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Plagiarism in e-Learning Systems: Identifying and Solving the Problem for Practical Assignments
Abstract
A big part of life long learning is the move from residential lectures to distance education. Distance education falls under the multi-modal policy of the teaching institution and thereby a change in student contact. The lecturer facilitating the distance education course is also faced with a problem where the quality and originality of submitted assignments need to be checked. This has always been a difficult task, as going through practical assignments and looking for similarities is a tedious job. Software checkers are available, but as yet, have not been integrated into popular online e-learning systems. If closer contact and warning to students are given at an early stage the problem is minimized as they know they are being closely monitored. As shown in this article, plagiarism is a current problem with online practical submissions. We also show how this problem can be minimized through the integration of plagiarism checking tools and other checking methods into e-learning systems
... It has already been stated that plagiarism is a serious problem in academia, especially in distance learning enabled with in e-learning environments, as stated, for example, in Reference [106]. But what is plagiarism exactly, and how is it defined? ...
... Articles are sorted by date of publication. The reviewed articles are as follows: [143], [39], [54], [42], [59], [67], [131], [139], [170], [171], [172], [163], [167], [173], [121], [16], [53], [75], [76], [72], [52], [134], [25], [71], [36], [73], [86], [94], [115], [117], [3], [9], [105], [106], [116], [147], [20], [182], [30], [32], [49], [66], [68], [69], [119], [146], [22], [40], [87], [91], [103], [160], [168], [176], [5], [19], [24], [27], [28], [41], [63], [79], [89], [99], [100], [112], [111], [123], [124], [165], [175], [177], [178], [23], [55], [74], [104], [110], [113], [127], [1], [8], [10], [15], [21], [29], [33], [34], [64], [70], [80], [82], [84], [90], [98], [107], [118], [120], [133], [152], [4], [26], [166], [56], [88], [96], [101], [129], [157], [169], [174], [179], [181], [6], [11], [12], [85], [95], [108], [128], [132], [138], [153], [156], [164], [180], [184], [2], [7], [13], [44], [46], [50], [81], [102], [109], [125], [135], [140], [150], [154], [158], [38], [61], [78], [83], [114], [122], [151], and [155]. ...
Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”
... Online learning and tests have seen exponential growth in the context of the COVID-19 pandemic. The quality of assignments should be checked properly by teachers to minimize plagiarism at an early stage [2]. It is a time consuming and tedious job to check every paper for plagiarism. ...
Plagiarism means taking another person's work and not giving any credit to them for it. Plagiarism is one of the most serious problems in academia and among researchers. Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only. Bengali is the most widely spoken language of Bangladesh and the second most spoken language in India with 300 million native speakers and 37 million second-language speakers. Plagiarism detection requires a large corpus for comparison. Bengali Literature has a history of 1300 years. Hence most Bengali Literature books are not yet digitalized properly. As there was no such corpus present for our purpose so we have collected Bengali Literature books from the National Digital Library of India and with a comprehensive methodology extracted texts from it and constructed our corpus. Our experimental results find out average accuracy between 72.10 % - 79.89 % in text extraction using OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have built a web application for end-user and successfully tested it for Plagiarism detection in Bengali texts. In future, we aim to construct a corpus with more books for more accurate detection.
... Online learning and tests have seen exponential growth in the context of the COVID-19 pandemic. The quality of assignments should be checked properly by teachers to minimize plagiarism at an early stage [2]. It is a time consuming and tedious job to check every paper for plagiarism. ...
Plagiarism means taking another person’s work and not giving any credit to them for it. Plagiarism is one of the most serious problems in academia and among researchers. Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only. Bengali is the most widely spoken language of Bangladesh and the second most spoken language in India with 300 million native speakers and 37 million second-language speakers. Plagiarism detection requires a large corpus for comparison. Bengali Literature has a history of 1300 years. Hence most Bengali Literature books are not yet digitalized properly. As there was no such corpus present for our purpose so we have collected Bengali Literature books from the National Digital Library of India and with a comprehensive methodology extracted texts from it and constructed our corpus. Our experimental results find out average accuracy between 72.10 % - 79.89 % in text extraction using OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have built a web application for end-user and successfully tested it for Plagiarism detection in Bengali texts. In future, we aim to construct a corpus with more books for more accurate detection.
