Features of the Information and Communication Technology Application by the Subjects of Special Education
Bibliographic record
Abstract
The main purpose of this study is to reveal the features of information and communication technologies application by the subjects of education in the conditions of special (correctional) school type VIII, and to identify the level of computer literacy of special education. The study was conducted on the basis of the State Budgetary Special (Correctional) Educational Institution of the Republic of Mordovia (SBS(C) EI RM) for the students and pupils with disabilities in the “Saransk special (correctional) general education school Type VIII”, Saransk (Russia). During the experiment, each group of respondents (teachers, students with intellectual disabilities and their parents) was offered the specially developed questionnaires. The assessment of responses was made by the following criteria: completeness of elementary computer skills the subjects possess; proficiency in computer programs and electronic products; degree of independence and the frequency of their application in practice. The results show us that the computer literacy of subjects is low. During the study we revealed that the use of modern information technologies is associated with certain difficulties, associated not only with the deficiency of computer and special software, but also with a low level of knowledge and skills of work on a personal computer among the subjects of special education.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".