Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating
开发算法检测教师作弊，利用芝加哥公立学校数据估计每年至少4-5%的小学课堂存在严重作弊，并发现激励变化会显著影响作弊频率，对教育政策设计有警示意义。
We develop an algorithm for detecting teacher cheating that combines information on unexpected test score fluctuations and suspicious patterns of answers for students in a classroom. Using data from the Chicago public schools, we estimate that serious cases of teacher or administrator cheating on standardized tests occur in a minimum of 4–5 percent of elementary school classrooms annually. The observed frequency of cheating appears to respond strongly to relatively minor changes in incentives. Our results highlight the fact that high-powered incentive systems, especially those with bright line rules, may induce unexpected behavioral distortions such as cheating. Statistical analysis, however, may provide a means of detecting illicit acts, despite the best attempts of perpetrators to keep them clandestine.