Development of Communication and Speech Skills of Students in the Process of Education
Bibliographic record
Abstract
The article aims to study and diagnose school students with disabilities' levels of communication and speech skills development. Communication technologies in the process of forming communicative competence will contribute to the successful implementation of correctional work, if necessary, an individual or adapted educational program for students with disabilities. During the conduction of the study, the authors used the following types of methods: analysis, synthesis, modelling, observation, survey - statistical method analysis of the data allowed to differentiate levels of communication and speech skills development. The authors revealed the most effective forms and methods of work at the literature levels. The scientific-methodical and practical aspects of the application of communication technologies for the development of school students with disabilities' speech activities were generalized. In modern education, it has been substantiated that communication technologies are used as a means of communication skills formation and as a means of activation of the students' cognitive work. Based on the theoretical analysis of the investigated problem and the experimental work results, the authors have formulated the generalizing positions. It was concluded that the solution to the investigation of the school students with disabilities' speech activities development problem is possible at smart scientific and methodological use of communication 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".