ProTaskinator: A Persuasive Mobile Application for Reducing Procrastination in University Students
Bibliographic record
Abstract
Smartphones are an inevitable part of our lives. The flashing screen of smartphones serves as a perfect distraction medium, one that facilitates procrastination. With access to multitude of apps, university students often slack in day-to-day activities which in turn affects their personal and professional lives. Mobile health apps that help people reduce procrastination exist, but their efficacy with respect to motivating users to achieve their goals is unknown and they often do not involve the target users in their design. To address this gap, we designed ProTaskinator, a persuasive mobile application to help university students reduce procrastination. We used a user-centered design approach which involves iteratively designing low-fidelity prototype, evaluation, and a high-fidelity prototype implementing various evidence-based persuasive strategies. Forty-four (44) university students evaluated the usability of ProTaskinator. Survey and semi-structured interviews were conducted to assess participants' perceived persuasiveness of the app and implemented strategies. Our results revealed that personalization and self-monitoring were the most effective persuasive strategies, and all the implemented strategies were found to be strongly persuasive. Overall, about 80% ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=35$</tex> ) of our participants found the app persuasive, easy-to-use, and relevant for reducing procrastination. We also conducted a thematic analysis of participants' qualitative feedback to uncover more insights. The findings from our evaluation show the potential of ProTaskinator in helping university students regulate the often-overlooked challenge of procrastination.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".