Plagiarism Detection

Plagiarism is regarded as a heinous crime within the academic community, but anecdotal evidence suggests that some writers plagiarize without intending to transgress academic conventions. This article reports a study of the writing of 17 postgraduate students. Source reports in the student-generated texts were compared to the original sources in order to describe the relationship between the two. Interviews were also conducted with the student writers and their supervisors. The student writing was found to contain textual features which could be described as plagiarism, but the writers’ accounts of their work and the textual analysis strongly suggest absence of intention to plagiarize, thus providing empirical verification of similar suggestions in the literature. Implications of these findings are discussed and include a recommendation that the focus on preventing plagiarism be shifted from post facto punishment to proactive teaching.

A fundamental question in information theory and in computer science is how to measure similarity or the amount of shared information between two sequences. We have proposed a metric, based on Kolmogorov complexity to answer this question, and have proven it to be universal. We apply this metric in measuring the amount of shared information between two computer programs, to enable plagiarism detection. We have designed and implemented a practical system SID (Software Integrity Diagnosis system) that approximates this metric by a heuristic compression algorithm. Experimental results demonstrate that SID has clear advantages over other plagiarism detection systems. SID system server is online at

Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on ''exact'' translations but does not generalize well.

The ways in which universities and individual academics attempt to deter and respond to student plagiarism may be based on untested assumptions about particular or primary reasons for this behaviour. Using a series of group interviews, this qualitative study gathered the views of 56 Australian university students on the possible reasons for plagiarism within their institution. The results indicate a wide and disparate range of possible contributing reasons for plagiarism including: institutional admission criteria; student understanding of plagiarism; poor academic skills; a range of teaching and learning factors; personality factors and external pressures. These findings are compared with other findings about reasons for student plagiarism in Australasia. The implications of these findings are considered for universities and individual academics seeking to better engage with their students to minimise or marginalise plagiarism.

This paper will address the question of the morality of technology. I believe this is an important question for our contemporary society in which technology, especially information technology, is increasingly becoming the default mode of social ordering. I want to suggest that the conventional manner of conceptualising the morality of technology is inadequate -even dangerous. The conventional view of technology is that technology represents technical means to achieve social ends. Thus, the moral problem of technology, from this perspective, is the way in which the given technical means are applied to particular (good or bad) social ends. In opposition to this I want to suggest that the assumed separation, of this approach, between technical means and social ends are inappropriate. It only serves to hide the most important political and ethical dimensions of technology. I want to suggest that the morality of technology is much more embedded and implicit than such a view would suggest. In order to critique this approach I will draw on phenomenology and the more recent work of Bruno Latour. With these intellectual resources in mind I will propose disclosive ethics as a way to make the morality of technology visible. I will give a brief account of this approach and show how it might guide our understanding of the ethics and politics of technology by considering two examples of contemporary information technology: search engines and plagiarism detection systems.

In the context of an increasingly mobile student population, and Greek students specifically, this paper opens up and reveals the manner in which a specific culturally situated human actor (the Greek student) and a specific culturally situated non-human actor (the plagiarism detection system) encounter, interpret and constitute each other within the situated context of the UK higher education system. Methodologically, we base our paper on a longitudinal in-depth case study that focussed on the teaching, learning and assessment practices in Greek public sector universities. Based on our Greek case example we specifically focus on how the delegation of plagiarism detection to a technical actor produces a particular set of agencies and intentionalities (a politics one might say) which unintentionally and unexpectedly conspires to constitute some students as plagiarists (who are not) and others as not (who are). We suggest that this is best explored by looking exactly at what is rendered visible and invisible in such imbrications. This has important implications for the design, implementation and use of IS in situated contexts.