... Distance and online education can be even more vulnerable to plagiarism because of its remote and asynchronous nature. Concern about such vulnerability is growing along with the increasing number of online programs (Heberling, 2002;Marais, Minnaar, & Argles, 2006). In online course settings, PDS should be used as a tool to combat plagiarism and educate students on proper research and writing practices. ...
p class="AbstractText">Learning management systems (LMS) play a central role in communications in online and distance education. In the digital era, with all the information now accessible at students’ fingertips, plagiarism detection services (PDS) have become a must-have part of LMS. Such integration provides a seamless experience for users, allowing PDS to check submitted digital artifacts without any noticeable effort by either professor or student. In most such systems, to compare a submitted work with possible sources on the Internet, the university transfers the student’s submission to a third-party service. Such an approach is often criticized by students, who regard this process as a violation of copyright law. To address this issue, this paper outlines an improved approach for PDS development that should allow universities to avoid such criticism. The major proposed alteration of the mainstream architecture is to move document preprocessing and search result clarification from the third-party system back to the university system. The proposed architecture changes would allow schools to submit only limited information to the third party and avoid criticism about intellectual property violation.
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... Digg®, Reddit®, or Blackboard Scholar® to document Internet information resources, use reference citation services such as RefWorks® or EndNote® to collect and manage library research citations, and provide instructor access to those sites for verification. Instructors may require that final assignments be submitted to plagiarism detection services (Marais, Minnaar, & Argeles, 2006), and may offer students the opportunity to submit drafts of their assignments to these services to identify potential plagiarism issues (Kirkpatrick, 2006). Instructors may also require students to submit a supplemental paper explaining how they organized their search effort, where and how they identified their sources, how they integrated their information, and how they responded to unique challenges that arose during their project (Hart & Friesner, 2004). ...
Student plagiarism in online learning environments inhibits student learning and damages institutional reputations. Instructors may use many methods and technologies to instructors to combat plagiarism in online classrooms, including the use of plagiarism detection tools, establishing and administering academic integrity policies, developing effective education programs, and improving assessment practices. The focus of this paper is on reducing plagiarism in online learning environments by improving the design of student assignments.
Abstrak: Penelitian kebijakan ini bertujuan untuk menganalisis pengembangan bahan ajar dan tugas menggunakan layanan deteksi plagiarisme pada LMS untuk pembelajaran Online calon guru Madrasah Ibtidaiyah. Uji coba materi dilaksanakan untuk mahasiswa calon guru Pendidikan guru Madrasah Ibtidaiyah Unipdu Jombang pada tahun akademik 2020/2021 semester 5. Pengumpulan data dilakukan dengan menggunakan observasi, dan kuesioner. Data dianalisis menggunakan analisis deskriptif kualitatif. Temuan penelitian adalah: Efektivitas layanan deteksi plagiarime pada LMS dalam hal penurunan plagiarisme karya calon guru Madrasah Ibtidaiyah. Disimpulkan bahwa implementasi deteksi plagiarisme pada LMS (Learning Management Systems) untuk pembelajaran online tersebut efektif untuk mengurangi plagiarisme karya mahasiswa calon guru Madrasah Ibtidaiyah. Kata Kunci: Deteksi plagiarisme, sistem manajemen pembelajaran, calon guru. Abstract: This policy research aims to analyze the development of teaching materials and assignments using plagiarism detection services on LMS for online learning for Pre Service Teacher of Madrasah Ibtidaiyah. The material trial was carried out for Pre Service Teacher of Madrasah Ibtidaiyah teacher education at Unipdu Jombang in the 2020/2021 semester 5. Data collection was carried out using observations and questionnaires. Data were analyzed using qualitative descriptive analysis. The findings of the study are: effectiveness of plagiarism detection services on LMS in terms of reducing plagiarism of Pre Service Teacher of Madrasah Ibtidaiyah. It was concluded that the implementation of plagiarism detection in LMS (Learning Management Systems) for online learning was effective in reducing plagiarism of the work Pre Service Teacher of Madrasah Ibtidaiyah. Pendahuluan Pesatnya perkembangan internet seiring dengan meningkatnya literasi digital yang telah memudahkan para pengguna digital untuk menyalin karya orang lain dan menempelkannya ke dalam karya mereka sendiri. Plagiarisme sekarang menjadi isu yang membara di komunitas pendidikan,
