Discuss about the Statistics Research for Computers and Composition.
The future perspective of automatic student plagiarism presents interesting trends. The increased liberty of the students in the use of computers in the teaching process has led to a proliferation of the plagiarism. The students are supposed to make numerous assignments in their academic life. They can get a wide online database of electronic texts which make their life easier. The plagiarism is often compared to “cheating” and it is considered as an offence in the field of academics (Hosny and Fatima, 2014). There is several software for the purpose of detecting plagiarism in the work of the students. However, the traditional plagiarism tools are unable to detect the complicated nature of the plagiarism (Hosny and Fatima, 2014). There are limitations in the simple plagiarism detection tools. This paper would explore the complexities of the plagiarism and how the traditional tools of plagiarism detection should be modified in order to detect the advanced issues of plagiarism.
This chapter would discuss about the burning issue of student plagiarism and its increasing prevalence in the academic world. It would also discuss the nature of plagiarism, its types, current state of plagiarism detection and the future requirements of the plagiarism software. This section would give way to the data collection as well as data analysis segments of the research on this topic.
One of the most common problems in the academic world is plagiarism. The Universities have a tough time in the detection of the plagiarism issues in the academic work of the students (Andreescu, 2013). It is concerned with the undocumented as well as unauthorized use of someone else’s work (Andreescu, 2013). The students often lack sufficient time to complete their assignments on time. This is because they are over loaded with class, extracurricular activities and group activities. They are already pre-occupied with the intricacies of the academic course (Andreescu, 2013). They find it too stressful to write their assignments on their own. They tend to search for alternative methods to do their assignments with least effort and within minimal amount of time (Shivaji and Prabhudeva, 2015). The students are often tempted to copy-paste the valuable work of others and give it their own name (Shivaji and Prabhudeva, 2015). They are inclined to get good grades in the University so that they can get decent jobs after graduation. A quality assignment is a prerequisite for getting good grades in academics (Shivaji and Prabhudeva, 2015). This makes them claim the works of the other authors.
The students are usually unwilling to accept the work of the others and do not provide sufficient citations. They are not willing to acknowledge the work of the others fearing that it is detrimental for their own career. They are afraid of the fact that if they give proper citations, then his own credibility may be questioned by the lecturer or others (Shivaji and Prabhudeva, 2015). He simply copies the text of the author’s and ignores the necessity of providing appropriate citations (Tripathi, Tiwari and Nithyanandam, 2015). This is because of two reasons. He may feel lethargic to perform unnecessary formalities or he may simply forget to provide them (Shivaji and Prabhudeva, 2015). The student may quote long passages without providing adequate references (Tripathi, Tiwari and Nithyanandam, 2015). There are a lot of free materials available online for the students which makes them tempted to copy paste the work. There are also sufficient “online essay providing services” that are directly related to the issue of plagiarism (Tripathi, Tiwari and Nithyanandam, 2015). This makes it one of the burning issues of modern day teaching methodologies.
It is extremely important to detect any plagiarism issues in the academic work of the students. Some of the most important reasons for the detection of any plagiarism issues are described below-
The plagiarism has serious impact on the academic life of the students. This is a growing menace that is posing a serious threat to the students. If a student is found to be engaged in plagiarism, it will not only affect their future professional life, but he would face suspicion and ignorance. Some of the consequences of plagiarism are as follows-
There are different types of plagiarism that are seen in the academic papers of the students. The following are the broad categories of plagiarism-
The early plagiarism detection software was equipped with the detection of simple copy-paste or straightforward use of the concepts of other authors. They were able to evaluate the rearrangement of the sentences and the paragraphs (Stapleton, 2012). However, the modern day students are well aware of the different criteria used by the plagiarism detection software and hence they twist the sentences so that they go undetected while checking in the plagiarism software (Stapleton, 2012). There are some students who engage in using the “anti plagiarism software” which are readily available over the internet (Stapleton, 2012). This makes it essential for the modern day plagiarism software to incorporate advanced features (Stapleton, 2012). The practice of plagiarism is not only done by the students, but there are instances when the academic staffs also engage in the same (Stapleton, 2012). They often publish papers that have huge similarity with the works produced by other authors. This happens since the original papers are already published in the internal journals and the professors strive to gain a quick reputation in the academic circle (Stapleton, 2012). There can be two kinds of plagiarism detection software namely automatic detection and manual detection (Oberreuter and VeláSquez, 2013).