Countless cases of plagiarism are detected across the Australian higher education sector each year. Generally speaking, policy and other responses to the issue focus on punitive, rather than on educative, measures. Recently, a subtle shift is discernable. As well as ensuring appropriate consequences for plagiarists, several universities are beginning to formalise the inclusion of learning and teaching strategies in anti-plagiarism related policy and practice, as well as paying closer attention to the communication of unambiguous definitions of plagiarism. This article outlines one example of the emerging educative approach and details the ways in which this approach has been implemented across an entire university. The necessity of evidence-based evaluation of approaches to reducing plagiarism in higher education is discussed.

Motivation: Duplicate publication impacts the quality of the scientific corpus, has been difficult to detect, and studies this far have been limited in scope and size .Using text similarity searches, we were able to identify signatures of duplicate citations among a body of abstracts. Results: A sample of 62,213 Medline citations was examined and a database of manually verified duplicate citations was created to study author publication behavior. We found that 0.04% of the citations with no shared authors were highly similar and are thus potential cases of plagiarism. 1.35% with shared authors were sufficiently similar to be considered a duplicate. Extrapolating, this would correspond to 3,500 and 117,500 duplicate citations in total, respectively. Availability: eTBLAST, an automated citation matching tool, and Déjà vu, the duplicate citation database, are freely available at

... Full-text electronic research document corpuses have grown substantially over the past decade, and permit a ... to discover X and Y: they had copied sufficiently many pages, even from singlesources, that they ... We thank Patrick Ng for his con-tributions to the data cleaning code. ...

Existing literature provides insight into the nature and extent of plagiarism amongst undergraduate students (e.g., Parameswaran & Devi, 2006;. Plagiarism amongst graduate students is relatively unstudied, however, and the existing data are largely based on self-reports. This study investigated the rates and potential causes of plagiarism amongst graduate students in master's and doctoral programmes in science, technology, engineering, mathematics, and science or mathematics education by examining actual research proposals written by graduate students. Results indicate that plagiarism is a prevalent issue at each of the three university sites sampled and across all of the investigated disciplines. Fine grained analyses suggest that this plagiarism may be largely unintentional and due to a lack of disciplinary enculturation. Specifically, participants that plagiarised had approximately one less semester of research experience than graduate students who did not plagiarise. Furthermore, participants who lacked primary literature in their research proposals were significantly more likely to plagiarise and often used inappropriate citation styles. Follow-up correspondence with participants indicates that participants plagiarised, in part, because they lacked an awareness of the role of primary literature in the research process. This suggests that explicit training in the role and use of primary literature may provide an opportunity for programmes or mentors to accelerate the development of graduate students' research skills. This study also revealed that plagiarism was more common amongst English as a Second Language (ESL) participants. Potential causes of plagiarism and solutions to address plagiarism among the ESL population will be discussed.

Laboratory work assignments are very important for computer science learning. Over the last 12 years many students have been involved in solving such assignments in the authors' department, having reached a figure of more than 400 students doing the same assignment in the same year. This number of students has required teachers to pay special attention to conceivable plagiarism cases. A plagiarism detection tool has been developed as part of a full toolset for helping in the management of the laboratory work assignments. This tool defines and uses four similarity criteria to measure how similar two assignment implementations are. The paper describes the plagiarism detection tool and the experience of using it over the last 12 years in four different programming assignments, from microprogramming a CPU to system programming in C.

Plagiarism is increasingly evident in business and academia. While links between demographic, personality, and situational factors have been found, previous research has not used actual plagiarism behavior as a criterion variable. Previous research on academic dishonesty has consistently used to self-report measures to establish prevalence of dishonest behavior. In this chapter the author covers the results of four studies that used plagiarism behavior to establish its prevalence, as well as relationships between plagiarism behavior and: 1. Integrity-related personal selection measures 2. Workplace deviance 3. Ethnicity 4. Acculturation 5. Individualism 6. Collectivism 7. Religiosity 8. Spirituality.

