Scaling anti‐plagiarism efforts to meet the needs of large online computer science classes: Challenges, solutions, and recommendations

Abstract

Background: Plagiarism is a very serious offence in academic institutions. Yet there is some reluctance to address plagiarism by educators as its enforcement can require a significant time commitment if not handled wisely. Handling plagiarism at scale has the potential to exacerbate this problem.Objectives: This article explores the challenges educators face when it comes to enforcing plagiarism from within the course environment, presents a solution that addresses these challenges, and provides guidelines for preventing and detecting plagiarism in our at-scale courses.Methods: A workflow is shared which was created from our experience of handling plagiarism within our large online computer science graduate level program. We present an empirical study of this workflow that shows the overall prevalence of misconduct. Results and Conclusions:Results demonstrate this workflow being effectively and efficiently applied to address plagiarism across four courses and three semesters by a single individual working half time. We have observed that warnings on low-stakes assignments can be very effective at deterring future misconduct. We have also observed that cases come to a quicker resolution when students readily acknowledge their own misconduct after being promptly notified.Takeaways from the study: Plagiarism should not only be managed through institution wide policies, but also through effective strategies implemented in the course environment. This is particularly important when handling plagiarism at scale. Addressing plagiarism starts in the course and therefore effective strategies for handling it there are needed.

Trustworthy remote assessments: A typology of pedagogical and technological strategies

“…Much of the attention of the existing literature has been focused on cheating during exams, rather than examining plagiarism in performance‐based assessments. In order to understand plagiarism in programming assignments, Adkins and Joyner (2022) explore code plagiarism in an international computer science degree with 12,000 active students. Within this context, Adkins and Joyner (2022) unpack different conflicts that affect students' understanding of plagiarism and its negative academic consequences.…”

“…In order to understand plagiarism in programming assignments, Adkins and Joyner (2022) explore code plagiarism in an international computer science degree with 12,000 active students. Within this context, Adkins and Joyner (2022) unpack different conflicts that affect students' understanding of plagiarism and its negative academic consequences. First, code copying in a professional context is significantly less stigmatized than in academic contexts because it is common to reuse segments of code in professional software development.…”

“…In the context of performance‐based assessments, particularly programming assignments, Adkins and Joyner (2022) also suggest acting once an instance of code plagiarism is identified, approaching students to explain the consequences for cheating, plagiarism, unauthorized collaboration, and other forms of academic dishonesty. To ensure consistency and transparency in academic integrity, Adkins and Joyner (2022) also suggest establishing formal rules and objective criteria for identifying and intervening in cases of misconduct wherever possible. And to avoid bias concerning case detection, they suggest removing identifying information to not affect instructors' interpretation until a clear determination of the dishonest act.…”

“…the instructors started applying strategies to discourage academically dishonest behaviours of their students throughout the use of the dashboard, such as giving different exam versions to students who submitted similar work. Therefore, Approach number 7 suggests using an instructor-facing dashboard to visualize the similarity in the students' responses to their exams and the closeness in the timestamp submissions, aiming to act upon dishonest behaviours promptly.In the context of performance-based assessments, particularly programming assignments,Adkins and Joyner (2022) also suggest acting once an instance of code plagiarism is identified, approaching students to explain the consequences for cheating, plagiarism, unauthorized collaboration, and other forms of academic dishonesty.To ensure consistency and transparency in academic integrity, Adkins and Joyner (2022) also suggest establishing formal rules and objective criteria for identifying and intervening in cases of misconduct wherever possible. And to avoid bias concerning case detection, they suggest removing identifying information to not affect instructors' interpretation until a clear determination of the dishonest act.…”

Simulating Similarities to Maintain Academic Integrity in Programming

“…To help instructors identifying plagiarism and collusion cases, an automated similarity detector can be employed. Adkins and Joyner (2022) presented a comprehensive workflow about the use, especially to deal with large classes. Programming similarity detectors are similar to those of text except that they focus more on comparing student submissions to one another rather than comparing those to sources from the internet (Foltỳnek et al, 2019;Lee et al, 2023).…”