Designing and Reflecting on Disability-Aware E-learning Systems: The Case of ONTODAPS
Bibliographic record
Abstract
The increasing use of technology to enhance learning means both disabled students and higher education institutions face the challenge of adapting technology to meet the educational and special needs of students. As most e-learning systems are not designed to meet special needs, it is imperative to look for newer ways of designing e-learning systems to ensure that they are disability-aware and meet their assistive technology needs. In this light, this paper summarizes the result of research to seek better ways of enhancing learning for disabled students. Here, the resultant ONTODAPS system is introduced, including the methodology developed to design the system, its architecture and evaluation by 30 disabled students. The results of the usability evaluation are presented and discussed. It is hoped that researchers, instructional designers and developers of e-learning systems would look to this paper to gain insight into the design and development of disability-aware e-learning systems that will ensure that they are both accessible and usable to disabled students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".