Perspectives of Teachers Towards Students with Diverse Disability: A Case of Special School in Kailali District
Bibliographic record
Abstract
Working with differently able children in the classroom is challenging in under-resourced contexts for Nepal teachers. This study explores teachers’ perceptions toward managing the diversity of the students in terms of disability, teaching-learning activities, assessment practices, and interpersonal skills in a special school. This qualitative study adopts a case study design. In-depth Interviews, focus group discussions (FGD), and participant observation were the major methods for data collection. Six teachers (three ordinary and three with disability) for FGD and two key informants (head teacher and chairperson) were selected purposively for in-depth Interviews from a special school located at Attariya in Kailali district. The study shows that teachers have positive perceptions toward disabilities, especially teachers with disability. Teachers have invested efforts in managing diversity in instruction and in assessment; however, it is challenging due to the traditional approach of teaching and evaluation, lack of training and resources, and the gap between policies and practices. The study further indicates that teachers’ positive perceptions and extracurricular activities are crucial for developing students’ interpersonal skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".