Abstract:
Plagiarism is very common among students in higher education institutes due to
many reasons such as lack of knowledge about the subject, poor academic writing
skills or difficulty in meeting a given deadline. The most popular method of
plagiarism is to use the online web pages or e-books as it is an easy effort to get the
contents from internet, change it and to submit as an original work. Hence, there are
bunch of online software tools as well as offline tools exists to detect the plagiarism.
However, there are less software tools to identify the copied works among students.
Therefore, in this research I developed a plagiarism detection tool to identify the
plagiarized assignments or tutorial submitted. Individual assignments and tutorials
which had been given to software engineering courses of the Department of
Computing and Information System of Wayamba University were used as the
dataset. Natural language processing algorithms were developed to derive the
statistical features from the assignments such as bag of words, most frequent words,
number of words, name entities and paragraphs etc. Moreover, Term Frequency and
Inverted Document Frequency (TF-IDF) module was developed to generate a
similarity index value among assignments. In addition, Latent semantic analysis
module was developed with the word dictionary and vector corpus. Features that
were generated and extracted from every module were used to identify the clusters
of similar assignments. K-mean clustering algorithms in rapid minor were used to
identify the clusters. Most of the submitted assignments were identified with number
of clusters. Once the clustering results were verified with the students, it was evident
that fairly good results were the given by the automatic cluster classification.
