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Record W2893122876 · doi:10.5539/jel.v7n6p156

Teaching the Skill of Reading Facial Expressions to a Child with Autism Using Musical Activities: A Case Study

2018· article· en· W2893122876 on OpenAlexvenueno aff
Bilgehan Eren

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySadnessAutismMusicalReading (process)Nonverbal communicationAutism spectrum disorderPopulationDevelopmental psychologySocial skillsClinical psychologyLinguistics

Abstract

fetched live from OpenAlex

Reading facial expressions is one of the non-verbal communication skills and is considered as being essential for children with Autism Spectrum Disorder (ASD) in terms of having effective communication and social interaction with others. Information from relevant literature indicates that musical activities can be used for teaching skills to this population. Therefore, it can be used for teaching the skill of reading expressions. The aim of this study was to investigate the effect of musical activities on teaching the skill of reading facial expressions to a child with ASD using musical activities. The study was conducted with a 5- year-old boy diagnosed with ASD attending a Special Education and Rehabilitation Center in Turkey. One-to-one music sessions were carried out once a week for 3 months. Interventions focusing on the emotion sadness consisted of a variety of musical activities. A descriptive analysis was used for all videotaped sessions. After 12 sessions, he showed success in the targetted behaviors. These results suggest that the therapeutic use of musical activities can be considered as an acceptable treatment option for teaching non-verbal communication skills 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 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.378
Teacher spread0.342 · 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 designQualitative
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

Citations3
Published2018
Admission routes1
Has abstractyes

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