General Education Teacher’s strategies and approaches for supporting intermediate/secondary students with Autism Spectrum Disorder (ASD)
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a range of neurodevelopmental disorders that tend to be diagnosed in childhood. The charactersitics of ASD exist in three categories: communication, social and repetitive body movements or behaviors. Specifically in Canada, Fombonne (2005) estimated a prevalence rate of 1 in 165. The province of Ontario saw a 58% increase in the enrolment of students with Autism over a recent seven year period. The increase in prevalence and placement of students with ASD in general education classrooms warrants the need to understand the impact general education teachers have on the academic achievement and social experiences of students with ASD through their practice. This study explores how Ontario intermediate and senior general education teachers are working to optimally support students with ASD. The instrument of data collection was a semi-structured interview protocol which was applied to two secondary teachers. Analysis of the data demonstrated that building student rapport, individualized instruction, a focus on equity and inclusive pedagogy were effective strategies for supporting students with ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".