Mobile Technologies That Help Post-Secondary Students Succeed: A Pilot Study of Canadian and Israeli Professionals and Students With Disabilities
Bibliographic record
Abstract
In this preliminary investigation we examine the uses of mobile devices such as smartphones, tablets and laptops for (a) non-academic and (b) academic purposes in the post-secondary classroom, as well as for (c) academic tasks outside the class by post-secondary students with disabilities. Integration of smartphones and other mobile devices into the learning process is innovative, challenging and highly relevant for post-secondary education. Also, research shows that post-secondary students like courses where use of their personal mobile devices in class is allowed. To explore how students with disabilities use their mobile devices we held four focus groups, with six to eight participants each: two in Canada (one for students with disabilities, one for professionals who assist students with disabilities) and two in Israel. The findings show that students with disabilities use their mobile devices for all the same reasons as nondisabled students. In addition, students with disabilities use general purpose mobile device features and apps as assistive aids. Implications of the blurring of the distinction between assistive and general use mobile device features and apps for the academic inclusion of post-secondary students with disabilities is discussed. It appears that for many students with disabilities, access to their personal mobile devices for academic purposes, both in and out of the classroom, is essential to ensure their full inclusion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".