Do Computer-Based Accommodations Matter? An Evaluation of Bundled Accommodations for Secondary Students With Mild Intellectual Disabilities
Bibliographic record
Abstract
Objectives: To investigate the effectiveness of accommodation policies and teaching practices for secondary students with mild intellectual disabilities, the present study compared the probability that the secondary school accommodated students- if they received assistive technology, computer, and various combinations of accommodations for the provincial math and literacy assessments in Ontario, Canada- would acquire levels of academic achievement comparable to non-accommodated counterparts. Methods: A total of 217 bundled packages, consisting of multiple accommodations, for secondary students with mild intellectual disabilities were examined by an adjusted odds ratio method in the present study. Results: Our results suggest that the probability of achieving the literacy standards differed among students with mild intellectual disabilities in relation to who did or did not receive specific combinations of accommodations. We found that accommodations that involved computer and/or assistive technology were more beneficial for literacy, rather than the math assessment, for accommodated students with mild intellectual disabilities. Conclusion: Our findings help identify the computer-based accommodations that produced significant differential effects on literacy in students with mild intellectual disabilities. Implications for education and future research are also discussed in this paper.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".