Catching Cheating Students
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.
We thank the professor teaching this course for providing data. We thank Eric Andersen, Dai-Rong Chen, Yue-Shuan Chun, Jason Lai, and Dhiren Patki for their excellent research assistance. We thank Ministry of Science and Technology, Taiwan for financial support (103-2628-H-002 -001). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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