The Impact of Using the Noorani Qaida on Developing Young Children’s Language Skills at Kindergartens in Al-Ahsa Governorate from the Noorani Qaida Teachers’ Perspective
Bibliographic record
Abstract
The study aims to investigate the impact of using the Noorani Qaida on developing language skills in young children attending kindergartens in Al-Ahsa Governorate from the perspective of Noorani Qaida teachers. To achieve the study's objective, a mixed-method approach was used to diversify data collection methods, including surveys and interviews. The sequential explanatory design was chosen as a type of mixed-methods research. In the first stage, quantitative data were collected through a questionnaire distributed to all the Noorani Qaida teachers in Al-Ahsa Governorate, totaling 30 teachers. In the second stage, qualitative data were collected through interviews with nine teachers to help interpret the quantitative results. The study results indicated that the impact of using the Noorani Qaida on developing language skills in young children attending kindergartens in Al-Ahsa Governorate from the perspective of Noorani Qaida teachers was significant with an average score of 4.07. The impact was highest on listening skills, with an average score of 4.41, followed by reading skills, with an average score of 4.01, both in the high range. Speaking skills ranked third with an average score of 3.99 in the high range. Writing skills ranked lowest with an average score of 3.86, still in the high range. The study recommended implementing the Noorani Qaida in kindergartens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".