Plagiarism detection can be divided in external and intrinsic methods. Naive external plagiarism analysis suers from computationally demanding full near- est neighbor searches within a reference corpus. We present a conceptually simple space partitioning approach to achieve search times sub linear in the number of ref- erence documents, trading precision for speed. We focus on full duplicate searches while achieving

Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased, translated or idea plagiarism. The authors of this paper demonstrated in recent studies that the detection rate can be significantly improved by not only relying on text analysis, but by additionally analyzing the citations of a document. Citations are valuable language independent markers that are similar to a fingerprint. In fact, our examinations of real world cases have shown that the order of citations in a document often remains similar even if the text has been strongly paraphrased or translated in order to disguise plagiarism. This paper introduces three algorithms and discusses their suitability for the purpose of Citation-based Plagiarism Detection. Due to the numerous ways in which plagiarism can occur, these algorithms need to be versatile. They must be capable of detecting transpositions, scaling and combinations in a local and global form. The algorithms are coined Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence. The evaluation showed that common forms of plagiarism can be detected reliably if these algorithms are combined.

Texts and their translations are a rich linguistic resource that can be used to train and test statistics-based Machine Translation systems and many other applications. In this paper, we present a working system that can identify translations and other very similar documents among a large number of candidates, by representing the document contents with a vector of thesaurus terms from a multilingual thesaurus, and by then measuring the semantic similarity between the vectors. Tests on different text types have shown that the system can detect translations with over 96% precision in a large search space of 820 documents or more. The system was tuned to ignore languagespecific similarities and to give similar documents in a second language the same similarity score as equivalent documents in the same language. The application can also be used to detect cross-lingual document plagiarism.

Plagiarism of material from the Internet is a widespread and growing problem. Computer science students, and those in other science and engineering courses, can sometimes get away with a" cut and paste" approach to assembling a paper in part ...

Empirical studies have revealed a disturbing prevalence of research misconduct in a wide variety of disciplines, although not, to date, in the areas of ethics and philosophy. This study aims to provide empirical evidence on perceptions of how serious a problem research misconduct is in these two disciplines in Spain, particularly regarding the effects that the model used to evaluate academics' research performance may have on their ethical behaviour. The methodological triangulation applied in the study combines a questionnaire, a debate at the annual meeting of scientific association, and in-depth interviews. Of the 541 questionnaires sent out, 201 responses were obtained (37.1% of the total sample), with a significant difference in the participation of researchers in philosophy (30.5%) and in ethics (52.8%); 26 researchers took part in the debate and 14 interviews were conducted. The questionnaire results reveal that 91.5% of the respondents considered research misconduct to be on the rise; 63.2% considered at least three of the fraudulent practices referred to in the study to be commonplace, and 84.1% identified two or more such practices. The researchers perceived a high prevalence of duplicate publication (66.5%) and self-plagiarism (59.0%), use of personal influence (57.5%) and citation manipulation (44.0%), in contrast to a low perceived incidence of data falsification or fabrication (10.0%). The debate and the interviews corroborated these data. Researchers associated the spread of these misconducts with the research evaluation model applied in Spain.

This paper overviews 15 plagiarism detectors that have been evaluated within the fourth international competition on plagiarism detection at PAN'12. We report on their performances for two sub-tasks of external plagiarism detection: candidate document retrieval and detailed document comparison. Furthermore, we introduce the PAN plagiarism corpus 2012, the TIRA experimentation platform, and the ChatNoir search engine for the ClueWeb. They add scale and realism to the evaluation as well as new means of measuring performance.

Efficient detection of plagiarism in programming assignments of students is of a great importance to the educational procedure. This paper presents a clustering oriented approach for facing the problem of source code plagiarism. The implemented software, called PDetect, accepts as input a set of program sources and extracts subsets (the clusters of plagiarism) such that each program within a particular subset has been derived from the same original. PDetect proposes the use of an appropriate measure for evaluating plagiarism detection performance and supports the idea of combining different plagiarism detection schemes. Furthermore, a cluster analysis is performed in order to provide information beneficial to the plagiarism detection process. PDetect is designed such that it may be easily adapted over any keyword-based programming language and it is quite beneficial when compared with earlier (state-of-the-art) plagiarism detection approaches.

