Straight from the Source: Perceptions of Students with Visual Impairments about Graphic Use
Bibliographic record
Abstract
Introduction This study analyzed the responses of a survey of students with visual impairments in Canada and the United States about their use of tactile and print graphics. Demographic, Likert scale, and open-ended questions focused on perceptions of quality, preferences, instruction, and strategies. Methods Percentages of agreement for tactile and print graphic users are reported. Comparisons were made between the two groups. Results Students felt positive about the quality of the graphics, but density and complexity were identified as challenges. Students varied as to whether they felt graphics supported their understanding of concepts. Both groups indicated that written descriptions were helpful. Students in this survey were positive about knowing how to use strategies that help them access graphics. Discussion Tactile graphics appear to play an additional role in inclusion for some students. Attention to instructional needs should not overlook students with visual impairments who use print graphics. Additional inclusion of quality written descriptions may support understanding of graphical information. Implications for practitioners Conceptual understanding would be supported by helping students recognize where graphics and descriptions are useful. Timeliness of access to graphics in the classroom and attention to quality graphics that reduce complexity and clutter remain important.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".