Quis custiodet ipsos custodes? How to detect and correct teacher cheating in Italian student data

The increasing diffusion of standardized assessments of students’ competences has been accompanied by an increasing need to make reliable data available to all stakeholders of the educational system (policy makers, teachers, researchers, families and students). In this light, we propose a multistep approach to detect and correct teacher cheating, which decreases the quality of student data offered by the Italian Institute for the Educational Evaluation of Instruction and Training. Our method integrates the “mechanistic” logic of the fuzzy clustering technique with a statistical model-based approach, and it aims to improve the detection of cheating and to correct test scores at both the class and student level. The results show a normalization of the scores and a stronger correction on data for Southern regions, where the propensity to cheat appears to be highest.