Charting Success: The Experience of Teachers of Students with Visual Impairments in Promoting Student Use of Graphics
Bibliographic record
Abstract
Introduction This study analyzed the qualitative responses of a survey of teachers of students with visual impairments in Canada and the United States about tactile and print graphic use by their students with visual impairments. Questions focused on barriers to students using graphics, strategies taught to tactile graphics users, and profiles of successful tactile and print graphics users. Methods The researchers followed a thematic analysis approach to independently code and then reach consensus on themes and subthemes of the qualitative responses. Results In general, the teachers cited a range of challenges under the main themes of quality and instruction. Subcategories included availability of time for both production and instruction, lack of standardization in material production, and student development of concepts through the use of graphics. Main characteristics of successful graphics users included motivation and an ability to apply skills across tasks. Variations in responses for tactile and print graphics users are highlighted. Discussion Findings highlighted areas in which teachers of students with visual impairments can focus to promote effective graphics use by their students. Commonality in strategies used for teaching tactile graphics was apparent, as was a general belief that being intelligent contributed to success. Implications for practitioners Frequent exposure and practice with graphics, whether tactile or print, is important. Developing an ability to analyze the features of a graphic and its impact on comprehension can inform strategies for and selection of instructional methods.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".