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Record W4318605741 · doi:10.1109/aivr56993.2022.00012

Improving Accessibility of Elevation Control in an Immersive Virtual Environment

2022· article· en· W4318605741 on OpenAlexaff
Hossain Samar Qorbani, Maryam Abdinejad, Ali Arya, Chris Joslin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsCarleton University
Fundersnot available
KeywordsWheelchairHuman–computer interactionComputer scienceSoftwareMultimediaVirtual realityFocus (optics)Focus groupControl (management)World Wide WebArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

Despite the advances made in Virtual Reality (VR) technology, the design of VR experiences lacks sufficient focus on accessibility and inclusion as the primary requirements. These are especially important for STEM education, where engaging in experiential activities is essential. This study was conducted to investigate accessibility considerations in the design and development of Immersive VR (IVR) learning spaces for wheelchair users. The specific research question is: How can we make a VR system easier to interact with for wheelchair users needing vertical movement? A user study with thirty (30) participants in three groups was conducted: Group A (the control group, non-wheelchair users) who used natural body movement to interact with the environment, Group B (verification group, non-wheelchair users) who used software controls for accessibility, and Group C (wheelchair users) who used the same software accessibility feature. The results indicate that the accessibility feature enabled wheelchair users to complete the tasks requiring raising or lowering of the body, with almost similar levels of completion rate and accuracy.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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