Examining how Teachers Support the Social and Academic Needs of Children with Autism Spectrum Disorder in Play-Based Kindergarten Classrooms
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that emerges in early childhood and is characterized by impairments in social interactions, communication, and language, and includes the presence of restricted or repetitive behaviours that can impede or interrupt functioning in daily living. Empirical evidence routinely suggests that structured tasks, predicable instructional context, and explicit teaching contribute to better social and academic success for kindergarten students with ASD. As such, it is important to understand how teachers instruct and plan for children with ASD in play-based kindergarten classrooms. Kindergarten classrooms in Ontario follow a play-based learning model, where the emphasis is placed on play, inquiry, and elements of child-led learning to aid in developmental and academic learning over the two-year program. Given the evidence of structured learning contexts for students with ASD and the open-ended nature of play-based learning, little is known about how teachers navigate the mandated play-based kindergarten curriculum in Ontario for students with ASD. This research explored how teachers support their students with ASD in a play-based kindergarten setting, and whether a possible tension existed when instructing in this environment. This study used a qualitative multiple case study to address: (1) how teachers are supporting the social and academic needs of students with ASD in a play-based environment, and (2) how teachers’ perceptions of play influence the types of interactions they plan for students with ASD. Findings from the inductive analysis noted a connection among cases regarding the value of play in the kindergarten classroom, perceptions of successful engagement and connection to learning for students with ASD, and allowing adaptations to mitigate challenges in the kindergarten classroom. In addition to findings, implications for practical use and future research are discussed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".