In this work, we determined, the level of incidence of the use of technologies on academic success and the incidence of interaction and experience on the level of plagiarism of university students. A sample of 10,952 students from 31 face-to-face universities in Ecuador was created. Students were classified based on their experience level, level of interaction with teachers and classmates, and the use they do with technology for academic activities. The results showed that the level of experience does not affect academic success, but does have an incidence on plagiarism levels that increase as this experience increases. Plagiarism reaches higher levels when level of experience, family income and hours of connection per day increases. Academic performance depends on the number of hours that students seek information and the number of academic videos they watch. Also, plagiarism tends to decrease as the student makes better use of technology for their academic activities.

Despite the importance of Supreme Court opinions for the American polity, scholars have dedicated little systematic research to investigating the factors that contribute to the content of the Court's opinions. In this paper, we examine the ability of lower federal courts to shape the content of Supreme Court opinions. We argue that lower court opinions will influence the content of the Court's opinions based on a number of factors, including the prestige of the lower court opinion author, the published or unpublished nature of the lower court opinion, the ideological compatibility of the lower court opinion vis-à-vis the Supreme Court's decision, the type of lower court opinion, and the lower court from which the opinion emanated. Utilizing plagiarism detection software to compare lower federal court opinions with the majority opinions of the Supreme Court during the 2002-2004 terms, we uncover support for our hypotheses, indicating that the Supreme Court systematically incorporates language from the lower federal courts into its majority opinions.

Easy access to the Web has led to increased potential for students cheating on assignments by plagiarising others' work. By the same token, Web-based tools offer the potential for instructors to check submitted assignments for signs of plagiarism. Overlap-detection tools are easy to use and accurate in plagiarism detection, so they can be an excellent deterrent to plagiarism. Documents can overlap for other reasons, too: Old documents are superseded, and authors summarize previous work identically in several papers. Overlap-detection tools can pinpoint interconnections in a corpus of documents and could be used in search engines.

Publication ethics principles became one of the main aspects of conducting scientific research and presenting its results. Publication ethics challenges cover a wide range of problems of varying importance that involve all participants of publication processes: authors, academic authorities, peer-reviewers, editorial board members, publishers, and funders. All stakeholders put efforts to make modern science and publication processes ethical. This goal is achieved first of all through detailed criteria of publication ethics and extensive author guidelines, as well as by increasing the level of awareness of these criteria in educational programs aimed at prophylactics of research misconduct. However, there is a need for technical facilities for detecting different cases of violation of ethical principles, and bibliometric methods are one of the most promising approaches. The paper summarizes the authors’ recent studies on bibliometric perspectives for detecting plagiarism, inappropriate authorship, and official misconduct among editorial board members.

Back in 2004, Google Inc. (Menlo Park, CA, USA) began digitizing full texts of magazines, journals, and books dating back centuries. At present, over 25 million books have been scanned and anyone can use the service (currently called Google Books) to search for materials free of charge (including academics of any discipline). All the books have been scanned, converted to text using optical character recognition and stored in its digital database. The present paper describes a very precise six-stage Boolean date-specific research method on Google, referred to as Internet Date Detection (IDD) for short. IDD can be used to examine countless alleged facts and myths in a systematic and verifiable way. Six examples of the IDD method in action are provided (the terms, words, and names 'self-fulfilling prophecy', 'Humpty Dumpty', 'living fossil', 'moral panic', 'boredom', and 'selfish gene') and each of these examples is shown to disconfirm widely accepted expert knowledge belief claims about their history of coinage, conception, and published origin. The paper also notes that Google's autonomous deep learning AI program RankBrain has possibly caused the IDD method to no longer work so well, addresses how it might be recovered, and how such problems might be avoided in the future.

