The Effectiveness of Using Visual Organizations in Improving Reading and Writing Skills for Students with Learning Disabilities from the Teachers’ Point of View
Bibliographic record
Abstract
The study aims to identify the effectiveness of using visual organizations in improving the reading and writing skills of students with learning disabilities from the point of view of the teachers of learning resource rooms, according to the variables of gender, scientific qualification and educational experience through the use of analytical descriptive method. The study was carried out on a sample of 87 male and female teachers, (38) males and 49 females. A questionnaire of 53 items was used and analyzed statistically, the results showed that the most prominent item in the effectiveness of the use of visual organizations to improve reading and writing for students with learning difficulties is: “able to read sentences enhanced with pictures and without pictures, and distinguish between the image of the character and its writing”, the most prominent obstacle to the effectiveness of using visual organizations is not to use visual organizations that fit with the teaching methods of students with learning disabilities, as for the variables, the study showed the existence of differences attributed to the impact of gender and for the benefit of females, the results showed that there are statistically significant differences due to the effect of scientific qualification, and the absence of statistical differences due to the effect of years of experience.
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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 | 0.007 |
| 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 | 0.001 |
| 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".