Assessing the usability of smartwatches for academic cheating during exams
Bibliographic record
Abstract
Smartwatches are growing in usage, yet they come with the additional challenge of regulating their usage during the taking of academic tests. However, it is unclear how effective they are at actually allowing students to cheat. We conducted an experiment that examines the use of smartwatches for cheating on Multiple-Choice Questions (MCQ) and Short Answers (SA) with either Pictures/Text shown on the watch to aid students. Our results indicate that smartwatches are neither efficient nor have a high usability rating for cheating. However, students are able to score higher on Multiple-Choice Questions compared to Short Answers. We use the cheating paradigm as an example to understand the perceived usability and appropriation of smartwatches in an academic setting. We provide suggestions that help to deter cheating in an academic setting. Our study contributes to the research on academic integrity and the growing demand of wearable technologies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".