Hermetic and Web Plagiarism Detection Systems for Student Essays—An Evaluation of the State-of-the-Art

Abstract

Plagiarism has become a serious problem in education, and several plagiarism detection systems have been developed for dealing with this problem. This study provides an empirical evaluation of eight plagiarism detection systems for student essays. We present a categorical hierarchy of the most common types of plagiarism that are encountered in student texts. Our purpose-built test set contains texts in which instances of several commonly utilized plagiaristic techniques have been embedded. While Sherlock was clearly the overall best hermetic detection system, SafeAssignment performed best in detecting web plagiarism. TurnitIn was found to be the most advanced system for detecting semi-automatic forms of plagiarism such as the substitution of Cyrillic equivalents for certain characters or the insertion of fake whitespaces. The survey indicates that none of the systems are capable of reliably detecting plagiarism from both local and Internet sources while at the same time being able to identify the technical tricks that plagiarizers use to conceal plagiarism.