The quizzical failure of a nudge on academic integrity education: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Studies on academic integrity reveal high rates of plagiarism and cheating among students. We have developed an online teaching tool, Integrity Games ( https://integgame.eu/ ), that uses serious games to teach academic integrity. In this paper, we test the impact of a soft intervention - a short quiz - that was added to the Integrity Games website to increase users' interest in learning about integrity. Based on general principles of behavioral science, our quiz highlighted the intricacy of integrity issues, generated social comparisons, and produced personalized advice. We expected that these interventions would create a need for knowledge and encourage participants to spend more time on the website. METHODS: In a randomized controlled trial involving N = 405 students from Switzerland and France, half of the users had to take a short quiz before playing the serious games, while the other half could directly play the games. We measured how much time they spent playing the games, and, in a post-experimental survey, we measured their desire to learn about integrity issues and their understanding of integrity issues. RESULTS: Contrary to our expectations, the quiz had a negative impact on time spent playing the serious games. Moreover, the quiz did not increase participants' desire to learn about integrity issues or their overall understanding of the topic. CONCLUSIONS: Our quiz did not have any measurable impact on curiosity or understanding of integrity issues, and may have had a negative impact on time spent on the Integrity games website. Our results highlight the difficulty of implementing behavioral insights in a real-world setting. TRIAL REGISTRATION: The study was preregistered at https://osf.io/73xty .
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 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".