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Record W2570892025 · doi:10.5206/eei.v26i1.7736

Children's Voices: Perspectives on Using Assistive Technology

2016· article· en· W2570892025 on OpenAlexaffvenue
Robin Elizabeth Schock, Elizabeth A. Lee

Bibliographic record

VenueExceptionality Education International · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsQueen's UniversitySt. Lawrence College
Fundersnot available
KeywordsThematic analysisAssistive technologyPsychologyFocus groupPerceptionQualitative researchMathematics educationPedagogyDevelopmental psychologyMedical educationComputer scienceMedicineSociology

Abstract

fetched live from OpenAlex

Rarely are the views of children with learning disabilities elicited. In this study, we used focus groups involving eight students with learning disabilities to explore their self-perceptions as learners and writers using assistive technology (AT). Three groups of two to three Grade 4–8 students and their parents participated in the qualitative study. Both student and parent responses provided data for thematic analysis that resulted in three themes: (a) changes in students’ self-perceptions as learners; (b) student and parental self-reported benefits of using assistive technology; and (c) inconsistencies in approaches to using assistive technology in schools. The implications for education are greater attention to the views of elementary school children; greater focus on the use of AT in the classroom; and greater AT training for teachers in order to better support the use of AT by students with LD.

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)0.006
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0080.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.392
Teacher spread0.358 · 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 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

Citations9
Published2016
Admission routes2
Has abstractyes

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Same venueExceptionality Education InternationalSame topicDisability Education and EmploymentFrench-language works237,207