Gender Differences in Metacognitive Reading Strategies of Business and Engineering Students in Oman
Bibliographic record
Abstract
Students often need help with reading comprehension, understanding, and retaining the meaning of the text. Overcoming reading difficulties involves teaching learners metacognitive strategies for effective reading. Metacognitive reading strategies enable readers to engage more actively with the material, improving comprehension, retention, and critical thinking skills while reading. This paper investigates the metacognitive strategy preferences of male and female Omani learners enrolled in Business and Engineering programs. Data was collected using the Survey of Reading Strategies (SORS). One hundred eighty-eight undergraduates, comprising 81 males and 107 females, responded to the survey. The data was analyzed using the SPSS statistical program. Descriptive statistics means and standard deviation were used to identify male and female learners' most preferred strategy scale (Global, Support, and Problem-solving strategies). Also, a t-test was used to determine individual strategy preferences between genders. The findings reveal no significant differences between male and female students for global and problem-solving strategies. However, results show a substantial difference between both genders for support strategies. Finally, the findings will inform curriculum developers and teachers in developing targeted metacognitive reading strategies to enhance students' competence in reading skills.
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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) | 0.004 | 0.001 |
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".