The purpose of this study was to evaluate the effectiveness of plagiarism detection software and penalty for plagiarizing in detecting and deterring plagiarism among medical students. The study was a continuation of previously published research in which second-year medicals students from 2001/2002 and 2002/2003 school years were required to write an essay based on one of the four scientific articles offered by the instructor. Students from 2004/2005 (N = 92) included in present study were given the same task. Topics of two of the four articles were considered less complex, and two were more complex. One less and one more complex articles were available only as hardcopies, whereas the other two were available in electronic format. The students from 2001/2002 (N = 111) were only told to write an original essay, whereas the students from 2002/2003 (N = 87) were additionally warned against plagiarism, explained what plagiarism was, and how to avoid it. The students from 2004/2005 were warned that their essays would be examined by plagiarism detection software and that those who had plagiarized would be penalized. Students from 2004/2005 plagiarized significantly less of their essays than students from the previous two groups (2% vs. 17% vs. 21%, respectively, P \ 0.001). Over time, students more frequently choose articles with more complex subjects (P \ 0.001) and articles in electronic format (P \ 0.001) as a source for their essays, but it did not influence the rate of plagiarism. Use of plagiarism detection software in evaluation of essays and consequent penalties had effectively deterred students from plagiarizing.

Easy access to the World Wide Web has raised concerns about copyright issues and plagiarism. It is easy to copy someone else's work and submit it as someone's own. This problem has been targeted by many systems, which use very similar approaches. These approaches are compared in this paper and suggestions are made when different strategies are more applicable than others. Some alternative approaches are proposed that perform better than previously presented methods. These previous methods share two common stages: chunking of documents and selection of representative chunks. We study both stages and also propose alternatives that are better in terms of accuracy and space requirement. The applications of these methods are not limited to plagiarism detection but may target other copy-detection problems. We also propose a third stage to be applied in the comparison that uses suffix trees and suffix vectors to identify the overlapping chunks.

The paper presents the implementation of a tool for plagiarism detection developed within the AXMEDIS project. The algorithm leverages the plagiarist behaviour, which is modeled as a combination of 3 basical actions: insertion, deletion, substitution. We recognize that this behaviour may occur at various level of the document structure: the plagiarist may insert, delete or substitute a word, period or a paragraph. The procedure consists in two main steps: document structure extraction and plagiarism function calculation. We propose a recursive plagiarism evaluation function to be evaluated at each level of the document structure which is based on the Levenshtein edit distance. We also propose a method that will eliminate unnecessary chunks comparison, avoiding similarity calculation of chunks which do not share enough 4-grams. We describe the similarity algorithm and discuss some implementation issues and future work.

Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting, style analysis etc. In this paper a new approach called Citation-based Plagiarism Detection is presented. It is suitable for identifying similar and plagiarized documents based on the citations used in the text. In contrast to text based procedures, the presented approach performs significantly better in identifying strong paraphrases, translated plagiarism and some cases of idea plagiarism. It is shown that detection rates can be improved by combining citation-based with text-based plagiarism detection. It is also shown that a large scale crowd-sourcing investigation as done in project GuttenPlag, which combines all other methods, delivered by far the best detection results.

These are published by MARCET Staff Development Resource Centre on behalf of the University. Papers in this series will report research-based and evidencebased approaches to learning, teaching assessment and learning supported in HIGHER EDUCATION. The SERIES Editorial Board includes members of the Northumbria RECAP Research Network.

This paper argues that the inappropriate framing and implementation of plagiarism detection systems in UK universities can unwittingly construct international students as Fplagiarists_. It argues that these systems are often implemented with inappropriate assumptions about plagiarism and the way in which new members of a community of practice develop the skills to become full members of that community. Drawing on the literature and some primary data it shows how expectations, norms and practices become translated and negotiated in such a way that legitimate attempts to conform with the expectations of the community of practice often become identified as plagiarism and illegitimate attempts at cheating often become obscured from view. It argues that this inappropriate framing and implementation of plagiarism detection systems may make academic integrity more illusive rather than less. It argues that in its current framing Y as systems for Fdetection and discipline_ Y plagiarism detection systems may become a new micro-politics of power with devastating consequences for those excluded.

