Educating Students about Programming Plagiarism and Collusion via Formative Feedback

Abstract

To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This paper presents an assessment submission system with automated, personalized, and timely formative feedback that can be used in institutions that apply some leniency in early instances of plagiarism and collusion. If a student’s submission shares coincidental or non-coincidental similarity with other submissions, personalized similarity reports are generated for the involved submissions and the students are expected to explain the similarity and resubmit the work. Otherwise, a report simulating similarities is sent just to the author of the submitted program to enhance their knowledge. Results from two quasi-experiments involving two academic semesters suggest that students with our approach are more aware of programming plagiarism and collusion, including the futility of some program disguises. Further, their submitted programs have lower similarity even at the level of program flow, suggesting that they are less likely to have engaged in programming plagiarism and collusion. Student behavior while using the system is also analyzed based on the statistics of the generated reports and student justifications for the reported similarities.

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments

“…This is necessary for recent online classes, different from traditional classrooms, as the possibility of cheating increases without the face-to-face monitoring of instructors [ 4 , 5 , 6 , 7 ]. Previous studies on such topics (i.e., detection of cheating on programming assignments) have mostly focused on detecting plagiarism in the students’ source code [ 12 , 13 , 14 , 18 , 19 ]. This is because the common approach to detect plagiarism in programming assignments is to compare the submitted assignments of students in the form of a hard copy in traditional classrooms.…”

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

“…This can involve cheating on exams or assignments, copying work from others, falsifying research data or results, buying or obtaining unauthorized access to assignments or exams, collaborating with others on individual assignments or passing off one’s own work as original. Such behavior goes against academic standards and can result in severe consequences such as assignment or course failure or suspension or expulsion from the academic institution (Karnalim et al , 2022).…”

“…Academic dishonesty is a violation of academic integrity that includes cheating, plagiarism or fabrication in an educational setting (Karnalim et al, 2022;Kelly et al, 2022;Smith et al, 2022;Wardani and Putri, 2023). This can involve cheating on exams or assignments, copying work from others, falsifying research data or results, buying or obtaining unauthorized access to assignments or exams, collaborating with others on individual assignments or passing off one's own work as original.…”

“…As an AI language model developed by OpenAI, ChatGPT should not be used for academic dishonesty or cheating (Finnie-Ansley et al , 2022; Kim et al , 2022). The use of AI language models for cheating on assignments is a violation of academic integrity and could lead to serious consequences for students (Karnalim et al , 2022). It is crucial for students to comprehend the significance of honest and original work in their academic pursuits and to engage in their studies with integrity (Karnalim et al , 2022).…”

“…The use of AI language models for cheating on assignments is a violation of academic integrity and could lead to serious consequences for students (Karnalim et al , 2022). It is crucial for students to comprehend the significance of honest and original work in their academic pursuits and to engage in their studies with integrity (Karnalim et al , 2022). Therefore, this paper aims to investigate the root of students’ behavior of using ChatGPT to cheat in their assignments.…”