The Role of RAMP Initiative (Reading and Mathematics Project) in Raising the Level of Students in the Basic stage in Reading and Numeracy Skills
Bibliographic record
Abstract
This study aimed to identify the RAMP initiative’s role (read comprehensively, answer with understanding) in raising the level of literacy skills and numeracy from the point of view of teachers. It also aimed to identify its effectiveness in improving the reading and writing level of the basic minimum stage, as an initiative that reduces the delay in reading and helps in the development of skills to help students solve mathematical problems with understanding and accommodating. Closed questionnaires were distributed to the Central Badia region; the questionnaire consisted of (35) paragraphs divided into three areas of reading, writing, and arithmetic, where 88 teachers answered the questionnaire. Statistical analysis was adopted (Statistical Package of Social Sciences (SPSS) was adopted to show the study results. The results showed a role for the RAMP initiative in raising the level of reading and numeracy skill to a reasonable degree, where the skill of reading got a good degree, was the highest skill of voice awareness to an extraordinary degree and other standards are good. Writing skill got a good degree; it was the highest, the skill of writing words and the least skill of creative writing, and also obtained the skill of calculating a good degree above (counting units) and the lowest domain (participation and composition of groups). The results showed no statistically significant differences due to variable years of experience, while the results showed statistically significant differences in favor of a bachelor’s degree.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".