Text-to-Speech Software and Reading Comprehension: The Impact for Students with Learning Disabilities
Bibliographic record
Abstract
This literature review examines the use of text-to-speech (TTS) software as an accommodation for students with learning disabilities and its impact on improving reading comprehension. As the development and availability of TTS tools and assistive technologies have increased over the past decade, it is significant to explore how they are used to accommodate students at all levels of education to promote a universal design of learning. Based on a review of the current literature and utilizing self-regulated learning theory as a framework, four significant themes have emerged: (a) TTS being seen as a compensatory tool; (b) improving reading abilities and comprehension; (c) increasing student motivation and self-efficacy; and (d) the need for training for students, educators, and parents. Findings of this literature review revealed that overall, TTS software is commonly used as a compensatory tool (mainly at the postsecondary level), has assisted in students improving reading speed, fluency, and content retention, resulted in increased student self-efficacy in reading abilities and independent learning, and that there is a significant need to allocate training and technological resources to support students. As there are various directions for future research, exploring this area can contribute to schools promoting inclusive and accommodating learning environments.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".