MétaCan
Menu
Back to cohort
Record W2027552899 · doi:10.2190/wacx-1vr9-hcmj-rtkb

Using Virtual Reality to Teach Disability Awareness

2002· article· en· W2027552899 on OpenAlexaff
Jayne Pivik, Joan McComas, Ian M. MacFarlane, Marc Laflamme

Bibliographic record

VenueJournal of Educational Computing Research · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsNortel (Canada)University of Ottawa
Fundersnot available
KeywordsWheelchairVirtual realityPsychologyComputer scienceSoftwareApplied psychologyHuman–computer interactionMultimediaMedical educationMedicine

Abstract

fetched live from OpenAlex

A desktop virtual reality (VR) program was designed and evaluated to teach children about the accessibility and attitudinal barriers encountered by their peers with mobility impairments. Within this software, children sitting in a virtual wheelchair experience obstacles such as stairs, narrow doors, objects too high to reach, and attitudinal barriers such as inappropriate comments. Using a collaborative research methodology, 15 youth with mobility impairments assisted in developing and beta-testing the software. The effectiveness of the program was then evaluated with 60 children in Grades 4–6 using a controlled pretest/posttest design. The results indicated that the program was effective for increasing children's knowledge of accessibility barriers. Attitudes, grade level, familiarity with individuals with a disability, and gender were also investigated.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.327
GPT teacher head0.556
Teacher spread0.229 · 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 designNot applicable
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

Citations53
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational Computing ResearchSame topicInclusion and Disability in Education and SportFrench-language works237,207