Using Margaret Carr's Learning Stories for Children with Autism Spectrum Disorder: Parental and Teacher Feedback
Bibliographic record
Abstract
This study explored the potential of Margaret Carr’s (2001) learning stories framework to assess the learning of children diagnosed with Autism Spectrum Disorder (ASD). Parents of four children with ASD who were enrolled in a pre-school program undertook writing learning stories of their children at home over a two-week period. During the same time period, a teacher who is also the researcher in this study, created learning stories for these children in the pre-school classroom. At the end of the two-week period, the parents and the teacher/researcher met to compare and discuss their stories and use the information to create individual program planning (IPP) goals for the four children. Findings indicate that these discussions helped to clarify the children’s behaviours and actions resulting in the development of more meaningful IPP goals. All the parents felt their participation in the process to have greatly benefited their child’s programming. However, questions arose regarding whether it was the actual format of the learning stories themselves, or whether it was the dispositional attributes in Carr’s framework which resulted in rich discussions.
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".