Procedures and Tools Used by Teachers When Completing Functional Vision Assessments with Children with Visual Impairments
Bibliographic record
Abstract
Introduction This study analyzed survey responses from 314 teachers of students with visual impairments regarding the tools and procedures used in completing functional vision assessments (FVAs). Methods Teachers of students with visual impairments in the United States and Canada completed an online survey during spring 2016. Results The majority of participants reported that they primarily assess pre-academic and academic students in kindergarten through 12th grade (K-12). More than 95% of all participants indicated that they assess near and distance visual acuity. Other commonly assessed skills and abilities were tracking ( n = 298; 95%), peripheral visual fields (n = 296; 94%), and color perception (n = 293; 93%). Approximately 50% of survey participants indicated that they use a screening tool in determining the need for an orientation and mobility (O&M) evaluation. Discussion The procedures and tools used by participants in completing FVAs varied based on the specific student being assessed. There was also considerable variation in visual skills assessed, as well as in what was included in the assessment report by the participants. Based on comments from participants, it appears that there are complex factors that influence the decision-making process regarding possible referrals for an O&M evaluation or a clinical low vision evaluation. Implications for practitioners Teachers should reflect on their own practices and procedures to determine whether they are including all pertinent information in their FVA reports, as well as explore whether they should assess additional visual skills in order to provide a rich description of how the student uses his or her vision throughout the day in a variety of environments.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".