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Record W2121405609

AccessAbility: Enabling Technology for Life Long Learning Inclusion in an Electronic Classroom - 2000

2002· article· en· W2121405609 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEducational Technology & Society · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInclusion (mineral)Educational technologyMathematics educationOnline learningComputer scienceInstructional designLearning disabilityElectronic learningUniversal Design for LearningAssistive technologyPopulationResource (disambiguation)MultimediaPedagogyMedical educationPsychologySociologyMedicineHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

A significant portion of the population is at risk of being excluded from online learning environments. People with learning and/or physically disabilities may be prevented from participation due to problems in the design of the learning technology itself or with the pedagogy directing its use. This paper presents an overview of the results of Inclusion in an Electronic Classroom, a study conducted by the Adaptive Technology Resource Centre at the University of Toronto. The study examined six courseware environments as used by people with various disabilities. Results show that it is imperative for courseware manufacturers and instructors to take accessibility into consideration when designing online courses. Operating an accessible, inclusive electronic classroom ensures students with disabilities can participate with parity in global educational exchange.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.339
Teacher spread0.312 · 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