Bibliographic record
Abstract
The purpose of this research study was to identify how assistive technologies can be used in the classroom to assist elementary students with reading disabilities. The intent of this study was targeted towards identifying and exploring the different types of available reading technologies, their benefits, as well as the potential drawbacks that they inflict. This research draws upon examining findings from various literature reviews which focused on the placement and the impacts that assistive technologies present to students with learning challenges. Additionally, interviews with experts in the fields of inclusive education, early literacy, technology, and English language learners were conducted to further these findings. A survey was sent out to inquire Mount Royal University teacher candidates, educational faculty, and various elementary school teachers regarding how they have seen technology used to assist readers. The results of this research study indicated that assistive reading technologies have the ability to propel readers to reach higher levels of success and self-efficacy, enable readers and nonreaders to engage with literature, increase comprehension, and decrease learning gaps between students. These findings are significant and useful for current and emerging facilitators as they serve to provide an awareness of reading technologies that are available and the benefits that they present to readers. However, it is essential to recognize that not every reading tool will produce the same results for every child and that assistive reading technologies should not solely be relied upon by students or teachers.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".