Metacognitive Reading Strategies, Motivation, and Reading Comprehension Performance of Saudi EFL Students
Bibliographic record
Abstract
Metacognitive reading strategies and reading motivation play a significant role in enhancing reading comprehension. In an attempt to prove the foregoing claim in a context where there is no strong culture for reading, this study tries to find out if there is indeed a relationship between and among metacognitive reading strategies, reading motivation, and reading comprehension performance. Prior to finding out relationships, the study tried to ascertain the level of awareness and use of metacognitive reading strategies of the respondents when they read English academic texts, their level of motivation and reading interests, and their overall reading performance. Using descriptive survey and descriptive correlational methods with 60 randomly selected Saudi college-level EFL students in an all-male government-owned industrial college in Saudi Arabia, the study found out that the respondents moderately use the different metacognitive reading strategies when reading academic texts. Of the three categories of metacognitive reading strategies, the Problem-Solving Strategies (PROB) is the most frequently used. It was also revealed that the respondents have high motivation to read. They particularly prefer to read humor/comic books. On the level of reading comprehension performance, the respondents performed below average. Using t-test, the study reveals that there is no correlation between metacognitive reading strategies and reading comprehension. There is also no correlation between reading interest/motivation and reading comprehension. However, there is positive correlation between reading strategies and reading motivation. The findings of this study interestingly contradict previous findings of most studies, thus invites more thorough investigation along the same line of inquiry.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".