Formation of Readiness of Future Teachers of the Republic of Kazakhstan for Implementation of Education in the Inclusive Environment
Bibliographic record
Abstract
Objective: The study aims to explore the readiness of future teachers for the implementation of education in the inclusive environment. Background: Inclusive education, which is intensively included in the practice of the modern school, raises many complex issues and new challenges. The teachers need specialised training in the field of correctional pedagogy to provide professional assistance and training for children with the oppositional defiant disorder. Method: The main method of research in the article is the study of literature on the research problem and a questionnaire survey method which was participated by 247 teachers of comprehensive schools of Karaganda Region. Results: During the analysis of the results of the survey, it was noted that many teachers were not ready for full and optimal communication with children with disabilities in development. The authors identified the conditions for the formation of professional and personal readiness of the teacher to work with children with disabilities. Conclusion: The transition to inclusive education requires the participation of all specialists of the education system and the availability of fundamental theoretical developments. The main problems of teachers who will have to work in the environment of inclusion are related to their theoretical training and practical skills of teaching this contingent of children.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".