Plagiarism in students programming assignment submissions causes considerable difficulties for course designers. Efficient detection of plagiarism in programming assignments of students is important to the educational procedure. This paper proposes a metric, based on information distance, to measure similarity between two programs. Furthermore, clustering analysis, based on shared near neighbors, is applied in order to provide more beneficial and detailed information about the program plagiarism. Experimental results demonstrate that our software has clear advantages over other plagiarism detection systems and it is quite beneficial to teachers to get rid of time-consuming and toilsome tasks. Key words: Program plagiarism, Detection, Information distance, Clustering
Many tutors believe that plagiarism, especially copying material from the Web, is a significant and increasing problem in UK higher education institutions. A number of academic and commercial groups are researching the nature and extent of the problem and are developing software tools and systems for plagiarism detection.
Recognising that prevention is better than cure, this paper commences by reviewing the advice that has been given by various institutions and agencies on how to specify assignments that are less prone to plagiarism. However, the evidence on the ground is that these precautions do not always prevent cheating and so effective detection systems are also needed. The major part of this paper will introduce a four-stage plagiarism detection model and describe some of the tools that can be used within it. Hopefully the deployment of an effective system will also have a significant deterrent effect.
In a digital library system, documents are available in digital form and therefore are more easily copied and their copyrights are more easily violated. This is a very serious problem, as it discourages owners of valuable information from sharing it with authorized users. There are two main philosophies for addressing this problem: prevention and detection. The former actually makes unauthorized use of documents difficult or impossible while the latter makes it easier to discover such activity. In this paper we propose a system for registering documents and then detecting copies, either complete copies or partial copies. We describe algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security) We also describe a working prototype, called COPS, describe implementation issues, and present experimental results that suggest the proper settings for copy detection parameters.
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.
The assessment of programming courses is usually carried out by
means of programming assignments. Since it is simple to copy and edit
computer programs, however, there will always be a temptation among some
students following such courses to copy and modify the work of others.
As the number of students in these courses is often high, it can be very
difficult to detect this plagiarism. The authors have developed a
package which will allow programming assignments to be submitted online,
and which includes software to assist in detecting possible instances of
plagiarism. In this paper, they discuss the concerns that motivated this
work, describe the developed software, tailoring the software to
different requirements and finally consider its implications for large
group teaching
Copy detection mechanisms for digital documents Using the Google search engine to detect word-for-word plagiarism in master's theses: a preliminary study
- S Brin
- J Davis
- H Garcia-Molina
- M Mccullough
- M Holmberg
October 2005.
[5] Brin, S., Davis, J., Garcia-Molina, H., Copy
detection mechanisms for digital documents, In Proceedings
of the ACM SIGMOD Conference, pages 398–409, 1995.
[6] McCullough, M., Holmberg, M., Using the Google
search engine to detect word-for-word plagiarism in master's
theses: a preliminary study, College Student Journal,
http://www.findarticles.com/p/articles/mi_m0FCR/is_3_39/ai
_n15384389, September 2005.
Using the Google search engine to detect word-for-word plagiarism in master's theses: a preliminary study
- M Mccullough
- M Holmberg
Authentication Integration, WebCT website, http://www.webct.com/service s/viewpage?name=services.authentication, Accessed on 8
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