What Apps Do Postsecondary Students with Attention Deficit Hyperactivity Disorder Actually Find Helpful for Doing Schoolwork? An Empirical Study
Bibliographic record
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) experts and individuals with ADHD have made many recommendations concerning mobile apps that could potentially help college students succeed. But do students know about these recommended apps? Do they find them useful? How do students use mobile apps for completing schoolwork? To answer these questions we carried out two empirical studies. In study 1, 35 Canadian postsecondary students who self-reported ADHD and 74 students without disabilities completed an online LimeSurvey questionnaire and indicated which of 20 expert recommended schoolwork-related apps they had tried and which they liked. In Study 2, nine students with ADHD specified how they used their technologies to complete schoolwork. Results indicate that students with and without ADHD were familiar with only 13 of the 20 apps recommended by experts, and that they liked only 11. For completing academic work, the most popular apps were built-in smartphone camera and recording apps. Students also found Microsoft, Google, Pomodoro and Kahoot apps helpful. Discord was the most popular app for collaboration with classmates. Results show that students with and without ADHD found the same apps and technologies helpful. To the best of our knowledge this is the first study to actually query students with ADHD about their app use and preferences. We conclude that disability service providers, academic administrators and access technologists need to stay up-to-date about general use mobile apps to enable them to make appropriate recommendations to help students with ADHD succeed in college.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".