Pre-Service English Language Teachers' Training to Work in Inclusive Educational Environment
Bibliographic record
Abstract
The article examines aspects of inclusive education in the Republic of Kazakhstan, the degree of English language teachers’ readiness to work with children with special educational needs (SEN) particularly with behavioral disorders or difficulties or attention deficit hyperactivity disorder children (ADHD). English is considered as a second foreign language (ESL) as it is studied in all the schools over Kazakhstan and thus more attention is paid to the teachers’ readiness in the educational process. The article pays special attention to the consideration of the theoretical prerequisites for ensuring the formation of an ESL teacher’s readiness to work with children with SEN in the school education system identifying the organizational and pedagogical conditions for ensuring the formation of a foreign language teacher’s readiness to work with children with ADHD syndrome in the school education system , development of a system for organizing the process of ensuring the formation of a pre-service ESL teacher’s readiness to work with children with ADHD syndrome in the school education system. The article provides a comparative analysis of the inclusive education system in Kazakhstan and provides recommendations for solving problems related to the training of pre-service ESL teachers to work with children with special education needs particularly with behavioral disorders or difficulties (corresponds to diagnosis of attention deficit hyperactivity syndrome and other behavioral disorders, including those caused by socio-psychological factors).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".