Using Appropriate Assessment to Plan Braille Literacy Instruction
Bibliographic record
Abstract
To be effective teachers of literacy for students who read braille, we need to know what our students’ strengths and needs are as all these components come together in braille literacy. Using an appropriate assessment that addresses the skill sets involved in braille literacy is critical to putting together effective intervention packages for our students. The first author used the Kamei-Hannan and Ricci Reading Assessment (2015) and the Braille Reading Analysis Chart (Harley, et al., 1997) to determine areas of need for a student in grade 2 in a braille literacy program. Needs included: identifying ending sounds and naming final letters and sounds; basic decoding of short and long vowel sounds; recognizing sight words; and identifying letters. Specific miscue patterns in tactile perception (reversals) were identified. Information gathered from these assessments was used to develop a targeted intervention package and informed the development of a balanced literacy program for this student. Following a period of implementation, data showed evidence of overall improvement in braille literacy skills.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".