Developing Guidelines for Evaluating the Adaptation of Accessible Web-Based Learning Materials
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
<p class="2">E-learning is a rapidly developing form of education. One of the key characteristics of e-learning is flexibility, which enables easier access to knowledge for everyone. Information and communications technology (ICT), which is e-learning’s main component, enables alternative means of accessing the web-based learning materials that comprise the content of e-learning. However, these materials can help provide a good educational experience only if they are designed carefully, which is especially true for people that have difficulties with learning from text or those with other learning disabilities (e.g., dyslexia). The main obstacle to learning for such people is usually posed by the form in which web-based learning materials are provided. Using guidelines from relevant literature, this article provides a checklist that assesses the degree to which web-based learning materials take account of the needs of people with disabilities, especially those with dyslexia. The article focuses more on the technical aspects of web-based learning materials, as they are a crucial factor that can influence the accessibility of web-based learning materials.</p>
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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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