Augmented Reality Based Spelling Assistance to Dysgraphia Students
Bibliographic record
Abstract
Dysgraphia, a learning disability associated with writing skills, hinders students to put their thought on paper and write correctly. Writing problems hit students most frequently that one third students become failed to acquire writing skill. Different IT based assistance solutions available for dysgraphia students but most of them are accommodations based or provides writing alternatives rather than developing writing skills of a dysgraphia student. Handwriting is an essential skill for academic life and developed handwriting skill helps student to protect their self-esteem and build student’s confidence to participate in other activities during class. Most of available writing assistance solutions do not provide interesting ways to acquire writing skills. To handle this problem, augmented reality (AR) based dysgraphia assistance solution has presented in this work. This study utilized AR to develop dysgraphia student’s interest in writing and used it to assist in writing activity by providing help in spellings. AR based dysgraphia assistance writing environment (AR-DAWE) modal use Google cloud API of speech-to-text and addressed one of the important issues of dysgraphia student that is associated with spelling mistakes.
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 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".