Automating Source Code Plagiarism Detection in a Moodle-Based Programming Course

• Main Authors: Aniceto, Rodrigo C, Holanda, Maristela, Da Silva, Dilma
• Format: Conference Proceeding
• Language: English
• Subjects: Behavioral sciences; Correlation; Data mining; Learning management systems; Moodle; Plagiarism; plagiarism detection; programming; Programming profession; source code; Source coding
• ISSN: 2377-634X
• DOI: 10.1109/FIE58773.2023.10342895

Plagiarism in programming courses in college is an issue. Automatic plagiarism detection tools using source code similarities are important to combat this issue. For example, Moss and JPlag tools are used worldwide by several universities for the detection of similarity in students' source codes. However, the similarity could cause lead to notification of false positives. Given the large number of students in programming courses, considering only similarity could increase the number of students that the instructors should investigate to decide whether there is plagiarism. In this context, this paper presents the ProjPlag tool that seeks to combine similarity detection results with student behavior data, such as the coding process in the learning management system (LMS) and assignment scores during the programming courses. The ProjPlag automates the utilization of Jplag and Moss tools and also analyses students' behavior by means of data extracted from the Moodle platform, the LMS used in the programming courses at the Department of Computer Science at the University of Brasilia. This paper presents an analysis of correlations between the list of confirmed plagiarized assignments with the student behavior data on the Moodle platform, such as time to implement an assignment, the remaining time from the deadline, and the assignment's score. ProjPlag was tested in two different academic semesters at the University of Brasilia in 2022 for the first programming course, (Algorithms and Computer Programming course) in the Department of Computer Science. The findings show that the detection tools can help the instructor to ascertain whether there was plagiarism or not. However, the majority of student behavior data from Moodle had limited relevance in confirming plagiarism and can generate a false alarm. The correlations between confirmed plagiarism and student's behavior (plagiarism and assignments scores, time spent coding, and project scores) on the Moodle platform were low.