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Record W4285192590 · doi:10.5220/0010977900003182

Using Inclusive Design for People with Cognitive Limitations to Develop Online Training in the Workplace

2022· article· en· W4285192590 on OpenAlexaff
Louise Sauvé, Patrick Plante, Gustavo Angulo Mendoza, Caroline Brassard, Guillaume Desjardins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversité du Québec en OutaouaisUniversité TÉLUQ
Fundersnot available
KeywordsTraining (meteorology)Computer scienceCognitionHuman–computer interactionApplied psychologyMultimediaPsychology

Abstract

fetched live from OpenAlex

Whether it's downloading applications, doing research, using communication tools, shopping online, filling out a form or finding directions, having good digital competencies is essential in our contemporary society. But what about people with cognitive limitations (PCLs)? It appears that more than 31% of PCLs do not have the basic competencies to face this new digital reality and thus function harmoniously in society. To enable them to become autonomous in activities requiring the use of the Internet via a tablet, a research and development project is underway to create TAQ-TIC, an online digital literacy learning environment adapted to their needs. Using an inclusive design approach that puts the learner at the heart of the creation process, we validated the design, usability, and pedagogical readability of TAQ-TIC with PCLs. Findings emerged that allowed us to make recommendations for online training intended for PCLs, notably the addition of navigation indicators and contextual aids, the cleaning up of screen pages both graphically and textually, and the predominant use of video-based content.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.270
GPT teacher head0.424
Teacher spread0.154 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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