Presenting an alternative source code plagiarism detection framework for improving the teaching and learning of programming

Abstract The transfer and teaching of programming and programming related skills has become, increasingly difficult on an undergraduate level over the past years. This is partially due to the number of programming languages available as well as access to readily available source code over the Web. Source code plagiarism is common practice amongst many undergraduate students. This practice has a detrimental effect on the presentation of specific content relating to introduction to programming courses. One of the problems identified in the research conducted is that turnaround time with relation to assessment and feedback, which are presented to the students, is a critical factor in the subsequent success rates of the subject. This paper investigates, utilizing a literature review, how plagiarism detection metrics and a framework for providing effective feedback to students and educators could be implemented to enhance the teaching and learning processes. The predominant technique used for detecting plagiarism is to evaluate how a piece of source code was constructed over time. By analyzing the students’ programming patterns, lectures can be adapted to address problem areas and react accordingly. The paper also provides an overview of current metrics used for plagiarism detection and suggests ways of improving the process by including enhanced techniques for the gathering of metrics over time as well as suggesting ways to use the metrics to aid learning on all cognitive levels. Some of the key considerations presented as part of this research include effective feedback mechanisms and real-time responses to plagiarism as well as contributing towards learning on different cognitive levels