Graphics Out Loud: Perceptions and Strategic Actions of Students With Visual Impairments When Engaging With Graphics
Bibliographic record
Abstract
Introduction: Facility in graphics use is critical to accessing data visualizations in science, technology, engineering, the arts, and mathematics (STEAM) content areas. Efforts to understand the cognitive processes underlying strategic action by students with visual impairments must consider both metacognition and self-regulated learning. Methods: Think-aloud transcripts were analyzed using a priori level one coding based on the Model of Graphic Interpretation (MoGI) followed by second-level coding to analyze nuanced commonalities and differences based on performance, medium, and level. Results: Differences in each component of the MoGI were found for print graphic and tactile graphic users, particularly based on performance and level. Higher performers were better able to articulate strategy use and reasons for selecting strategies. Discussion: Findings coincide with quantitative findings of the participants (see Zebehazy & Wilton, 2021 ). Transcripts provided additional confirming evidence of the interdependence of MoGI components. Implications for Practitioners: Use of think aloud can support assessment and instruction of students with visual impairments to build strategic action and metacognition when engaging with 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".