Teachers’ Attitude towards Inclusive Education: The Case of Algerian Middle School Teachers of English
Bibliographic record
Abstract
Students with special educational needs have largely been marginalised from regular schools. As the 1900s drew to a close, the Education of All Handicapped Children Act put an end to child segregation from regular schools as an attempt to prevailing equity amongst students and promoting individual differences. Accordingly, disabled students started studying alongside their same-age peers in regular schools. This step requires from teachers to renew their teaching methods and differentiate instruction to meet all students’ needs. Inclusive Education is a teaching approach which considers students’ diverse needs and works on meeting those needs through differentiating instruction. This study aims to explore teachers’ readiness to differentiate instruction to embrace students’ needs and bypass problems associated with disabled students. Methodologically, a qualitative study was carried out principally focusing on a survey and focus group. A sample of 74 in-service middle school teachers of English was chosen as an attempt to provide a more comprehensive picture of their readiness and attitude towards the integration of inclusive education in English courses at middle schools. Ultimately, the analysis of the findings indicated that middle school teachers are ready to integrate inclusive education in their classes as a useful input to meet students’ diverse needs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".