Bibliographic record
Abstract
Purpose – The purpose of this paper is to present the viewpoint of the authors on the use of the iPad as an assistive technology tool for post-secondary students with disabilities. Design/methodology/approach – Although this paper is not classified as a research article, the viewpoints discussed by the authors are related to a pilot study and continuing case study research they are conducting. Findings – The authors indicate that they have been surprised at the positive results they have observed in the iPad implementation, particularly with students moving to the iPad to continue their studies at the completion of the research. Practical implications – This paper discusses the opportunities and limitations afforded by the use of the iPad with post-secondary students as well as suggestions for implementation. Social implications – After decades of experience in the field of assistive technology, the authors are becoming convinced that the iPad offers significant opportunities for learning for students with disabilities. One of the exciting parts of being involved in these iPad studies has been to observe: the transformation of student study skills, the increased student self-discovery around how they learn, and the increase in student confidence in technology use. Perhaps rather than labeling the iPad as a mobile device or an assistive technology tool, the authors need to look at different terminology to define it. The ownership of this device by post-secondary students is growing every year, and it is a device that does not set students with disabilities apart from their peers. It is a device that can effectively support student learning through built in accessibility features and the use of commonly available and used apps. Perhaps using the term “equalizing technology” to describe the iPad might be more appropriate. Originality/value – This paper discusses the opportunities and limitations afforded by the use of the iPad with post-secondary students as well as suggestions for implementation. This is a rapidly developing area in universities and colleges around the world.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".