Abstract
Test collusion (TC) is a form of cheating in which, examinees operate in groups to alter normal item responses. TC is becoming increasingly common, especially within high-stakes, large-scale examinations. However, research on TC detection methods remains scarce. The present article proposes a new algorithm for TC detection, inspired by variable selection within high-dimensional statistical analysis. The algorithm relies only on item responses and supports different response similarity indices. Simulation and practical studies were conducted to (1) compare the performance of the new algorithm against the recently developed clique detector approach, and (2) verify the performance of the new algorithm in a large-scale test setting.
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