Nhancing cognitive and psychosocial well-being among visually impaired university students: the impact of content-focused accessible e-learning material
Bibliographic record
Abstract
This mixed-methods study explores the educational and therapeutic benefits of Content-Focused Accessible E-Learning Material (CFAELM) for visually impaired university students. A total of 49 participants with documented visual impairments were recruited from various academic disciplines across STEM, Humanities, and Social Sciences. Utilizing a combination of pre- and postintervention surveys, cognitive assessments (Montreal Cognitive Assessment - MoCA), psychosocial measures (Psychological Well-being Scale and Social Support Questionnaire), and usage logs, this research provides a holistic view of the impact of CFAELM on this demographic. The findings reveal significant improvements in cognitive functioning post-intervention, with mean MoCA scores increasing from 24.3 (±2.1) to 26.8 (±2.5). Psychosocial well-being also showed notable enhancement; Psychological Well-being Scale scores rose from 65.7 (±8.9) to 72.4 (±7.6), and Social Support Questionnaire scores increased from 28.6 (±4.2) to 31.2 (±3.8). Usage data indicated high engagement levels, with participants accessing CFAELM for an average of 5.2 days per week and spending approximately 3.5 hours per day on the material. Feedback from participants overwhelmingly recognized CFAELM as highly accessible (77.6%) and effective (83.7%) in facilitating their learning experiences. These results underscore the crucial role of tailored e-learning materials in enhancing both the cognitive functions and psychosocial well-being of visually impaired university students. By highlighting significant improvements in academic and psychosocial domains, this study contributes to the discourse on inclusive education, advocating for the integration of accessible e-learning resources in higher education curriculums to better support students with visual impairments. The study's mixed-methods approach further enriches our understanding, offering both quantitative evidence of CFAELM's benefits and qualitative insights into participants' experiences, thus providing a comprehensive overview of its efficacy in meeting the unique educational needs of visually impaired learners.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".