audiovisual, visual and audio, affect EFL learners’ writing ability. First, before doing the main research, the researcher piloted her study. Then, 45 students, both males and females, aged from 23 to 38, were selected randomly out of advanced level EFL learners at an English Institute in Shiraz, Iran and they were also divided into three groups of 15. Three documentaries, i.e. stress, superstition and nature tech, were selected (www.YouTube.com) as audiovisual materials. The texts of the very documentaries were used as the visual or reading materials and the listening forms of the same documentaries were applied as the audio materials. The participants were asked to write about the topics once before each mode of presentation and after. The writings were scored out of nine based on IELTS writing criteria by two raters. Inter-rater reliability was calculated between each set of scores. One-way ANOVA, matched t-test and the effect size were used. The results revealed that the audiovisual group performed better than the audio group and the audio group performed better than the visual group in their post-writings.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".