A biclustering approach for detecting cheating in educational assessments

Cheating detection in educational assessments remains a challenging issue, complicated by increasingly sophisticated cheating strategies and the rise of remote testing environments. In this dissertation, I introduce a novel biclustering approach for detecting cheating by simultaneously identifying groups of examinees and test items exhibiting suspicious response patterns. This biclustering method enables comprehensive detection of cheating behaviors by analyzing response accuracy, response time, and answer choices.