MétaCan
Menu
Back to cohort
Record W2145422031 · doi:10.1076/edre.7.2.185.3869

Technology Integration for Students with Disabilities: Empirically Based Recommendations for Faculty

2001· article· en· W2145422031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducational Research and Evaluation · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsMcGill University
Fundersnot available
KeywordsVariety (cybernetics)Adaptation (eye)Sample (material)Medical educationPsychologyAssistive technologyMathematics educationComputer scienceMedicineHuman–computer interaction

Abstract

fetched live from OpenAlex

In 3 empirical studies we examined the computer technology needs and concerns of close to 800 college and university students with various disabilities. Findings indicate that the overwhelming majority of these students used computers, but that almost half needed some type of adaptation to use computers effectively. Data provided by the students and by a small sample of professors underscore the importance of universal design in a variety of areas: courseware development, electronic teaching and learning materials, and campus information technology infrastructure. Sex and age of students were only minimally related to attitudes toward computers or their use in our samples. Key findings summarize the problems faced by students with different disabilities as well as the computer related adaptations that are seen as helpful. These are used to formulate concrete, practical recommendations for faculty to help them ensure full access to their courses.

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.

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)0.004
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.449
GPT teacher head0.614
Teacher spread0.165 · 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