Investigating the Efficacy of <i>Reading Adventure Time!</i> for Improving Reading Skills in Children with Visual Impairments
Bibliographic record
Abstract
Introduction: Reading Adventure Time!, formerly known as the pilot version of the iBraille Challenge Mobile App, is an educational technology tool integrating digital literacy to support braille reading and writing instruction for students in 1st–12th grades. Designed to operate on an Apple iPad with a refreshable braille display, Reading Adventure Time! uses gaming strategies to motivate students to improve literacy skills such as fluency, comprehension, writing dictation, and proofreading. Methods: The application (app) was developed under a Stepping Up Technology grant (H327S120007), which was disseminated to more than 50 teachers and students. Teachers and caregivers completed a Likert-type scale of technology skills as a pre- and postmeasure. Students’ reading speed, comprehension, and miscues were measured by the app. Results: Over 50 participants who used the app showed gains in reading and technology skills. Discussion: Students’ reading speeds, as measured by the app, mirror the reading speeds found in prior research (e.g., the ABC Braille Study). The impact on technology skills for teachers, caregivers, and students was much greater than anticipated. Implications for practitioners: The study provides evidence supporting Reading Adventure Time! as a supplemental intervention that addresses several reading skills and may be used in conjunction with a total, balanced literacy program.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".