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Record W1889460518 · doi:10.47678/cjhe.v44i1.183704

Accessible by design: Applying UDL principles in a first year undergraduate course

2014· article· en· W1889460518 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Higher Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsOntario Tech UniversityYork University
Fundersnot available
KeywordsFlexibility (engineering)Universal Design for LearningPsychologyMedical educationCourse (navigation)Variety (cybernetics)Perspective (graphical)PerceptionIntervention (counseling)Higher educationMathematics educationEngineeringComputer scienceMedicineManagement

Abstract

fetched live from OpenAlex

This article presents a case study of a technology-enhanced face-to-face health sciences course in which the principles of Universal Design for Learning (UDL) were applied. Students were offered a variety of means of representation, engagement, and expression throughout the course, and were surveyed and interviewed at the end of the term to identify how the UDL-inspired course attributes influenced their perceptions of course accessibility. Students responded very positively to the course design, and felt that the weaving of UDL throughout the course resulted in increased flexibility, social presence, reduced stress, and enhanced success. Overall, students felt more in control of their own learning process and empowered to make personal choices to best support their own learning. This course design also led to increased satisfaction from the perspective of the instructor and reduced the need for intervention by the campus disability services department.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.054
GPT teacher head0.346
Teacher spread0.292 · 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