Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We develop a simple algorithm for detecting exam cheating between students who copy off one another's exams. 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% of the students. Students studying together cannot explain our findings. Matching incorrect answers proves to be a stronger indicator of cheating than matching correct answers. When seating locations are randomly assigned, and monitoring is increased, cheating virtually disappears.