Improving Plagiarism Detection in Coding Assignments by Dynamic Removal of Common Ground

Plagiarism in online learning environments has a detrimental effect on the trust of online courses and their viability. Automatic plagiarism detection systems do exist yet the specific situation in online courses restricts their use. To allow for easy automated grading, online assignments usually are less open and instead require students to fill in small gaps. Therefore solutions tend to be very similar, yet are then not necessarily plagiarized. In this paper we propose a new approach to detect code re-use that increases the prediction accuracy by dynamically removing parts in assignments which are part of almost every assignment—the so called common ground. Our approach shows significantly better F-measure and Cohen's Kappa results than other state of the art algorithms such as Moss or JPlag. The proposed method is also language agnostic to the point that training and test data sets can be taken from different programming languages.