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Record W2916403622 · doi:10.1177/0162643419841550

Understanding Teacher Perceptions of Assistive Technology

2019· article· en· W2916403622 on OpenAlexafffundabout
Bronwyn Lamond, Todd Cunningham

Bibliographic record

VenueJournal of Special Education Technology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsInstitute for Christian Studies
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPerceptionCertificationThematic analysisAssistive technologyLiteracyConsistency (knowledge bases)Professional developmentMedical educationComputer literacyTeacher educationMathematics educationApplied psychologyPedagogyComputer scienceQualitative researchMedicine

Abstract

fetched live from OpenAlex

This research examined Grade 6–10 Ontario Certified Teachers’ ( n = 24) perceptions of assistive technology (AT) and the factors correlated with perceived usefulness of AT. A mixed methods design that included a survey consisting of open- and closed-ended items elicited information about teachers’ AT knowledge and training, their basic computer literacy, their perception of administrative support for access to and implementation of AT, the usefulness of AT, and the factors that encourage or discourage AT use in the classroom. Results of correlational analysis suggested that computer literacy and AT knowledge were significantly positively correlated with perceived usefulness of AT, and a thematic analysis further identified that training and student factors may influence AT use. Implications for preservice and professional development teacher training are discussed, given the consistency of teacher-reported need for greater training opportunities for both students and teachers.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.098
GPT teacher head0.442
Teacher spread0.344 · 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 designObservational
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

Citations36
Published2019
Admission routes3
Has abstractyes

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