Psychological Analysis of Manifestations of Nonverbal Intelligence of Senior Pre-Schoolers in the Context of Inclusive Education
Bibliographic record
Abstract
Inclusive preschool education ensures the full development of pre-schoolers with disabilities and promotes their social adaptation. The use of visual material is one of the effective means of stimulating cognitive activity in the process of preschool inclusion. The success of its perception and assimilation depends on the level of non-verbal intelligence. Aim: The research aims to analyze the manifestations of non-verbal intelligence in healthy senior pre-schoolers and children with disabilities. Methods: The research involved theoretical methods of systematization and generalization of academic information, as well as empirical methods of psychological diagnostics. Statistical indicators were determined through descriptive statistics and a one-factor analysis of variance. Results: The study's results showed a high level of nonverbal intelligence and creative abilities in healthy senior pre-schoolers and pre-schoolers with disabilities. The analysis didn’t reveal any statistically significant differences (р≥0.05) between the indicators of non-verbal intelligence of the studied healthy senior pre-schoolers and pre-schoolers with disabilities. Conclusions: children with disabilities have medium-high indicators of the development of non-verbal intelligence and creative abilities, which do not statistically differ from the indicators of their healthy peers. Prospects: The obtained results can contribute to developing adaptation schemes for children with disabilities in the context of inclusive education. The obtained data provide the background for expanding the teaching materials for developing non-verbal intelligence in inclusive groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".