Proceedings ArticleDOI
Towards an error free plagarism detection process
Thomas Lancaster,Fintan Culwin +1 more
- Vol. 33, Iss: 3, pp 57-60
TLDR
A Four-Stage Plagiarism Detection Process that attempts to ensure no suspicious similarity is missed and that no student is unfairly accused of plagiarism is described.Abstract:
For decades many computing departments have deployed systems for the detection of plagiarised student source code submissions. Automated systems to detect free-text student plagiarism are just becoming available and the experience of computing educators is valuable for their successful deployment.This paper describes a Four-Stage Plagiarism Detection Process that attempts to ensure no suspicious similarity is missed and that no student is unfairly accused of plagiarism. Required characteristics of an effective similarity detection engine are proposed and an investigation of a simple engine is described. An innovative prototype tool designed to decrease the workload of tutors investigating undue similarity is also presented.read more
Citations
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Proceedings ArticleDOI
Methods and tools for exploring novice compilation behaviour
TL;DR: Over the course of two years, first-year university students learning to program in Java are observed, collecting and studying thousands of snapshots of their programs from one compilation to the next.
Dissertation
An exploration of novice compilation behaviour in BlueJ
TL;DR: This work has proposed a quantification of novice compilation behavior which is called the error quotient, which provides a powerful indicator for how much or little a student is struggling with the language while programming, and correlates significantly with traditional indicators for academic progress.
Journal ArticleDOI
Patterns of plagiarism
Charles Daly,Jane M. Horgan +1 more
TL;DR: A new technique was used to analyse how students plagiarise programs in an introductory programming course, placing a watermark on a student's program and monitoring programs for the watermark during assignment submission, and it emerged that the recipient students performed significantly worse than the suppliers.
Journal ArticleDOI
Retrieving similar documents from the web
Álvaro Pereira,Nivio Ziviani +1 more
TL;DR: A mechanism for detecting and retrieving documents from the web with a similarity relation to a suspicious document and a comparison of each candidate document and the suspicious document using Shingles and Patricia tree.
Proceedings ArticleDOI
Strategies for promoting academic integrity in CS courses
TL;DR: In this article, the authors describe the approach taken at Stanford University over the past ten years in an attempt to control plagiarism in computer science courses, which consists of five steps: encouraging computer science faculty to become actively engaged in the university judicial process.
References
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Journal ArticleDOI
Undergraduate Cheating: Who Does What and Why?.
TL;DR: In this article, the authors report data from a series of studies across different academic disciplines and different institutions and report that cheating behaviours such as copying each other's work, plagiarism, altering and inventing research data were admitted to by more than 60% of the students.
Journal ArticleDOI
An algorithmic approach to the detection and prevention of plagiarism
TL;DR: This paper discuses one possible quantification which works well when applied to student computer pro grams and shows how this problem can be reduced by quantifyin g papers in such a way that equivalent papers are given equal values.
A review of electronic services for plagiarism detection in student submissions
Fintan Culwin,Thomas Lancaster +1 more
TL;DR: The need for widespread plagiarism detection systems is reviewed and four services are discussed: the Measure of Software Similarity (MOSS) service for program source code and the plagiarism.org, Integriguard and copycatch.com services for free-text submissions.
Proceedings ArticleDOI
Visualising intra-corpal plagiarism
Fintan Culwin,Thomas Lancaster +1 more
TL;DR: VAST improves on the human-eye approach by identifying similarities which a tutor might otherwise miss, thus saving investigative time, and is demonstrated using noise-free synthetic texts and actual student submissions containing intra-corpal plagiarism.