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Record W6884652968 · doi:10.11575/prism/39343

Promoting the Social Inclusion of Students with Autism Spectrum Disorder via Mobile Technologies

2021· other· en· W6884652968 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)mHealthDigital inclusionPopulationPsychological interventionSocial life

Abstract

fetched live from OpenAlex

Autism Spectrum Disorder (ASD) is the most commonly diagnosed neurodevelopment disorder in Canada, and approximately 1 out of 66 children is identified with ASD in Canada. Children with autism demonstrate impairments in language, communication skills, and social interactions; hence, these children have difficulties with communicating and interacting socially with their peers, educators, and parents. One of the latest interventions is the use of mobile technologies in assisting children with ASD in developing their social, communication, language, and other educational skills required for their academic success. However, social inclusion of these students in the classroom is still challenging. Hence, there is a need to determine effective ways of integrating mobile technologies in the classroom in order to promote the social inclusion of students with ASD. Using case study research, this mixed method study explored how students with ASD are socially included in the learning activities using mobile technologies and the impact on educators and parents. Educators, other education professionals, and parents participated in surveys and one-on-one interviews to provide further insights about their experiences in meeting students’ communication, social, language, and educational needs via mobile technologies. Analysis of both quantitative and qualitative data resulted into four emerging themes: (a) a balanced learning model where social inclusion of students with ASD is supported through the balance between mobile technologies and personal interaction; (b) resources and supports such as open educational resources, funding to support mobile technologies, evidence-based knowledge on mobile technologies, and training for all stakeholders are necessary in creating a socially inclusive environment for students with ASD; (c) goal oriented and needs-based usage where mobile technologies are used for addressing the needs of students with ASD and for achieving a particular goal or purpose in education; and (d) a team oriented approach that involves collaboration among students, parents, school administrators, educators, other education professionals, and various stakeholders in promoting the social inclusion via mobile technologies. The findings from this research study indicated various aspects (promoters) that are important in promoting the social inclusion of students with ASD via mobile technologies. Through these promoters, effective levels of social inclusion can be achieved where students’ communication, social, language, and educational needs are addressed sufficiently.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0050.004
Open science0.0010.013
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.325
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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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