A Guide to Assistive Technology for Teachers in Special Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Everyone has the right to learn and to succeed in education. For people with certain disabilities, learning can be a challenging task, and proper use of certain assistive technologies can significantly ease the challenge, and help the learners to succeed. For teachers in special education, knowing existing assistive technology is an important step towards the proper use of those technologies and success in special education. This chapter provides a guide for teachers about assistive technology and its uses in special education. Assistive technology for people with learning difficulties, assistive technology for the visually impaired, and assistive technology for people with hearing difficulties will be discussed. Since online learning and the Internet are becoming trends in distance education, this chapter will focus on assistive technologies for Web-based distance learning, including assistive technologies for better human-computer interaction. Selecting more appropriate assistive technology for a given learner with a certain learning disability, among many choices, will be discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it