The Impact of the Rereading Method on Reading Speed and Comprehension of Normal and Scrambled Texts among Arabic EFL Learners
Bibliographic record
Abstract
The rereading method benefits automatic word recognition, which impacts the development of L2 learners' reading speed and text comprehension. This study aimed to compare the effect of the rereading method on reading speed and reading comprehension of normal and scrambled texts in Arabic EFL learners. It was developed through quantitative research of analytical design to analyze the behavior of reading speed and text comprehension. The study focused on 50 Arab first-year EFL learners, divided into two groups, low and high level, according to their reading ability. They were given to read scrambled (S) and normal (N) passages with the conditions NN, SS, SN, and NS. The results show that when students are confronted with reading of the second passage preceded by normal texts, they read faster, presenting the same behavior for the general sample as for the low- and high-level groups. On the other hand, the rereading method favors reading speed when the second passage is scrambled. High-level students were shown to read faster, regardless of the condition of the passage. Regarding text comprehension, when the second passages are scrambled (SS, NS), students present better text comprehension. These results suggest that despite the difficulty of the text, a transfer from the first passage to the second readings may occur. The results indicate that the rereading method improves text comprehension and reading speed.
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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 | 0.006 |
| 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) | 0.001 | 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".