A Study of Reading Strategy Awareness Among College English Major at Taif University, KSA; and Factors affecting Reading Comprehension
Bibliographic record
Abstract
Reading comprehension for English as Foreign Language learners is a very complex process to understand how readers sort the logic of written symbols, so it is crucial that the progression of reading comprehension and the dynamics leading to the product of this process be understood properly. There is no lack of studies on reading comprehension aiming to explain its nature and trying to show how the task of comprehension is accomplished. Research has shown how Arab students struggle with reading problems encountered with both bottom-up and top-down processes. Hence, these students are not only slow readers, inefficient and unskilled in terms of comprehension. (Al-Mahrooqi & Asante, 2012) While much research is devoted to reading as such, little is available on how Arab students challenge when doing this. The researcher will inspect the Saudi college EFL learners’ use of three reading strategies (cognitive, meta-cognitive, compensation strategies), and their control on the relationships between reading strategy use and their English reading comprehension. The present paper is an effort to deliver a base for better understanding the relationship between reading comprehension and reading strategies. The results of this paper will be implemented for better reading strategy instruction at Taif University, KSA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".