Application of the Integration Model in the System of Inclusive Education
Bibliographic record
Abstract
The introduction of inclusive education is an important factor in the development of society. The teacher plays special role in this context, the motivational readiness of teachers to work in an inclusive environment is important. The analysis of educational programmes showed that graduates of pedagogical majors are subject to equally high requirements for their psychological readiness to work with children with special educational needs. However, the educational components aimed at the formation of psychological readiness for the studied quality are not fully represented. Therefore, the aim of the study was to experimentally compare the motivational and value aspects of the psychological readiness of future teachers majoring in Special Education and Educational, Pedagogical Sciences to work in an inclusive educational environment. The research involved the method of T.I. Ilyina “Study of Educational Motivation in High School”, the Inclusive Education Questionnaire, V.V. Boyko’s Communicative Tolerance Test were used. Statistical analysis of the data was performed using the Mann-Whitney U-test, Fisher’s φ-test. As a result it was determined that students majoring in Special Education have a higher level of psychological readiness to work in an inclusive educational environment than students majoring in Educational, Pedagogical Sciences. To sum up, it is necessary to strengthen educational programmes for teacher training with the components of inclusive education, as well as to include inclusive-oriented topics in the general professional subjects. We consider the study of cognitive and activity aspects of psychological readiness of future teachers to work in the inclusive education as a prospect for further research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".