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Record W4402967388 · doi:10.5206/eei.v34i1.16705

A Document Analysis on the Use of Assistive Technology in School-Based Instruction for Children with Specific Learning Disorders in Cameroon and Canada

2024· article· en· W4402967388 on OpenAlexaffvenueabout
Blessed Yonge, Kimberly Maich

Bibliographic record

VenueExceptionality Education International · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAssistive technologyPsychologyMathematics educationSpecial educationLearning disabilityPedagogyDevelopmental psychologyComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

This study explored the use of assistive technology (AT) in the school-based instruction of children with specific learning disorders (SLDs) in Cameroon and Canada using a content-analysis, qualitative research design. Eight documents met inclusion criteria and were analyzed, leading to a comparison of both countries. Three themes are reported on: availability of AT, policies and legislation, and financial assistance. The results of the study show that AT is available for use in both countries and that there are policies and legislation in both countries to control access to and provision of AT. Also, there is financial assistance provided to help fund AT needs for children with SLD. Implications and a number of recommendations are offered, including providing AT to children with SLD; increasing funding and financial support for the provision of AT; training children with SLD, teachers, and family members in the use of AT; and enacting national AT legislation.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.374
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.382
Teacher spread0.345 · 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 teacher head, 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

Citations0
Published2024
Admission routes3
Has abstractyes

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