Intention Involvement in the Nature of Plagiarism

Background: This article addressed one of the issues of research ethics that is called the nature of plagiarism coupled with involvement of intention. By definition, plagiarism is the attribution of others’ works to one’s own. This may be done intentionally and/or unintentionally. Some researchers believe that intention is not involved in the nature of plagiarism and an author who forgets to make references to the used sources has committed plagiarism since this forgetfulness has led to the attribution of others’ work to one’s own. In contrast, some experts call such a person a wrongdoer, not a plagiarist. Conclusion: By likening this problem to the issue of involvement of intention in telling a lie, the author separates two kinds of plagiarism: act-plagiarism and agent-plagiarism. The intention does not involve in the act-plagiarism (to be an act an instance of plagiarism), but it is involved in the agent-plagiarism (to call someone plagiarist). As a result, an author who forgets to make reference is not a plagiarist, but his/her act is an instance of plagiarism. Keywords: Intention, Plagiarism, Intentional Plagiarism, Unintentional Plagiaris

Shape-Based Plagiarism Detection for Flowchart Figures in Texts

Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts results in look holes that people can take advantage. That means people can plagiarized figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper presents a method for detecting flow chart figure plagiarism based on shape-based image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets.Comment: 12 page

The Effectiveness of Low-Level Structure-based Approach Toward Source Code Plagiarism Level Taxonomy

Low-level approach is a novel way to detect source code plagiarism. Such approach is proven to be effective when compared to baseline approach (i.e., an approach which relies on source code token subsequence matching) in controlled environment. We evaluate the effectiveness of state of the art in low-level approach based on Faidhi \& Robinson's plagiarism level taxonomy; real plagiarism cases are employed as dataset in this work. Our evaluation shows that state of the art in low-level approach is effective to handle most plagiarism attacks. Further, it also outperforms its predecessor and baseline approach in most plagiarism levels.Comment: The 6th International Conference on Information and Communication Technolog

Plagiarism On The Internet

This article addresses the problem of plagiarism on the Internet and offers practical guidelines and instructions for dealing with that problem. While technology-based tools such as plagiarism detection services are discussed, primary focus is given to stopping plagiarism before it occurs. Prevention ahead of time is considered far better than detection later in time. Good prevention techniques involve educating the faculty in terms of how they can better plagiarism-proof\u27 their assignments, and encouraging the administration to create academic plagiarism policies, academic integrity codes, and Christian academic policy statements. This article developed from a workshop presentation made at the 2002 Association of Academic Christian Librarians\u27 Annual Conference

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

Measuring plagiarism: Researching what students do, not what they say they do

Student plagiarism in colleges and universities has become a controversial issue in recent years. A key problem has been the lack of reliable empirical data on the frequency, nature and extent of plagiarism in student assignments. The aim of the study described here was to provide this data. Patterns of plagiarism were tracked in two university business studies assignments involving over 500 students and over 1000 scripts. Turnitin software was used to facilitate the identification of plagiarised material in assignments. The findings confirmed some common assertions about the nature of student plagiarism but did not provide support for a number of others

Experiments to investigate the utility of nearest neighbour metrics based on linguistically informed features for detecting textual plagiarism

Plagiarism detection is a challenge for linguistic models — most current implemented models use simple occurrence statistics for linguistic items. In this paper we report two experiments related to plagiarism detection where we use a model for distributional semantics and of sentence stylistics to compare sentence by sentence the likelihood of a text being partly plagiarised. The result of the comparison are displayed for visual inspection by a plagiarism assessor

The System Kato: Detecting Cases of Plagiarism for Answer-Set Programs

Plagiarism detection is a growing need among educational institutions and solutions for different purposes exist. An important field in this direction is detecting cases of source-code plagiarism. In this paper, we present the tool Kato for supporting the detection of this kind of plagiarism in the area of answer-set programming (ASP). Currently, the tool is implemented for DLV programs but it is designed to handle other logic-programming dialects as well. We review the basic features of Kato, introduce its theoretical underpinnings, and discuss an application of Kato for plagiarism detection in the context of courses on logic programming at the Vienna University of Technology