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

A Machine Learning Approach for Plagiarism Detection

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases. Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author. The Intrinsic method aims to generate a model of author “style” by revealing a set of certain features of authorship. The model’s generation procedure focuses on just one author as an attempt to summarise aspects of an author’s style in a definitive and clear-cut manner. The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists

Improving source code plagiarism detection systems

Образовање у области рачунарства укључује практичан рад кроз програмске задатке који су честа мета плагијаризма. У овом раду су дискутовани различити аспекти плагијаризма у програмском коду у академском окружењу, извршена је упоредна анализа софтверских система за детекцију сличности и предложена њихова унапређења. Изабрани системи су евалуирани коришћењем три различита програма над којима је коришћено више од 20 типова лексичких и структуралних измена које су примењиване на код током 1, 2, 4, и 8 сати рада. Примењено је и реално оптерећење које је укључивало задатке обима од 50 до 1000 линија програмског кода са три различита предмета које је похађало од 100 до 300 студената. Резултати су показали да 5-10% студената, сходно метрици и критеријумима ове тезе, плагира своја решења...Computing education involves practical training through programming assignments which are frequent targets for plagiarism. In this thesis, different aspects of source code plagiarism in academic environment are discussed. Comparative analysis of source code similarity detection systems was performed and several improvеments were proposed. Selected systems were evaluated using simulated plagiarism based on three programming assignments produced after 1, 2, 4, and 8 hours of work on baseline version using more than 20 types of lexical and structural modifications. Real-life student codes from three different courses were also used for evaluation. The courses were attended by 100 to 300 students, and the solutions varied from 50 to 1000 lines of code. The results show that 5-10% of students plagiarized their solutions, according to the criteria used in this thesis..

A study on plagiarism detection and plagiarism direction identification using natural language processing techniques

Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection. This thesis provides novel perspectives on plagiarism detection and plagiarism direction identi cation tasks. The hypothesis is that original texts and rewritten texts exhibit signi cant but measurable di erences, and that these di erences can be captured through statistical and linguistic indicators. To investigate this hypothesis, four main research objectives are de ned. First, a novel framework for plagiarism detection is proposed. It involves the use of Natural Language Processing techniques, rather than only relying on the vii traditional string-matching approaches. The objective is to investigate and evaluate the in uence of text pre-processing, and statistical, shallow and deep linguistic techniques using a corpus-based approach. This is achieved by evaluating the techniques in two main experimental settings. Second, the role of machine learning in this novel framework is investigated. The objective is to determine whether the application of machine learning in the plagiarism detection task is helpful. This is achieved by comparing a thresholdsetting approach against a supervised machine learning classi er. Third, the prospect of applying the proposed framework in a large-scale scenario is explored. The objective is to investigate the scalability of the proposed framework and algorithms. This is achieved by experimenting with a large-scale corpus in three stages. The rst two stages are based on longer text lengths and the nal stage is based on segments of texts. Finally, the plagiarism direction identi cation problem is explored as supervised machine learning classi cation and ranking tasks. Statistical and linguistic features are investigated individually or in various combinations. The objective is to introduce a new perspective on the traditional brute-force pair-wise comparison of texts. Instead of comparing original texts against rewritten texts, features are drawn based on traits of texts to build a pattern for original and rewritten texts. Thus, the classi cation or ranking task is to t a piece of text into a pattern. The framework is tested by empirical experiments, and the results from initial experiments show that deep linguistic analysis contributes to solving the problems we address in this thesis. Further experiments show that combining shallow and viii deep techniques helps improve the classi cation of plagiarised texts by reducing the number of false negatives. In addition, the experiment on plagiarism direction detection shows that rewritten texts can be identi ed by statistical and linguistic traits. The conclusions of this study o er ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.EThOS - Electronic Theses Online ServiceGBUnited Kingdo