Transactions Within a Classroom-Based AAC Intervention Targeting Preschool Students with Autism Spectrum Disorders: A Mixed-Methods Investigation
Bibliographic record
Abstract
This study examined the changes in the communication skills of preschool students with autism spectrum disorder (ASD) that resulted from an intervention that featured three evidencebased, transactional approaches to augmentative and alternative communication (AAC) intervention: (a) attributing communicative meaning to student behaviours; (b) providing aided language input; and (c) focusing on graphic symbols representing core vocabulary. Using a mixed-methods design with multiple sources of data (i.e., observation field notes, IEPs, and direct communication assessment), the study was conducted in three classrooms with 6 educators and 13 preschool students with ASD. The purpose was to explore interaction patterns between educators and students while also analyzing improvements in student communication as measured by the Communication Matrix. The results point to a transactional relationship between educators’ and students’ communication across the three classrooms. This group of preschool students with ASD learned to use abstract graphic symbols representing core vocabulary to request as a result of educators’ focus on this requesting. A number of students demonstrated growth in use of non-symbolic communication for social interaction and information sharing as a result of educators’ increased use of aided language input.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".