Plagiarism--does it ring a bell?
Intellectual dishonesty such as plagiarism comes in many shapes and forms. Brazenly copying of someone else’s piece and blatantly passing it up as one’s own work is an easy way to submit a project. Sure, it is easy to plagiarize but these days, with technological advances such as google, it's also easier to detect plagiarized work. It’s the more sophisticated forms involving rephrased (but remain uncited) materials or materials sourced from the analog world that makes plagiarism hard to detect for electronic tools against copied work.
Fortunately, for as long as the submitted material is electronic, there’s always a way to determine whether a submission is plagiarized or not. You can also look at readability levels and sentence construction. Or examine specific phrases to compare with other materials previously submitted by other students.
What if the student has no prior submissions? In this case, you can compare his submitted work with the materials submitted by his fellow students as well as compare it with the vast amount of materials available online. Somehow, the rater or the teacher must have the confidence in his own ability to flag plagiarized work.
Such confidence (or lack of it) is only probabilistic. At what percentage of the whole document would prove conclusive that it is plagiarized? 10%, 49%, 55%, 70%? The higher the percentage of similarities between the submitted document and other previously submitted documents (whether online or those submitted by his fellow students), the stronger is the teacher’s confidence that the submission is plagiarized. It’s the lower numbers that may prove to be rather tricky – and it’s never an exact science.
Like any probabilistic tools, plagiarism-detection apps are bound to yield either false positive results telling us there’s a likelihood of plagiarism where there’s none or false negative results showing no plagiarism when there’s one in fact. (See, for instance, this piece from Inside Higher Ed.)
Hence, even more important than one’s reliance on plagiarism-detection apps or tools, is the engagement of a teacher with students. Processes or systems can be put in place where students do not have to plagiarize any material. Do you see gradual progress between this version and the later versions? Or do you see a sudden improvement in a student’s “eloquence” that may need a second look at his work?
This is the kind of context where plagiarism detection is properly put. Using moodLearning anti-plagiarism (mLaP) tool, for instance, one at least has the ability to automate the routine part of screening for copied parts of submitted document. Advantages include (see also details here):
Unlimited number of users. Unlike many other commercial services, the mLaP Service does not limit the number of users. It comes bundled with the service contract with moodLearning.
Combined power of major search engines. Using the results from major search engines like Google, Bing, and others, the mLaP Service searches the Internet for similar documents to be compared with student submitted documents, to check for possible plagiarism.
Integrated search. The mLaP Service is embedded right in the settings of Assignment submission, thus avoiding the added task of submitting or cutting-and-pasting student papers in third-party online portals.
IP Control. The partner institution keeps the control or ownership of intellectual property (IP) of submitted documents from its constituents. These files do not leave the institution's installation.
Native search engine. In addition to the combined power of major search engines, the mLaP Service has its own powerful network search engine (moodLearning Search) that can be used to index contents found within a particular network or domain.
Privacy support. The mLaP service provides privacy support to submitters of documents.
Compared with the leading plagiarism scanner in the market, mLaP cannot be far behind either.
Do you think an anti-plagiarism service like mLaP needs to be integrated into your own learning management system? Talk to us at moodLearning.