Subjects Adaptation Techniques for Primary School Pupils with Special Educational Needs
Bibliographic record
Abstract
The research on the adaptation of children with disabilities in general education institutions is topical, as the education of children with special educational needs provides helps them to acquire a profession and become self-sufficient. The aim of this work was to develop subjects adaptation techniques for primary school pupils with special educational needs. The teachers’ attitudes to inclusive education were determined through the Attitudes Towards Inclusion Scale (AIS Scale). The Teacher Efficacy for Inclusive Practices (TEIP) Scale was used to establish the teachers’ effectiveness in implementing inclusive practices. The Concerns about Inclusive Education Scale (CIES) was used to study concerns about inclusive education, while the intention to teach in an inclusive classroom was identified through the Intention to Teach in Inclusive Classroom Scale (ITICS). The impact of general education alongside ordinary children on children with special educational needs was determined through Stott’s Observation Charts. The techniques proposed in this study involve the use of different methods, tools, technologies by teachers that are required for the adaptation and correction of subjects. It is necessary to take into account the individual capabilities of pupils with special educational needs, as well as to meet the educational needs of ordinary pupils. It is worth to further work on finding new methods and techniques for adapting curricula to the inclusive educational environment based on new advances in pedagogy and innovative technologies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".