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Record W1503967193

Digital Stories in Writing Instruction for Middle School Students with Autism

2014· article· en· W1503967193 on OpenAlexvenueno aff
Joy F. Xin

Bibliographic record

VenueStudies in literature and language · 2014
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsAutismPsychologyMathematics educationSubject (documents)Multiple baseline designElectronic publishingSpellingPublishingQuality (philosophy)Special educationIntervention (counseling)Computer sciencePedagogyLinguisticsDevelopmental psychologyLiteratureThe Internet
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effect of computer-assisted writing instruction using digital stories for middle school students with autism.  Four students diagnosed with Autism Spectrum Disorders (ASD) participated in the study. A single-subject, multiple-baseline research design across students with ABC phases was used to evaluate students’ learning. During the baseline, students were assigned topics for free writing. During the intervention, digital pictures were presented to teach students to develop six compositions following the four stages of writing, including planning, drafting, editing and publishing. Each composition was evaluated by teachers to record total number of written words, correct words, and complete sentences as well as writing quality. Subsequently, the students were assigned to develop their own digital stories for two selected topics to evaluate their skill maintenance. Results showed that the students increased their number of written words and complete sentences when computer-assisted digital stories were applied within writing instruction. It indicates that using technology in writing instruction has potential to support students with autism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.033
GPT teacher head0.397
Teacher spread0.364 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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