Mind Maps to Modify Lack of Attention among Saudi Kindergarten Children
Bibliographic record
Abstract
<p class="apa">This research study aims at investigating the impact of Mind Maps on modifying the lack of attention in Arabic language class among Saudi Kindergarten children. To achieve the goals of this study the researcher used an experimental design with a random sample from AlRae’d Kindergarten’s children in Riyadh -Saudi Arabia for the academic year (2014-2015). The study sample consisted of (40) children divided into two groups: (23) in the experimental and (17) in the control group. The researcher used Al-Obeidi’s (1999) Lack of Attention Scale LAS. Validity of the tool was approved through a half division to measure lack of attention (0.93) which is considered good. The scale was used before and after the implementation of the experiment on both groups. Results showed a positive change in attention concentration in favor of the experimental group. Thus, the researcher recommended the use of Mind Maps in teaching kindergarten children to avoid attention deficiency.</p>
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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 | 0.001 |
| 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) | 0.002 | 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".