Treffer: A detection process to create awareness of source-code plagiarism among students using it to pass introductory programming.

The COVID-19 pandemic restrained the academic environment and changed the rules of the educational game; contact classes were restricted, and online assessments had to be introduced. This situation opened an opportunity for some students to use source-code plagiarism to pass coding assessments in the Introduction to Programming subject module. The focus of this paper is on making sense of this environment to establish a process to ensure that students obtain the skills they need to build on in subsequent modules. This is necessary to reach the outcomes of a computing course. Four aspects were used in establishing this source-code plagiarism awareness process in focusing on one class of students. Qualitative data were gathered by firstly requesting the class to supply feedback on their understanding of source-code plagiarism, and secondly inviting students identified as guilty of Python source-code plagiarism to start a conversation with the lecturer, which was triangulated with quantitative data regarding the success of the latter group in terms of their pass rate. Although the Measure of Software Similarity tool was instrumental in establishing a source-code plagiarism detection process, it is cumbersome and time consuming. Hence fourthly, it was compared to other available tools to determine their suitability in comparison. A refined source-code plagiarism awareness process is the resultant finding of this paper. [ABSTRACT FROM AUTHOR]

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