Catching Cheating Students
Working Paper 21628
DOI 10.3386/w21628
Issue Date
We develop a simple algorithm for detecting exam cheating between students who copy off one another’s exam. When this algorithm is applied to exams in a general science course at a top university, we find strong evidence of cheating by at least 10 percent of the students. Students studying together cannot explain our findings. Matching incorrect answers prove to be a stronger indicator of cheating than matching correct answers. When seating locations are randomly assigned, and monitoring is increased, cheating virtually disappears.
Non-Technical Summaries
- To identify likely cheaters, the researchers compared the predicted number and the observed number of matching answers for all...
Published Versions
Ming‐Jen Lin & Steven D. Levitt, 2020. "Catching Cheating Students," Economica, London School of Economics and Political Science, vol. 87(348), pages 885-900, October. citation courtesy of