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Record W2157792901 · doi:10.1080/10400430903519944

Multiple Perspectives on the Accessibility of E-Learning in Canadian Colleges and Universities

2010· article· en· W2157792901 on OpenAlexaffabout
Jennison V. Asuncion, Catherine S. Fichten, Vittoria Ferraro, Caroline Chwojka, Maria Barile, Mai Nhu Nguyen, Joan Wolforth

Bibliographic record

VenueAssistive Technology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsMcGill UniversityDawson College
Fundersnot available
KeywordsLearning disabilityExploratory researchPsychologyMedical educationService providerWeb accessibilityService (business)MedicineThe InternetComputer scienceSociologyBusinessMarketingWorld Wide Web

Abstract

fetched live from OpenAlex

An exploratory study identified and compared the views of 77 campus disability service providers, 38 professors, and 45 e-learning professionals from Canadian colleges and universities regarding their experiences with e-learning and its accessibility to students with disabilities. Findings indicate that all groups saw benefit in having someone who makes e-learning accessible to students with disabilities on campus and that problems related to e-learning accessibility were most likely to go to campus disability service providers and least likely to e-learning professionals. Only half of the participants indicated that professors are taught about e-learning accessibility, that there is someone on campus who makes e-learning accessible, that accessibility is a criterion for selecting new types of e-learning, and that their school has e-learning accessibility guidelines or policies. These findings suggest that important e-learning accessibility problems remain. Recommendations for colleges and universities on how to increase e-learning accessibility are provided.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.963
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0240.009
Scholarly communication0.0070.002
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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 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

Citations22
Published2010
Admission routes2
Has abstractyes

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