Analysis of student’s final qualification theses using text loans detection systems - D. A. Petrusevich; K. D. Shakhardin (Статистика и экономика)
In this paper there are results of the bachelor and master theses citing analysis. These students graduated from the Higher mathematics chair of the Russian Technological University in the summer of 2018. In this comparative analysis the dependencies of thesis loan percent on parameters of students, statistical values of their theses are explored. This research is actual because of the progress and development of new informational technologies used in the educational system. Popularity of the text loan detection systems increases. Automatic plagiarism detection systems are intended to make educational process better, the text drawing search easier, to support the copyright laws and academical honesty. The percentage is given by two main Russian plagiarism detection systems: Antiplagiat and Rucontext. Connections between thesis parameters are explored. Advantages of each text loan detection systems are described. In this research there are the results of the pedagogical experiment aimed to analyze statistically the dependencies of the bachelor’s and master’s theses loan percentage which have been got from Antiplagiat and Rucontext systems on the author’s parameters, statistical values describing thesis text. The comparison between statistical results of these systems have been made. The conclusions about their advantages have been presented in the paper. In order to make the comparison methods of the mathematical statistics have been used. Numerical experiment has been provided by means of the packages of the R statistical language. The difference between text loan percentages in the Antiplagiat and Rucontext systems has been analyzed. It has been shown that it grows when length of the text becomes larger. The dependencies of the text loan percentage on the available parameters of the thesis author and text parameters have been presented. The dependencies types are the same for the both systems. Scale of the coefficients in the statistical dependencies is also the same. The difference is in the very set of the parameters: the Rucontext percentage is better described statistically with the sex of the author, the Antiplagiat percentage is described with the type of the higher education (bachelor’s or master’s thesis). Also the dependency of the text loan percentage on the length of the thesis text differs: the Antiplagiat percentage is better described statistically with the number of words but the Rucontext percentage is described with the number of characters. It seems that these differences can be explained with different text search and analyze algorithms. The dependencies between the Rucontext percentage and the Antiplagiat text loan percentage is presented.