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Record W2954001945 · doi:10.1177/0162353219855681

Learning Experiences of Highly Able Learners With ASD: Using a Success Case Method

2019· article· en· W2954001945 on OpenAlexafffund
I-Chen Wu, C. Owen Lo, Kuei-Fang Tsai

Bibliographic record

Venuejournal for the education of the gifted · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsPsychologyPsychosocialFlexibility (engineering)CurriculumAutism spectrum disorderContext (archaeology)AnxietyAutismDevelopmental psychologyMathematics educationPedagogyPsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.384
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2019
Admission routes2
Has abstractyes

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