Designing and Reflecting on Disability-Aware E-learning Systems: The Case of ONTODAPS
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it