An Empirical Study on Enhancing English Reading Skills among Female Middle School Students in Government Schools in Saudi Arabia and its Direct Impact on Academic Achievement
Bibliographic record
Abstract
This study aimed to address the issue of low English reading proficiency among female middle school students in government schools in Saudi Arabia. The study targeted a sample of year-seven students; forty-two students were under direct observation in classroom and participated in interviews, questionnaire, and assessment forms. The primary focus of this study was to employ reading comprehension assessments as the major influencing factor. The main research question was how reading comprehension assessments was used to enhance the English reading proficiency of year 7 female students. This study primarily examined reading comprehension assessments, gathered existing literature on the topic, and formulated a hypothesis to help resolve the problem. The result of this study shows clear indication that reading comprehension assessments had a positive impact on students’ attitude toward the English language subject. Students’ reading proficiency improved, and their confidence in reading in front of their peers increased. Incorporating various activities contributed to achieving lesson objectives, deepened comprehension, and enhanced reading comprehension skills. Integrating modern technology into education stimulated students’ motivation to learn.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".