The Impact of Assistive Technologies in Enhancing English Learning Outcomes for Students with Disabilities: A Meta-Narrative Analysis
Bibliographic record
Abstract
Integrating assistive technologies in education is crucial for enhancing English learning outcomes among students with disabilities. This meta-narrative analysis aims to synthesize existing research on the impact of assistive technologies in improving English language skills in this population. We conducted a comprehensive search across databases, including Taylor and Francis and Scopus, identifying relevant studies published from 2020 to 2023. Eleven peer-reviewed articles met the inclusion criteria, focusing on empirical studies that evaluated tools such as EducaPlay, Rosetta Stone, PECS, AR applications, inclusive videos, LEA tools, VAS and multimedia tools, web-based drill programs, the AMALL application, and various assistive technologies like JAWS, MELDICT, OCR scanners, and Braille devices. Data collection involved extracting critical information on the effectiveness of the learning tools, types of disabilities, and educational outcomes related to language skills in using technology learning tools to study the English language for disabled students. The analysis revealed significant improvements in vocabulary, reading comprehension, and writing skills, demonstrating the positive impact of assistive technologies on English learning. The findings suggest that these technologies enhance engagement and motivation, offering personalized support that addresses individual needs. The discussion highlights the variability in effectiveness across different technologies and the importance of proper implementation and training for educators.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".