Bibliographic record
Abstract
This research manuscript investigates how technology can be used to help students with dyslexia. Using a Google Forms survey and an interview with an expert on the topic, different types of technologies, the pros and cons of using assistive technology, and recommendations for implementing assistive technology in the classroom are listed. It was found that assistive technology is beneficial for students with dyslexia, but each student will benefit from different technologies. The main challenges with assistive technology that this research project uncovered included, glitches, not being user friendly, and the cost of some of these technologies. These problems apply mainly to higher tech assistive technologies, so including low tech assistive technologies in the classroom as well as high tech options could benefit students and avoid some of these challenges. Understanding some of the different assistive technologies that are available and beneficial for students with dyslexia is important for those involved in the education system so that we can give students with dyslexia the tools that they need to succeed. When educators know about, understand how to use, and have the resources to acquire assistive technologies then technology can be used to help students with dyslexia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".