The manual detection of plagiarism deals with checking of the plagiarism issue by the human beings. This is an old method of checking the plagiarism that consumes a significant amount of time and energy of the teaching faculty (Cosma and Joy, 2012). The automatic detection of plagiarism is a modern day tool of checking the plagiarism issue. The automatic plagiarism detection tools check the textual plagiarism and source code plagiarism (Cosma and Joy, 2012). The textual plagiarism utilizes the “grammar based methods”, “semantic-based methods” and “grammar semantics hybrid methods” (Cosma and Joy, 2012). Some of the most common plagiarism tools used are Turnitin, PlagScan, PlagAware, iThenticate and others (Cosma and Joy, 2012).
The modern day plagiarism software is often unable to detect the advanced issues of plagiarism. There are technological loopholes in the plagiarism detection software that are unable to detect the latest copy paste issues in student’s papers (Falchikov, 2013). The students engage in modifying their own work in order to avoid the issues of plagiarism (Falchikov, 2013). The plagiarism detection software works by extracting the text portions from the assignment and then matching them with any published document. The students engage in mechanisms that prevents the software from extracting texts from the document (Baepler and Reynolds, 2014). This does not affect the view of the document or the layout/format of the documents (Falchikov, 2013). This makes the software unable to detect any plagiarism related issues and it shows 0% plagiarism. The students are also engaging in converting their assignments in a PDF format and subsequently altering the “character map” of the text (Lambert, 2014). They can also rearrange the “character codes” in the PDF version of their assignments so that the connecting link between the printed representation and text is lost (Kossey, Berger and Brown, 2015). This makes it impossible for the plagiarism detection software to detect any similarity.
In this age of technological advancements, it is important to create efficient plagiarism detection tools that would be difficult to deceive (Howard et al., 2013). This is required for the purpose of elevating the quality of academic assignments and also reduces the instances of the plagiarism (Ganascia, Glaudes and Del Lungo, 2014). The advanced plagiarism tools should perform morphological analysis of the assignment of the students. The software should also engage in the removal of suffixes (Howard et al., 2013). It is also used to isolate the word from a given word. The new age software also engages in the conversion of sentences into “parse trees” and the use of transposition of the given words (Wagner, 2012). The software can also use the electronic thesaurus. It is also important to track the citation and the reference list of the students (Howard et al., 2013). The advanced plagiarism software would be able to detect the latest tricks of the students and would successfully detect the plagiarism issues (Howard et al., 2013). This would make the issue of plagiarism less severe and detect any possible plagiarism issues in an effective manner.
The plagiarism software should be able to detect the latest glitches present in the assignment of the students. The software should not only detect the grammar issues or the sentence construction issues of the assignments, but it should also detect the issue of stealing someone else’s idea. It is important to check the work of ghost writers who often unethically uses the concept of another author in their own works. The modern software should also be able to detect the cross language issues which are often found in the papers. The future plagiarism detectors must check the all round quality of the assignments ranging from copy paste to the reproduction of someone else’s innovative ideas.
Conclusion
The issue of student plagiarism is a serious concern in the academic world. It is important to install good plagiarism detection software used for academic purposes. There are severe consequences of the plagiarism issue such as damaged professional reputation, legal and lowering of the academic reputation. There are different types of plagiarism such as accidental, direct, mosaic, tough and self. The current scenario of the plagiarism detection system is also discussed with reference to the automatic detection methods. There are several loopholes in the present scenario of the detection of the plagiarism by the help of several tools. The need for the advanced plagiarism detection tools for plagiarism is also discussed. The concluding part of the paper deals with the recommendations for the creation of an efficient tool for the detection of plagiarism. Some concepts have been given that lead to the efficient utilization of the plagiarism software. This paper would surely enhance the understanding of the plagiarism detection software and how to customize them for fulfilling the needs of the modern day assignments.
