Academic Achievement of Children and Adolescents with High-Functioning Autism Spectrum Disorder with In-Depth Focus on Written Expression
Bibliographic record
Abstract
The goal of this research was to identify areas of strength and need in the academic abilities of students with high functioning autism spectrum disorder (HFASD).\nThree studies were undertaken: 1) six meta-analyses investigated whether nonverbal IQ was in accordance with academic achievement scores in the areas of reading, writing, and math for students with HFASD; 2) the narrative writing skills of students with HFASD were examined in order to describe the ways their writing may differ from their typically developing (TD) peers; and 3) the persuasive writing of students with HFASD was examined to determine whether their texts resembled writer-based prose to a greater extent than their peers. Across all three studies, the role of language ability as a predictor of academic success was explored.\nResults of the first study showed that students with HFASD were generally performing academically as would be expected by their Performance IQ. In addition, across all subject areas, there was great variability in student performance, such that some students with HFASD had strong academic skills and others had weaker skills. The second study demonstrated that the written narratives of students were HFASD were highly similar to those of their TD peers. However, the students with ASD were weaker in their use of narrative elements and form (narrative text structure, character development, integrating the inner worlds of their characters with the events in the story). The third study revealed that the persuasive writing of students with HFASD differed across several key indicators: syntactic complexity, lexical diversity, overall persuasive quality. As well, the texts of the group with HFASD could be characterized as writer-based prose to a greater extent than the texts of their peers. Finally, the importance of language ability in predicting academic achievement was confirmed across all studies.\nThe results of these studies highlighted the limitations of trying to characterize the academic skills of individuals with ASD using global scores of performance. The detailed descriptions of the written texts of students with ASD provided a critical foundation for developing educational interventions. These studies were the first of their kind.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".