Collaboration Versus Cheating: Reducing Code Plagiarism in an Online MS Computer Science Program

By Tony Mason, Ada Gavrilovska, David A. Joyner. 2018

37 citations recorded by the source corpus.

We outline how we detected programming plagiarism in an introductory online course for a master's of science in computer science program, how we achieved a statistically significant reduction in programming plagiarism by combining a clear explanation of university and class policy on academic honesty reinforced with a short but formal assessment, and how we evaluated plagiarism rates before and after implementing our policy and assessment.

Subfields: Health Informatics, Safety Research, AI Ethics, Fairness & Society, Software Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

1. Percy Liang
2. Dan Jurafsky
3. Tatsunori B. Hashimoto

In AI Ethics, Fairness & Society

1. Percy Liang
2. Dan Jurafsky
3. Mark A. Lemley
4. Tatsunori B. Hashimoto
5. Xuechen Li
6. Peter Henderson

Citations in the corpus

• Foundation Models and Fair Use (2023, positive): “The 20% threshold is chosen inspired by common values used in plagiarism detection for flagging submissions for further manual inspection (Mason et al., 2019).”