Inclusion of Children with Intellectual Disability in Early Childhood Education in Saudi Arabia: Impact of Teacher Characteristics
Bibliographic record
Abstract
Background: The practice of inclusive education in mainstream classrooms has gained momentum worldwide in recent years. This situation has attracted the interest of researchers in equal measures, with the main focus being directed at the impact of teacher attitudes toward inclusive education. The previous studies have mainly focused on inclusive education in primary and secondary education environments in the Western world while paying no attention to early childhood education in developing countries like Saudi Arabia. Aim: This study fills this knowledge gap by investigating the impact of teacher characteristics on the attitudes toward the inclusion of children with intellectual disabilities in early childhood education in Saudi Arabia. Methods: Data were collected through a web-based survey. Statistical analysis was conducted using SPSS to generate descriptive statistics and correlations. Results: There were statistically significant relationships between acceptance of inclusion of learners with IDs and all the independent variables; age (p=0.000), gender (p=0.021), training (p=0.000), professional role (p=0.015), knowledge (p=0.000) and experience (p=0.000). Conclusion and Recommendations: Early childhood education teachers in Saudi Arabia should receive training on special needs to attain knowledge and experiences in dealing with learners with IDs in inclusive environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".