Much of the current literature on plagiarism focuses on students, attempting to understand how students view the concept of plagiarism, the best ways to prevent it, and the impact of collaboration on the concept of original authorship. In this article, we look at the role of plagiarism in 761 conference abstracts written by graduate students, early-to late-career faculty, and industry representatives, representing institutions from nearly 70 countries. These abstracts were submitted for participation in an international conference focused on the liberal arts hosted by our institution over the past four years. This study analyzes the corpus for patterns of plagiarism among professional academic writers. Our findings indicate that, while other demographic categories were not consistent indicators of text-matching, full professors were the most prevalent group to produce self-plagiarized abstracts. Overall, our study illuminates the significance of power dynamics in conferences' efforts to maintain academic integrity. When Middle East Engineering University 1 (MEEU) launched its first International Liberal Arts Conference (ILAC), the organizing committee was inundated with proposals, hailing from scholars in a variety of disciplines from across the globe. The conference was intentionally multidisciplinary and designed to bring diverse, new scholarship from around the world into conversation. While an exciting goal, it raised concerns about the conference organizers' ability to maintain academic integrity. Committee members recognized a very specific limitation with running an interdisciplinary conference: reviewers often lacked detailed knowledge of the current literature in every field represented by the submissions received. In the first four years of the conference, submissions came from over 70 countries spanning virtually every field in the social sciences, arts, and humanities. This challenge prompted the organizing committee to utilize a familiar tool, one often used in their own classrooms: Turnitin, the originality checking software used to find text-matching in submitted documents. Turnitin offered an opportunity to aid committee members in identifying potential cases of plagiarism in many disciplines beyond those represented by members of the organizing committee. The ILAC has been held annually for the last four years at MEEU. While the theme is different each year, the conference consistently attracts scholars from all around the world to exchange new and original ideas within the area of liberal arts (broadly

The paper discusses self-plagiarism and associated practices in scholarly publishing. It approaches at some length the conceptual issues raised by the notion of self-plagiarism. It distinguishes among and then examines the main families of arguments against self-plagiarism, as well as the question of possibly legitimate reasons to engage in this practice. It concludes that some of the animus frequently reserved for self-plagiarism may be the result of, among others, poor choice of a label, unwarranted generalizations as to its ill effects based on the specific experience (and goals) of particular disciplines, and widespread but not necessarily beneficial publishing practices.

Ethics have received increased attention from the media and academia in recent years. Most reports suggest that one form of unethical conduct – plagiarism – is on the rise in the business schools. Stereotypes of Asian students as being more prone to plagiarize are frequently found in the literature, though not concretely substantiated. This study used a behavioral criterion to examine the relationships among ethnicity, acculturation, and plagiarism in a sample of 158 undergraduate and graduate students. Significant differences in plagiarism behavior were found based on level of student acculturation, but not ethnicity. Considerations and implications for training and managing international students and workers are discussed.

Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. Existing plagiarism detection systems reliably identify copied text, or near copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. We present Semantic Concept Pattern Analysis - an approach that performs an integrated analysis of semantic text relatedness and structural text similarity. Using 25 officially retracted academic plagiarism cases, we demonstrate that our approach can detect plagiarism that established text matching approaches would not identify. We view our approach as a promising addition to improve the detection capabilities for strong paraphrases. We plan to further improve Semantic Concept Pattern Analysis and include the approach as part of an integrated detection process that analyzes heterogeneous similarity features to better identify the many possible forms of plagiarism in academic documents.

