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Record W2809110991 · doi:10.1177/0162643418781300

Comparison of Tablet-Delivered and Instructor-Delivered Teaching on Receptive Identification in Children With Autism Spectrum Disorders

2018· article· en· W2809110991 on OpenAlexafffund
Sabine Saade, Marc J. Lanovaz, Marie‐Michèle Dufour

Bibliographic record

VenueJournal of Special Education Technology · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de Montréal
KeywordsAutismGeneralizationPsychologyReinforcementAutism spectrum disorderIdentification (biology)ModalitiesTeaching methodMathematics educationDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

The purpose of our study was to compare the effectiveness of tablet- and instructor-delivered teaching (i.e., prompting and reinforcement) on the receptive identification of one-word concepts in children with autism spectrum disorders (ASDs). To this end, we embedded a multielement design within a multiple probe design to compare the effectiveness of the two instructional modalities in seven participants. Two of the seven participants showed generalization on all concepts in fewer instructional trials following instructor-delivered teaching, whereas the remaining five participants had mixed results depending on the concept. In total, the participants showed more rapid generalization with the instructor for 14 of the 19 concepts taught. Our results suggest that tablets should not systematically replace instructor-delivered prompting and reinforcement, but that they may be used to provide supplementary teaching to children with ASD.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.351
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2018
Admission routes2
Has abstractyes

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