The Opinions of Special Education Teachers on the Use of Assistive Technologies in Special Education
Bibliographic record
Abstract
This research is a descriptive study aiming to determine at what level assistive technologies are utilized in the special education field, to identify the barriers to use of assistive technologies, and to support strategies based on opinions and thoughts of teachers working on this field. The population of this research was constituted by teachers working in public and private education institutions operating in 2017-2018 academic year affiliated to the city of Hakkâri in Turkey. In this research, a survey developed by Linda Chmiliar of Athabasca University in 2007 was used as the data collection tool. The study included 211 teachers from different age groups, gender and students with special needs at different levels of instruction. As a result of the research, it was determined that the most restricting factors in the use of assistive technologies in special education were the cost of the equipment, lack of sufficient assistive technology tools for the students and lack of knowledge about the assistive technologies. Factors related to the most important support strategies related to the use of assistive technologies were identified as budget support for providing tools and equipment, educational support for assistive technologies and technical support for the use of equipment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".