Investigating comprehension measures of <i>Reading Adventure Time!</i> For improving reading skills
Bibliographic record
Abstract
Reading Adventure Time! was designed to support braille reading skills. The education technology tool operates on an Apple iPad using a refreshable braille display and was developed under a United States Federal grant, the Stepping Up Technology program (H327S120007). Forty-nine student/teacher pairs used the app. Students read passages on their braille displays and responded to comprehension questions. Data included reading comprehension scores, accuracy, and reading miscues for each passage read using the app. Students read orally and silently, and passages consisted of both literary and expository literature. Results indicated that comprehension of literary and expository texts was highly positively correlated r(48) = .79, p < .000. Student-participants in the apprentice category answered more questions correctly when reading silently. For students who used rereading as a comprehension strategy, a positive correlation was found between the number of rereads and comprehension. Variables impacting reading comprehension included the level of vision, socioeconomic status, and a preference for print as a reading medium. Overall results indicated that, in general, students’ reading comprehension was a strength. Students’ comprehension at lower grade levels was slightly higher than that in upper grade levels, and comprehension scores were similar for both literary and expository passages at all ages. Students’ comprehension was slightly better when reading orally versus silently. Students used rereading as a strategy to assist with comprehension, although not extensively. The study provides evidence supporting Reading Adventure Time! as a high-tech digital tool supporting literacy skills development in conjunction with literacy instruction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".