Existing anti-plagiarism tools are, in fact, text matching systems but do not make accurate judgments about plagiarism. Texts that are acceptable to be redundant and texts that are cited properly are all highlighted as plagiarism, and the real decision of plagiarism is left up to the user. To reduce the human input and to give more reliance to automatic plagiarism detectors, we propose an Intelligent Plagiarism Reasoner (iPlag), which works by combining several analytical procedures. Scholarly documents under investigation are segmented into logical tree-structured representation using a procedure called D-SEGMENT. Statistical methods are utilised to assign numerical weights to structural components under a technique called C-WEIGHT. Relevance ranking (R-RANK) and plagiarism screening approaches (P-SCREEN) are adjusted to incorporate structural weights, citation evidences, syntax-based and semantic-based methods into plagiarism detection results. We encourage current plagiarism detection systems to adapt the proposed framework.

Detecting instances of plagiarism in student homework, especially programming homework,
is an important issue for practitioners. In the past decades, several tools have emerged that are able to
effectively compare large corpora of homeworks and sort pairs by degree of similarity. However, those tools
are available to students as well, allowing them to experiment and develop elaborate methods for evading
detection. Also, such tools are unable to detect instances of "external plagiarism" where students obtained unethical help from sources not among other students of the same course. One way to battle this problem is to monitor student activity while solving their homeworks using a cloud-based integrated development environment (IDE) and detect suspicious behaviours. Each editing event in program source can be stored as a new commit to create a form of ultra-fine-grained source code repository. In this paper, the authors propose several new features that can be extracted from such repositories with the purpose of building a comprehensive profile of each individual developer. Machine learning techniques were used to detect
suspicious behaviours, which allowed the authors to significantly improve upon the performance of more traditional plagiarism detection tools.

Plagiarism is one form of academic dishonesty, which is often done by students in programming classes. In a large class, detecting plagiarism manually is both difficult and time-consuming、especially due to the numerous modifications of the source code to conceal the cheating.

Unauthorized re-use of code by students is a widespread problem in academic institutions, and raises liability issues for industry. Manual plagiarism detection is time-consuming, and current effective plagiarism detection approaches cannot be easily scaled to very large code repositories. While there are practical text-based plagiarism detection systems capable of working with large collections, this is not the case for code-based plagiarism detection. In this paper, we propose techniques for detecting plagiarism in program code using text similarity measures and local alignment. Through detailed empirical evaluation on small and large collections of programs, we show that our approach is highly scalable while maintaining similar levels of effectiveness to that of the popular JPlag and MOSS systems.

This paper describes a new approach towards detecting plagiarism and scientific documents that have been read but not cited. In contrast to existing approaches, which analyze documents’ words but ignore their citations, this approach is based on citation analysis and allows duplicate and plagiarism detection even if a document has been paraphrased or translated, since the relative position of citations remains similar. Although this approach allows in many cases the detection of plagiarized work that could not be detected automatically with the traditional approaches, it should be considered as an extension rather than a substitute. Whereas the known text analysis methods can detect copied or, to a certain degree, modified passages, the proposed approach requires longer passages with at least two citations in order to create a digital fingerprint.

The ready availability of free online machine translation (MT) systems has given rise to a problem in the world of language teaching in that students -especially weaker ones -use free online MT to do their translation homework. Apart from the pedagogic implications, one question of interest is whether we can devise any techniques for automatically detecting such use. This paper reports an experiment which aims to address this particular problem, using methods from the broader world of computational stylometry, plagiarism detection, text reuse, and MT evaluation. A pilot experiment comparing 'honest' and 'derived' translations produced by 25 intermediate learners of Spanish, Italian and German is reported.

Most Australian universities have made attempts of various kinds to address plagiarism. Some have responded in recent times with a primary focus on catching and punishing plagiarists, often assisted by computer software packages. Others have taken a more holistic approach incorporating foci on policy, assessment regime and student preparation and education as well as on ensuring appropriate consequences for plagiarists. This paper outlines one example of the latter approach in one Australian university faculty and details the outcomes in terms of policy recommendations and resources to support the approach. The necessity of evidence-based evaluation of attempts to reduce plagiarism in higher education is argued.

Teaching computer engineering calls for an important practical component, usually covered by setting several laboratory exercises for each course. These exercises are specified as assignments by the teachers and have to be completed by the students. At ...