Caught in the Act: Predicting Cheating in Unproctored Knowledge Assessment
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Description: Cheating is a serious threat in unproctored ability assessment, irrespective of countermeasures taken, anticipated consequences (high vs. low stakes), and test modality (paper-pencil vs. computer-based). In the present study, we examined the power of a) questionnaire-based indicators (i.e., honesty-humility and overclaiming scales), b) test data (i.e., performance with extreme difficult items), and c) para data (i.e., reaction times, switching between browser tabs) to predict participants’ cheating behavior. To this end, 315 participants worked on a knowledge test in an unproctored online assessment and subsequently in a proctored lab assessment. We used multiple regression analysis and an extended latent change score model to assess the potential of the different indicators to predict cheating behavior. In summary, test data and para data were the best predictors of knowledge score differences between the online and lab session, while traditional questionnaire-based indicators, such as scales for honesty or overclaiming, were not predictive. We discuss the findings with respect to unproctored online testing in general and provide practical advice on the detection of cheating in online ability assessments.