Graphic Reading Performance of Students with Visual Impairments and Its Implication for Instruction and Assessment
Bibliographic record
Abstract
Introduction: The ability of students to engage with graphical materials supports learning in science, technology, engineering, arts, and mathematics areas. For students with visual impairments, understanding the factors that contribute to the effective interpretation of graphics can promote meaningful access to the curricula. Methods: Forty students with visual impairments completed multiple-choice question tasks for five types of graphics presented in their medium of choice and provided difficulty ratings. The teachers of students with visual impairments rated the students on several factors. Statistical analyses investigated the relationship between performance differences and teacher-rated factors. Results: Significant differences in performance between print and tactile graphics users were found for bar graph, map, and total correct responses on all tasks. For some tasks, perceived difficulty by tactile graphic users did not align with actual performance. Teachers’ ratings of students who had Individualized Education Program goals for graphics, independence in using graphics, problem-solving ability, mathematics ability, and frequency of engaging with graphics contributed to significant differences in performance across total correct and most individual graphic results. Discussion: Although medium type was a significant contributor across graphic types, some teacher-rated variables appeared to mitigate the importance of medium on student performance. Depending on the graphic type, experience, content knowledge, skills with graphics, and confidence and motivation can all affect student performance when interpreting graphics. Implications for practitioners: Teachers should provide students with early and frequent opportunities to engage with graphics and support their problem-solving abilities regarding how to engage with different graphic types to enhance their independent use of graphics.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".