The Lived Experiences in English Language Learning of the Thai Visually Impaired Students in the Inclusive Classroom
Bibliographic record
Abstract
Under the light of educational equality, visually impaired students (VIS) have the right to improve their quality of life through educational achievement. Fortunately, policies and regulations encourage inclusive education to support all types of students including students with visual impairment. This means that the VIS are required to complete a compulsory educational system including studying English language in school and university levels. However, the mismatch between the objectives of the support policies and the practicality towards English Language Learning (ELL) among these students still exists in Thailand, and the difficulties in the ELL of the VIS remain uninvestigated. Hence, this study aims to explore the essence and meaning of ELL in an inclusive classroom derived from the perceptions of the VIS. To elicit the experiences from the participants, the phenomenological methodology was employed as the research design. The findings were drawn from nine students with visual impairment studying in an inclusive classroom setting. The data was collected from in-depth interviews and grouped into units of meaning or themes. The results showed that the essence of this study was shaped from both negative and positive aspects of ELL in an inclusive classroom, which can contribute to the VIS, practitioners, and administrative levels as guidance for future practices.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".