MétaCan
Menu
Back to cohort
Record W2570892025 · doi:10.5206/eei.v26i1.7736

Children's Voices: Perspectives on Using Assistive Technology

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.034
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