English Language Learners: Experiences of Teachers of Students with Visual Impairments who Work with this Population
Bibliographic record
Abstract
Introduction This article presents a study that gathered data from 66 teachers of students with visual impairments about their preparation to work with children who are visually impaired and are learning English, and their knowledge of instructional strategies and methods of instruction. Methods An online five-part survey was available to teachers of students with visual impairments in the United States and Canada for a month-long period in the spring of 2012. Results The 66 participants had various levels of knowledge of strategies for teaching English language learners. Many used common instructional strategies for English language learning when meeting their students’ learning needs. When they taught braille to the students, they almost always taught in English. Thirty percent of the teachers did not feel qualified to work with students who are visually impaired and English language learners. Discussion The participants were rich in their knowledge of strategies for teaching English, indicating that this group of self-selected individuals may have chosen to participate because the topic was of interest to them. Two-thirds of them felt qualified to work with students who are visually impaired and English language learners. Implications for practitioners Teachers of students with visual impairments are often knowledgeable about educational strategies used with students who are learning English. They reported an overlap in strategies used with students who are visually impaired and students who are English language learners. When they teach braille, they most often do so in English braille. There is a need to duplicate this study with a wider cross section of teachers of students with visual impairments to ensure that the data reported here are representative of the population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".