Issues of quality assurance in the management of plagiarism in blended learning environments

Increasing access to and availability of electronic resources presents students with a rich library of opportunities for independent study. But students also find themselves in the confusing territory of how they should best use these resources within their assessment activities. Likewise, teaching institutions are faced with the problems of plagiarism and collusion, and the challenges of educating, deterring, detecting, and dealing with breaches of policy in a fair and consistent way across all disciplines. This paper examines issues of quality assurance in the management of plagiarism by discussing the following questions: – How can effective automated plagiarism detection services be introduced and managed across the institution? – What teaching and assessment practices can be adopted to deter plagiarism? – What part should collusion and plagiarism detection tools play in educating and deterring students? – What are appropriate penalties for plagiarism and collusion and how can these be applied consistently across disciplines? Drawing together three distinct strands of research, in both distance and campus based institutions, the authors discuss how practice and policy have evolved in recent years in an attempt to reduce the incidence of plagiarism and collusion. The paper will illustrate this evolution by reporting on recent developments in assessment strategy, detection tools, and policy within two UK HE Institutions: The UK Open University and Manchester Metropolitan University

Issues Related to the Detection of Source Code Plagiarism in Students Assignments

Detecting similarity or plagiarism in the academic research publications, source code, etc. has been a long time complex and time consuming task. Several algorithms, tools and websites exist that try to find plagiarism or possible plagiarism in those human creative products. In this paper we used source code plagiarism detection tools to assess the level of plagiarism in source codes. We also investigated issues related to accuracy and challenges in detecting possible plagiarism in students\u27 assignments. In a second study, we evaluated some tools against detecting possible plagiarism in research papers. Results showed that such process or decision is not binary to make and that subjectivity is high. In addition, there is a need to tune plagiarism detection tools to give criticality or weights by users of those tools to categorize and classify different levels of seriousness for committing plagiarism

Source-code plagiarism : an academic perspective

In computing courses, students are often required to complete tutorial and laboratory exercises asking them to produce source-code. Academics may require students to submit source-code produced as part of such exercises in order to monitor their students’ understanding of the material taught on that module, and submitted source-code may be checked for similarities in order to identify instances of plagiarism. In exercises that require students to work individually, source-code plagiarism can occur between students or students may plagiarise by copying material from a book or from other sources. We have conducted a survey of UK academics who teach programming on computing courses, in order to establish what is understood to constitute source-code plagiarism in an undergraduate context. In this report, we analyse the responses received from 59 academics. This report presents a detailed description of what can constitute source-code plagiarism from the perspective of academics who teach programming on computing courses, based on the responses to the survey

Source-code plagiarism : a UK academic perspective

In computing courses, students are often required to complete tutorial and laboratory exercises asking them to produce source-code. Academics may require students to submit source-code produced as part of such exercises in order to monitor their students' understanding of the material taught on that module, and submitted source-code may be checked for similarities in order to identify instances of plagiarism. In exercises that require students to work individually, source-code plagiarism can occur between students or students may plagiarise by copying material from a book or from other sources. We have conducted a survey of UK academics who teach programming on computing courses, in order to establish what is understood to constitute source-code plagiarism in an undergraduate context. In this report, we analyse the responses received from 59 academics. This report presents a detailed description of what can constitute source-code plagiarism from the perspective of academics who teach programming on computing courses, based on the responses to the survey

Structural analysis of source code plagiarism using graphs

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg in fulfillment of the requirements for the degree of Master of Science. May 2017Plagiarism is a serious problem in academia. It is prevalent in the computing discipline where students are expected to submit source code assignments as part of their assessment; hence, there is every likelihood of copying. Ideally, students can collaborate with each other to perform a programming task, but it is expected that each student submit his/her own solution for the programming task. More so, one might conclude that the interaction would make them learn programming. Unfortunately, that may not always be the case. In undergraduate courses, especially in the computer sciences, if a given class is large, it would be unfeasible for an instructor to manually check each and every assignment for probable plagiarism. Even if the class size were smaller, it is still impractical to inspect every assignment for likely plagiarism because some potentially plagiarised content could still be missed by humans. Therefore, automatically checking the source code programs for likely plagiarism is essential. There have been many proposed methods that attempt to detect source code plagiarism in undergraduate source code assignments but, an ideal system should be able to differentiate actual cases of plagiarism from coincidental similarities that usually occur in source code plagiarism. Some of the existing source code plagiarism detection systems are either not scalable, or performed better when programs are modified with a number of insertions and deletions to obfuscate plagiarism. To address this issue, a graph-based model which considers structural similarities of programs is introduced to address cases of plagiarism in programming assignments. This research study proposes an approach to measuring cases of similarities in programming assignments using an existing plagiarism detection system to find similarities in programs, and a graph-based model to annotate the programs. We describe experiments with data sets of undergraduate Java programs to inspect the programs for plagiarism and evaluate the graph-model with good precision. An evaluation of the graph-based model reveals a high rate of plagiarism in the programs and resilience to many obfuscation techniques, while false detection (coincident similarity) rarely occurred. If this detection method is adopted into use, it will aid an instructor to carry out the detection process conscientiously.MT 201

Plagiarism detection in source programs using structural similarities

The paper presents a plagiarism detection framework the goal of which is to determine whether two programs are similar to each other, and if so, to what extent. The issue of plagiarism detection has been considered earlier for written material, such as student essays. For these, text-based algorithms have been published. We argue that in case of program code comparison, structure based techniques may be much more suitable. The main idea is to transform the source code into mathematical objects, use appropriate reduction and comparison methods on these, and interpret the results appropriately. We have designed a generic program structure comparison framework and implemented it for the Prolog and SML programming languages. We have been using the implementation at BUTE to successfully detect plagiarism in homework assignments for years