MétaCan
Menu
Back to cohort
Record W1547878961 · doi:10.5772/21136

Creating Inclusive Environments for Children with Autism

2011· book-chapter· en· W1547878961 on OpenAlexaffabout
Dagmara Woronko, Isabel Killor

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsYork University
Fundersnot available
KeywordsAutismMainstreamInclusion (mineral)SocializationPsychologySet (abstract data type)PopulationDiversity (politics)Universal designSpecial needsPedagogyDevelopmental psychologySocial psychologyPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

Although the prevalence rate of Autism Spectrum Disorders (ASD) varies, recent figures suggest that close to 1% of children (Autism Society Canada, 2009; CDC, 2006) are identified with ASD. With inclusive philosophies paving the way for education in mainstream classrooms, attention must be given to equitable opportunities and practices for this growing population in our school systems The research on inclusion and children with ASD is fairly limited; however, it has shown that in some regions at least, these students are excluded from school at a "significantly higher rate than students with other [special education needs]" (Humphrey & Lewis, 2008, p. 132). Children with ASD are "considered more difficult to include effectively than those with other SEN" (Humphrey & Lewis, 2008, p. 133). While there is much diversity with respect to strengths, abilities, functional levels and challenges among children with ASD, a core set of universal concerns exist. Some of these include sensory responsiveness, communication, and socialization. This chapter will focus on creating a sensory responsive environment, developing effective verbal and/or non-verbal communication, and fostering genuine relationships. Using the three main principles of Universal Design for Learning (UDL) as an overarching assumption, this chapter will explore how educators can provide effective opportunities for multiple means of representation, expression, and engagement to address common areas of need (CAST, 2011; Hehir, 2009). By following these principles, an educator is able to create a community that welcomes all while addressing students' specific needs. Many educators have been overwhelmed with the past practice of individualization in isolation, a task that left many students alone and disconnected from their peers. The current practices of differentiation and universal design for learning enable educators to plan for their students in such a way that all are integral, contributing, valued members of the learning community. This chapter provides a synthesis of current research on evidence-based classroom interventions and accommodations for learners with ASD in inclusive settings, at all age levels, with respect to sensory environments, assisted communication, and facilitation of social relationships. Emphasis is placed on accommodations that meet UDL requirements. A comprehensive search of the Education Resources Information Center (ERIC) database was conducted using keywords for, and related to, autism, universal design for learning and www.intechopen.com Autism Spectrum Disorders -From Genes to Environment 214 sensory, communication, and social skills interventions. Empirical research was reviewed, as well as qualitative studies and narratives. Manual searches of the reference lists were conducted to identify additional sources.

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)0.002
metaresearch head score (Gemma)0.004
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: Other
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0060.005
Open science0.0020.021
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.058
GPT teacher head0.367
Teacher spread0.309 · 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

Citations17
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueInTech eBooksSame topicAssistive Technology in Communication and MobilityFrench-language works237,207