Defining Cut Point for Kullback-Leibler Divergence to Detect Answer Copying

Creative Commons License

UÇAR A., doğan c. d.

International Journal of Assessment Tools in Education, vol.8, no.1, pp.156-166, 2021 (ESCI) identifier


Distance learning has become a popular phenomenon across the worldduring the COVID-19 pandemic. This led to answer copying behavior amongindividuals. The cut point of the Kullback-Leibler Divergence (KL) method, oneof the copy detecting methods, was calculated using the Youden Index, CostBenefit, and Min Score p-value approaches. Using the cut point obtained,individuals were classified as a copier or not, and the KL method was examinedfor cases where the determination power of the KL method was 1000, and 3000sample size, 40 test length, copiers' rate was 0.05 and 0.15, and copying percentagewas 0.1, 0.3 and 0.6. As a result, when the cut point was obtained with the MinScore p-value approach, one of the cutting methods approaches, it was seen thatthe power of the KL index to detect copier was high under all conditions. Similarly,under all conditions, it was observed that the second method, in which the detectionpower of the KL method was high, was the Youden Index approach. When thesample size and the copiers' rate increased, it was observed that the power of theKL method decreased when the cut point with the cost-benefit approach was used.