Development of a novel, theoretically motivated scale to assess cognitive learning styles related to the autism spectrum
Bibliographic record
Abstract
BACKGROUND: Although theoretical efforts have been made to address the cognitive learning styles of individuals on the autism spectrum, no instrument to measure such learning styles is currently available. The current study aimed to develop such a scale based on the learning style theory of Qian and Lipkin (Front Hum Neurosci 5:77, 2011). METHODS: Response data from total of 768 undergraduate students was used for this study. This sample was split into two subsamples of N = 460 and N = 308 for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), respectively. The correlations between the resulting new subscales and some other potentially related measures were examined. RESULTS: A three-factor structure with 19 items was obtained measuring need for task clarity/familiarity, susceptibility to cognitive load, and the grasping of conceptual relations. CONCLUSIONS: This newly developed measure can be used to help understand the nature of the individual differences in cognitive processing that are evident across both the autism spectrum as well as the overall population more generally.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".