CATCHING PLAGIARISTS: DETECTING PLAGIARISM IN STUDENT SOURCE CODE ASSIGNMENTS IN A VIRTUAL LEARNING ENVIRONMENT
The widespread problem of plagiarism in academic environment is as old as academia itself. The spate of plagiarism has sparked lots of controversies in academics and it has received renewed interest as a result of advancement in technology, which has made the cheating process effortless to explore.
In undergraduate courses, especially in the computing discipline, students are expected to submit source codes which are graded for originality and correctness, which also contribute to a final grade of a programming course. Yet, students engage in different forms of cheating and devise several means to deceive their instructors. Student engagement in performing programming assignments is allowed but, it is expected that each and every source code must be solely produced by a student. However, in a real context, the reverse seems to be the case.
Several algorithms and tools to detect suspicious similarities in source code have been developed; different techniques have been revamped for this purpose. Although, there is still a growing concern of reporting true cases of cheating in a virtual learning environment.
This paper describes the development of a plagiarism detection plugin for MOSS (Moodle Anti-plagiarism engine); adapted with a graph-based approach in exposing suspicious cases of plagiarism in a virtual learning environment.