Leadership and Monitoring Practices for the Inclusion of Students with Autism in Mainstream Primary Schools: A Mixed-Methods Study from Israel with International Perspectives
Bibliographic record
Abstract
The inclusion of students with Autism Spectrum Disorder (ASD) in mainstream primary schools presents both promising opportunities and persistent challenges for educators and school leaders. This study investigates how principals and teachers in Israel promote effective inclusion through leadership strategies, monitoring mechanisms, and pedagogical practices, while situating the findings within international perspectives. A mixed-methods design was employed, combining semi-structured interviews with principals, teachers, and aides (qualitative component) and structured surveys measuring perceptions of inclusion efficacy and monitoring frequency (quantitative component). The sample included 10 principals, 30 teachers, and 10 aides from diverse primary schools across Israel. Findings revealed critical challenges—limited training, insufficient monitoring tools, and resource constraints—but also highlighted successful practices such as individualized education programs, structured social-emotional learning (SEL), and assistive technology integration. Comparative insights from Finland, Canada, and the United Kingdom emphasized the role of sustained professional development, systematic monitoring, and collaborative leadership in achieving effective inclusion. The study concludes that sustainable inclusion for students with autism depends on strengthening pedagogical leadership, embedding structured monitoring systems, and investing in ongoing professional learning. These findings contribute to a deeper understanding of inclusive education by linking leadership, monitoring, and teacher development to the broader goal of equity and participation for all learners.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".