Teacher’s attitude towards inclusion of children with disabilities in general education system of Bangladesh
Bibliographic record
Abstract
Inclusive education (IE) is widely recognised as an effective approach to ensure equity and equality in education. Teachers' attitudes play a crucial role in the successful implementation of IE, making it essential to examine their perceptions. This study aimed to measure teachers' attitudes towards the implementation of IE in Bangladesh. A quantitative research design was adopted, using a nonprobability convenience sampling method. Data were collected with the Principals' Attitudes Towards Inclusive Education (PATIE) scale. Section A addressed teacher demographics, while Section B focused on the PATIE scale. The sample comprised 120 teachers, including 60 mainstream and 60 special education teachers from various schools. Among the respondents, 45% were aged 30–40 years, 57.5% were female and 60% were senior teachers. Nearly half (45%) had special training on disability and most (85%) had direct experience with disability. The results revealed significant differences in attitudes based on school type, teaching experience with special needs and disability-related training. Overall, teachers showed a moderately positive attitude towards IE. The findings highlight the importance of fostering supportive environments where children with disabilities feel welcome and safe. To achieve effective IE, policymakers must prioritise teacher training, provide adequate resources and establish policies that encourage positive mindset changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".