ALGORITHMIC AND PROGRAM IMPLEMENTATION OF THE PLAGIARISM DEFINITION IN LEARNING MANAGEMENT SYSTEMS

• Main Authors: Y. B. Popova, A. V. Goloburda
• Format: Article
• Language: English
• Published: Belarusian National Technical University 2018-06-01
• Series: Sistemnyj Analiz i Prikladnaâ Informatika
• Subjects: plagiarism; vector document model; terms; n-list; similarity matrix; cluster; cluster analysis
• Online Access: https://sapi.bntu.by/jour/article/view/206

The main advantage of using information technologies in education, which consists in speeding up and simplifying of information exchange, is also its drawback, because it raises the problem of plagiarism. The purpose of this paper is to develop testing text software for uniqueness in learning management systems. To achieve this goal, it is necessary to solve a range of problems related to the choice of a method for determining plagiarism, its algorithmization and software implementation. The work deals with the methods of shingles, super-shingles, signature methods, vector models of text representation, as well as cluster analysis of text information. The authors suggest a modification of the vector model to improve the accuracy of determining similar documents by creating an N-list of each document separately. As a result, a pairwise comparison of the documents and the formation of the image of one document relative to the N-list of the other will occur. Thus, in the i-th row of the similarity matrix, the coefficients of similarity of all the documents considered relative to the i-th document will be recorded. The proposed modification will also speed up the calculation process, since there is no need to search for common terms for all documents. To analyze a large number of student’s works in order to test them for plagiarism, the authors propose using a cluster approach. Its application showed that the time for determining duplicates for one document and for all documents included in the sample is the same. For the same time it is possible to get all the options for the same works of students. Thus, the use of cluster analysis of text information in determining plagiarism significantly saves both the teacher’s time and computing resources. The software implementation of the proposed algorithms is implemented as a web service in the Java language.