MétaCan
Menu
Back to cohort
Record W2208192401

Building on what we know: The iPad as an assistive technology tool for post-secondary students with disabilities

2015· article· en· W2208192401 on OpenAlexaff
Linda Chmiliar, Carrie Anton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAssistive technologyAccommodationAutismPsychologyMedical educationMathematics educationAutism spectrum disorderUniversal Design for LearningSpecial needsInclusion (mineral)PedagogyComputer scienceMedicineHuman–computer interactionDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

With the emergence of the first iPad on the market, educators of students with special needs in primary and secondary schools have been interested in the device as an assistive technology tool, particularly for students with Autism Spectrum Disorder and developmental disabilities. In the last few years, this interested has extended to educators in post-secondary institutions. This is due to the fact that, the iPad may offer significant opportunities to assist students with disabilities in their learning. The iPad with its provision of accessibility supports and available multifunction apps may be an important course accommodation for students with disabilities. The use of the iPad with post-secondary students with disabilities is somewhat untried and unstudied in the post-secondary academic environment. This study examined the use of the iPad by eight post-secondary students with disabilities studying at a distance. How the students used the iPad, the apps they used and did not use, the difficulties they experienced, and the supports that they required, were all documented throughout their completion of one course.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.092
GPT teacher head0.472
Teacher spread0.380 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

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