The Effect of Mental Perseverance Strategy for Teaching Arabic Language for Developing Reading Comprehension and Divergent Thinking Skills among First-Year Literary Secondary School Students
Bibliographic record
Abstract
The current paper examines the impact of employing a mental perseverance strategy in teaching Arabic language, with a focus on enhancing reading comprehension and fostering divergent thinking skills in first-year literary secondary students. The sample included 77 students from Aqaba Governorate, divided into two groups: an experimental group exposed to the mental perseverance strategy and a control group that received traditional instruction. The first group, a total of (37) students, was chosen as a control group taught in the usual way, and the second group, a total of (40) students, was chosen as an experimental group taught using mental perseverance. To determine the effect of the mental perseverance strategy, it was conducted. Preparing two tests, the first a test in reading comprehension skills and the second a test in divergent thinking skills. The validity and reliability of the two tests were verified. The strategy was applied to the study sample for a period of four weeks, two classes per week. The study concluded that using the mental perseverance strategy had a clear impact on improving the skills of reading comprehension and divergent thinking. The observed difference was statistically significant, with the results favoring the experimental group.
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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 | 0.000 | 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".