The Effect of Video- and Audio-Assisted Reading on Saudi EFL Learners’ Reading Fluency and Comprehension
Bibliographic record
Abstract
To be successful, students must have excellent reading abilities, as reading is the most important skill for achieving high levels of learning and personal development. This study aims to investigate the effectiveness of using audio-visual aids to develop reading fluency and comprehension among English language students of the University of Jeddah. This quantitative research employed a quasi-experimental method. Data were collected from a reading comprehension test and a reading fluency test, measuring students’ word count per minute. Participants were divided into four groups, two each (a control group and experimental group) of men and women students. The men and women experimental groups comprised 26 students each, while the control groups comprised 24 and 28 students, respectively. The experimental groups watched videos to activate their thinking before reading the passage, and then read comprehension passages. Contrarily, the control groups were taught conventionally. Two achievement tests were used as pre-test and post-test to determine the improvement in the experimental groups. Independent- and paired samples t-tests were performed to compare the results of the two groups, which showed that the experimental groups outperformed the control groups in reading comprehension and reading fluency (p < .05). This study also investigated the differences between men and women regarding reading fluency and reading comprehension; the results did not show any differences between them (p > 0.05). These positive results shows that video and audio are effective for students in developing and supporting their reading fluency and comprehension. Therefore, it could be integrated and combined with traditional textbooks as supporting material to enhance reading skills among English language 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 | 0.001 | 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".