References
Andreescu, L., 2013. Self-plagiarism in academic publishing: the anatomy of a misnomer. Science and Engineering Ethics, 19(3), pp.775-797.
Angélil-Carter, S., 2014. Stolen language?: Plagiarism in writing. Routledge.
Baepler, P. and Reynolds, T., 2014. The digital manifesto: Engaging student writers with digital video assignments. Computers and composition, 34, pp.122-136.
Bretag, T., 2013. Challenges in addressing plagiarism in education. PLoS Med, 10(12), p.e1001574.
Cosma, G. and Joy, M., 2012. An approach to source-code plagiarism detection and investigation using latent semantic analysis. IEEE transactions on computers, 61(3), pp.379-394.
DeGeeter, M., Harris, K., Kehr, H., Ford, C., Lane, D.C., Nuzum, D.S., Compton, C. and Gibson, W., 2014. Pharmacy students’ ability to identify plagiarism after an educational intervention. American journal of pharmaceutical education, 78(2).
Falchikov, N., 2013. Improving assessment through student involvement: Practical solutions for aiding learning in higher and further education. Routledge.
Ganascia, J.G., Glaudes, P. and Del Lungo, A., 2014. Automatic detection of reuses and citations in literary texts. Literary and Linguistic Computing,29(3), pp.412-421.
Graham-Matheson, L. and Starr, S., 2013. Is it cheating–or learning the craft of writing? Using Turnitin to help students avoid plagiarism. Research in learning technology, 21.
Hosny, M. and Fatima, S., 2014. Attitude of students towards cheating and plagiarism: University case study. Journal of Applied Sciences, 14(8), p.748.
Howard, M.J., Gupta, S., Pollock, L. and Vijay-Shanker, K., 2013, May. Automatically mining software-based, semantically-similar words from comment-code mappings. In Proceedings of the 10th Working Conference on Mining Software Repositories (pp. 377-386). IEEE Press.
Kossey, J., Berger, A. and Brown, V., 2015. Connecting to Educational Resources Online with QR Codes. FDLA Journal, 2(1), p.1.
LAMBERT, L.R., 2014. Occurrence of plagiarism in the writing of international graduate business students and its detection by SafeAssign(Doctoral dissertation, University of Illinois at Urbana-Champaign).
Oberreuter, G. and VeláSquez, J.D., 2013. Text mining applied to plagiarism detection: The use of words for detecting deviations in the writing style.Expert Systems with Applications, 40(9), pp.3756-3763.
Sheehan, E.A., 2014. That’s What She Said: Educating Students about Plagiarism. Essays from E-xcellence in Teaching Volume XIII, p.43.
Shivaji, S.K. and Prabhudeva, S., 2015. Plagiarism Detection by using Karp-Rabin and String Matching Algorithm Together. International Journal of Computer Applications, 115(23).
Stapleton, P., 2012. Gauging the effectiveness of anti-plagiarism software: An empirical study of second language graduate writers. Journal of English for Academic Purposes, 11(2), pp.125-133.
Tripathi, R., Tiwari, P. and Nithyanandam, K., 2015, January. Avoiding plagiarism in research through free online plagiarism tools. In Emerging Trends and Technologies in Libraries and Information Services (ETTLIS), 2015 4th International Symposium on (pp. 275-280). IEEE.
Wagner, J., 2012. Detecting grammatical errors with treebank-induced, probabilistic parsers (Doctoral dissertation, Dublin City University).
Yadav, S., Rawal, G. and Baxi, M., 2016. Plagiarism
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