Effectiveness of Information and Communication Programmes for Children with Special Educational Needs in the Context of Speech Development
Bibliographic record
Abstract
The work aims to determine the effectiveness of information and communication programs for special educational needs (SENs) in the context of speech development. The aim was achieved through the use of general theoretical methods of analysis, observations, and calculations of the ranking factor, efficiency coefficient, and Phillips correlation coefficient. It was established that the most effective approaches for the development of children's speech are the use of information technologies (2.5), social interaction (2.37), and group approach (2.25). Results: The results provided grounds for developing a speech and general development program for children aged 4 to 7. The program provided for the creation of mind maps, provision of joint reading, performance of educational songs, and development of speech in accordance with the specified topic. Training became possible due to the use of modern technologies, namely the applications MindMeister, Digital Inclusion, and speech synthesis from Google and YouTube. It was established that the developed program had a positive effect on the improvement of children’s phonetic speech compared to the results at the beginning of the study. Conclusions: The practical significance of the work implies the possibility of using the developed program for speech correction among a larger number of children. Prospects: Research prospects are related to elaborating speech development mechanisms depending on the degree of speech impairment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".