Instruction in Specialized Braille Codes, Abacus, and Tactile Graphics at Universities in the United States and Canada
Why this work is in the frame
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Bibliographic record
Abstract
Introduction This study gathered data on methods and materials that are used to teach the Nemeth braille code, computer braille, foreign-language braille, and music braille in 26 university programs in the United States and Canada that prepare teachers of students with visual impairments. Information about instruction in the abacus and the preparation of tactile graphics was also gathered. Methods A faculty representative from each university completed a 39-question online demographic survey during fall 2011. Frequency counts for each item were tabulated, and comments were reviewed and categorized. Results All 26 university programs provided instruction in the Nemeth braille code. Most also provided introductory information on foreign-language braille, computer braille, and music braille. There was a high rate of consistency across the programs in what constituted a braille error. The university programs required students to prepare tactile graphics and learn computation on the abacus. The delivery of courses through a hybrid model was most common. Discussion University programs are providing instruction in the Nemeth braille code, though there is variability in the topics that are covered, the books that are used, and the assignments that are required. Most university programs are also exposing their preservice students to specialized braille codes and are teaching them to produce tactile graphics and to perform computations on the abacus. Future studies are needed to look at the quality of instruction and, if the amount of instruction in the different topics is sufficient, to prepare future teachers of students with visual impairments adequately. Implications for practitioners Data gathered from this study will assist university programs to evaluate the content of their courses on the topics that were studied. Adjustment in the content of courses may result, which may subsequently affect the skill set of practitioners as they complete university preparation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it