Learning Experiences of Highly Able Learners With ASD: Using a Success Case Method
Bibliographic record
Abstract
Empirical studies investigating twice-exceptional students’ learning experiences and intricate needs remain scarce to date. Even though highly able learners with autism spectrum disorder (ASDs) demonstrate potential for high achievement and/or creative productivity, they also face potential psychosocial distresses such as anxiety disorders and poor self-concept. This study aimed to explore positive educational opportunities for highly able learners diagnosed with ASD. Using a success case method, the researchers invited two fifth-grade highly able learners with ASD to participate in this study. Data collection included interviews, observations, questionnaires, and supplementary artifacts. Adopting a general inductive analysis approach and a Glaserian coding paradigm, the researchers identified a core category, supportive school context (SSC), along with three subcategories: (a) curriculum flexibility, (b) strength-based approaches, and (c) safe environment. The findings could provide effective pedagogical strategies for teachers, school administrators, and parents. Furthermore, we rendered several suggestions for future research.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".