Supporting the Transition of Elementary School Students Who Use AAC as They Are Promoted From One Grade to the Next
Bibliographic record
Abstract
Abstract One of the challenges in supporting students who use AAC and who have complex learning needs is the transition from one school year to the next. These students are typically supported by large teams, many of whom change from one year to the next. Effective, efficient transfer of knowledge and skill from one team to the next does not always occur, compromising the continuity of programming. This article will outline a 3-year project (spanning two transition periods) that aimed to improve on the transition practices within a school board in Ontario Canada. Data collection focused on beliefs and knowledge about transition, transfer of important strategies and tools, and the actual process undertaken by each school. Following analysis of data collected in five schools during the first transition period, the schools developed and piloted a transition checklist, designed to improve on practice. The transition checklist, which resulted in significant change in practice in all five schools, will be described and is offered by the author upon request.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".