ONLINE ASSIGNMENT PLAGIARISM CHECKER PROJECT USING DATA MINING

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ONLINE ASSIGNMENT PLAGIARISM CHECKER PROJECT USING DATA MINING

ABSTRACT

Higher education students are expected to develop critical analysis and creative thinking skills, where plagiarism can damage the development of these skills in addition to damaging the whole education process and experience. Furthermore, plagiarism undermines the trust between the lecturers and students and the reputation of the academic institutions can be affected if plagiarism is not considered seriously, where the degrees offered by these institutions can be devalued. In this paper, two plagiarism prevention techniques followed by two plagiarism detection techniques used in the engineering education in the University of Southampton are presented. The plagiarism prevention techniques presented are based on assigning individual coursework specifications to students and the use of individual presentation of coursework findings. Then, the plagiarism detection techniques are based on detecting the writing styles of students and testing the student’ codes in different configurations.

ONLINE ASSIGNMENT PLAGIARISM CHECKER PROJECT USING DATA MINING

CHAPTER ONE

INTRODUCTION

  1. 0 Introduction

‘Plagiarism is the act of taking the writings of another person and passing them o’ as one’s own. The fraudulence is closely related to forgery and piracy-practices generally in violation of copyright laws.’ (Encyclopedia Britannica, cited in Asim, MEr Ali, HM, Dahwa, A, & Vaclav, S 2011). In the age of information technology and the wide use of laptops, tablets and smart phones by students, it has become easy for students to access and use information online. Hence, plagiarism detection and prevention has become a major concern is higher education worldwide. According to the University of Southampton handbook, ‘Plagiarism is using someone else’s work without acknowledging it or crediting the original author’ (University of Southampton, 2017b).

Plagiarism has a number of negative effects on education. Students who plagiarise lose the chance to develop their critical thinking and research experience they are supposed to gain in higher education. Additionally, plagiarism generally affects the relations between the lecturers and students, where trust can be lost. Furthermore, the reputation of academic institutions can be destroyed and their degrees devalued, if plagiarism is allowed to be the norm (Dey & Sobhan, 2006). Therefore, universities often educate students about plagiarism, apply plagiarism prevention and detection techniques and then use appropriate punishments in order to deter students. The University of Southampton takes plagiarism very seriously and applies a range of penalties to deter students, which can range from failing an assignment to failing a class or getting suspended or expelled (University of Southampton, 2017a, 2017b).

There have been several techniques for detecting plagiarism, which mainly use software tools for detecting the different types of plagiarism (Cosma & Joy 2012; Halak & El-Hajjar, 2016; Jhi et al., 2015; Jiffriya, Jahan, Ragel, & Deegalla, 2013; Li, Chen, Xin, Bin, & Vitanyi, 2004; Rosales et al., 2008a; Tian et al., 2015). These techniques are mostly based on comparing text and finding any textual similarity to published material available in the software repositories, which is then validated by an instructor (Li et al., 2004). The ‘Turnitin’ software tool (Turnitin, 2017) is an example of such a tool, which is widely adopted by the academic institutions in the UK including the University of Southampton.

While using software tools for detecting plagiarism can be effective in highlighting potential plagiarism cases, these tend to be mainly textual plagiarism, where the text is directly copied from its source without paraphrasing or acknowledging the source. The software tools tend to be less effective to highlight ideas-theft or collusion or software plagiarism. This is due to the fact that these software tools compare the text of a report with the text in submissions available in the repository, which makes these tools not efficient in detecting the plagiarism of undocumented ideas. On the other hand, the repository of these software tools might be limited in covering all relevant literature and hence they may fail to detect plagiarised material (Kaner & Fiedler, 2008a).

Another common case can be found in class-based assignments, where students share the solution to the common problems. This is particularly relevant in engineering coursework assignments, where students are requested to develop a hardware or software using specific configurations. Source code plagiarism detections (Cosma & Joy 2012; Rosales et al., 2008a) have been developed to detect such cases, but students can use different programming languages to implement the same solutions, where detection of plagiarism using these tools becomes inefficient.

In this paper, we commence by presenting a detailed description of plagiarism and its types chapter 2. Then, we present two techniques for preventing plagiarism in chapter 3 and another two techniques to detect plagiarism in engineering class assignments in chapter 4. The plagiarism prevention techniques presented are based on assigning individual coursework specifications to students and the use of individual presentation of coursework findings. On the other hand, the plagiarism detection techniques are based on evaluation of the writing style of technical reports and the rigorous testing of the software codes submitted by students. These techniques are currently applied to courses at the undergraduate and Master level in the University of Southampton, where we show that they are effective at reducing plagiarism and improving students’ understanding. Finally, we present the conclusions in chapter 5.

  1. 2PROBLEM STATEMENT

There exists lots of plagiarism detection software, implemented either as online applications or as desktop applications. Most popular plagiarism detection software combines the following corpus to check plagiarism:

  • A reference corpus of documents in their databases.
  • A reference corpus of documents uploaded by the user.
  • Documents and web pages on the world wide web.

Turnitin, the just contracted plagiarism detection software for fighting plagiarism in Nigerian universities (The Punch 2013) combines the three in the detection of plagiarism.

Till date no plagiarism detection software has been developed in Nigeria. We rely on those from abroad which are mostly tailored to the needs of the environment where they were developed. Also, one can say that majority of research works and other works in Nigerian universities are yet unpublished and therefore not likely to be on the web or in the databases of popular plagiarism detection software. Hence if an award winning research work is yet to published, a plagiarist may decide to use that work without being detected.

There is therefore a need to create a reference corpus where source documents of research works done in Nigerian universities can be kept for testing plagiarism.

1.3 OBJECTIVES OF STUDY

This research should accomplish the following:

  1. Research on some important methodologies that have been used to detect external plagiarism.
  2. Create a model for a corpus where all submitted projects (published or not) in Nigerian universities can be collected to be used as source documents for testing if a suspected work contains plagiarized contents.

III. Develop an online system that will be able to detect based on titles of projects whether a project already existing in the reference corpus has been plagiarized by a new project.

1.4 SCOPE AND LIMITATION OF STUDY

As a case study, the model for this project work will be based on past projects from the department of computer science, University of Lagos. This project majorly focuses on external plagiarism detection, which is just like a step in the whole process of detecting plagiarism automatically.

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