The Impact of Presentation Mode and Technology on Reading Comprehension among Blind and Sighted Individuals
Bibliographic record
Abstract
SIGNIFICANCE: Technological advancements have made distributing reading materials in audio formats more common. Investigating how presentation mode impacts comprehension among sighted and blind individuals will inform the distribution of information to enhance comprehension. PURPOSE: The aims were (1) to investigate the hypothesis that reading comprehension is enhanced by increased physical engagement and cognitive effort through text or braille and (2) to explore how assistive technology impacts comprehension for blind individuals. METHODS: In a within-subjects design, 31 sighted and 34 blind participants read and listened to scientific passages and verbally answered free-response questions about what they read and heard. For sighted participants, passages were presented in text and human voice actor recordings. For blind participants, passages were presented with hard-copy braille, a refreshable braille display, voice actor recordings, and a screen reader. RESULTS: Comprehension scores were analyzed using mixed-effects regression and pairwise comparisons on the estimated marginal means. In study 1, the comprehension difference between text or hard-copy braille and the voice actor formats was assessed to address the first aim. Sighted participants had better comprehension with text (mean, 74.8%; 95% confidence interval [CI], 70.5 to 79.1%) than with a voice actor (mean, 69.7%; 95% CI, 65.4 to 74.0%; P = .02), and blind participants had superior comprehension with hard-copy braille (mean, 70.4%; 95% CI, 63.3 to 77.5%) than with a voice actor (mean, 61.9%; 95% CI, 54.7 to 69.0%; P = .03). In study 2, the comprehension differences among blind participants between the four formats were investigated to address the second aim. Comprehension was better with hard-copy braille (mean, 70.6%; 95% CI, 63.4 to 77.7%) than with a screen reader (mean, 60.7%; 95% CI, 53.5 to 67.9%; P = .02) and better with a braille display (mean, 69.7%; 95% CI, 62.5 to 76.9%) than with a screen reader (P = .04). CONCLUSIONS: Study 1 supports the hypothesis that more physically engaging tasks enhance comprehension, and study 2 suggests that listening to scientific materials using a synthesized voice may reduce comprehension ability compared with hard-copy braille and braille displays.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".