Teaching Students with Disabilities in Post-secondary Landscapes: Navigating Elements of Inclusion, Differentiation, Universal Design for Learning, and Technology
Bibliographic record
Abstract
High school graduates with disabilities—many now accustomed to assistive technology and learning accommodations—are moving on to higher education, comprising approximately 10% of the student body. Although post-secondary classroom demographics are becoming increasingly diverse in terms of abilities and learning needs, educators’ methods of teaching are not keeping pace. They often have good intentions to sustain the use of technology in the classroom, but, over time, its use decreases because of waning commitments of time and to training. There is little research revealing how educators navigate their changing roles within these classrooms and how they embed inclusion, differentiated instruction, universal designs for learning, and technology to address not just the learning needs of students with disabilities, but of all learners. This article reports on these issues, arguing the importance of seeking professional development in teaching methods and narrowing the gap between desired and actual use of effective tools to engage learners who need learning accommodations at the post-secondary level.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".