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Journal of Innovation in Science, Engineering and Technology

Document Type

Original Study

Abstract

With more and more institutions adopting online delivery of education, the accompanying need for assessing students online is increasing. However, the tendency of some candidates to engage in fraudulent acts to gain undue advantage over other candidates undermine the integrity of the online testing process. It is a major challenge faced by test administering institutions. The most widespread fraudulent act is impersonation. Existing studies primarily focus on the detection of this particular fraudulent act. However, there are other fraudulent acts as well, resorted to by cheating candidates. Methods of detecting these other acts have not been sufficiently studied. The objective of the study presented in this paper was to address this gap through the extension of a non-obtrusive authentication system intended for the detection of impersonations during online tests, developed and tested by the authors, to detect fraudulent acts other than impersonation. The system used keystroke dynamics of the candidates for authentication. This study was particularly aimed at examining the capability of the already tested imposter detection system to detect other fraudulent acts as well. Experiments were conducted using 37 volunteers roleplaying as online test candidates amidst simulated scenarios of fraudulent test taking other than impersonation and their keystroke data were acquired. These data were used to play back the test taking offline, while exercising continuous authentication using the imposter detection system. Quantitative analysis of the resulting data indicates that the imposter detection system can detect the fraudulent acts of look up and collaboration with accuracies of 51.7% and 59.9% respectively, which are dependable but marginally lower than the accuracy of imposter detection. This implies that the system is inherently capable of detecting fraudulent acts other than impersonation, although with lesser accuracy. Plausible reasons for the lower accuracy in detecting other fraudulent acts and ways of improving it are discussed.

Pages

81

Last Page

93

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