Speed and Accuracy Measures of School-Age Readers With Visual Impairments Using a Refreshable Braille Display
Bibliographic record
Abstract
This study provides information on the use of a refreshable braille display in relation to reading speeds and accuracy for students with visual impairments. The characteristics and variables which were statistically significant predictors of reading speed were explored. Forty-nine students in grades 1–9 participated with their teachers of students with visual impairments. In this 16-week study participants used the Reading Adventure Time! app to complete a pretest, intervention, and eight progress monitoring checks. Using descriptive statistics, correlation, and multiple regression analyses the researchers found silent reading speeds averaged 16.80 WPM at grades 1–2, 46.43 WPM at grades 3–4, 46.12 WPM at grades 5–6, and 50.51 WPM at grades 7–9. Oral reading speeds averaged 18.37 WPM at grades 1–2, 49.05 WPM at grades 3–4, 45.62 WPM at grades 5–6, and 45.82 WPM at grades 7–9. On average, there were few miscues for participants at all grade levels. Statistically significant predictors of reading speed included the number of braille cells on the refreshable braille display, the proportion of students receiving free and reduced lunch recipients, time spent in literacy instruction with the general education teacher, and whether the student was a dual braille and print reader. Reading speeds were comparable to those found in studies which examined reading paper-based formats. The most common statistically significant predictor of reading speed was the number of cells on the refreshable braille display. Wise decisions about the types of refreshable displays used can potentially make a difference in students’ reading speeds.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".