Supporting the information journey of students with disabilities through accessible learning materials
Bibliographic record
Abstract
Purpose This paper aims to suggest how the information journey of students with disabilities could be facilitated, by first revealing the existence of inaccessible formats such as Portable Document Format (PDF) and then suggesting the inclusion of alternative formats of accessible learning materials, thus improving retrieval. Design/methodology/approach A sample of 400 articles published over 10 years (2009-2018) from four journals are selected and analysed for accessibility against the Web Content Accessibility Guidelines WCAG 2.0 by using automated accessibility checkers, a screen reader and manual human expertise. The results are presented and recommendations made on improving accessibility. Findings The findings suggest that the PDF versions of the selected journal articles are not accessible for screen reader users but could be improved by adopting accessible and inclusive practices. Including alternative formats of the learning materials could help support the student information journey. Research limitations/implications The results of the study might not be very representative of all the articles in the journals given the small sample size. Additionally, the criteria used in the study do not consider all existing disabilities. Thus, although the PDFs may be inaccessible for some people with disabilities, they may be accessible to others. Practical implications Given that PDFs seem to be the preferred format of journal articles online, there is potential for a difficult information journey for some students due to the limitations posed by inaccessibility of the PDFs. Thus, it is recommended to include alternative formats which could be more accessible, giving the student the choice of accessing the learning materials in their preferred format. Social implications If students are unable to access the learning materials that are required for their course, this could lead to poor grade, which might negatively affect the students’ morale. In some cases, some students might drop out. Originality/value This study analyses the accessibility of learning materials provided by a third party (journal publishers) and how they affect the student, something that is not usually given much importance when research in accessibility is carried out.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".