Fuzzy Logic and Optimization of Educational Paths

Abstract

In our work we analyze a set of economic factors, which affect on student's decision of educational path change. Taking into account such factors as the expected salary after graduation, educational costs, set of government exams and the amount of educational paths in the university, we estimate the possibility for each student to optimize his personal educational path. To describe the behavior of a rational individual and to estimate the optimal and preferred educational path under these conditions we use the classical economic theory, the classical theory of economic behavior, the methodology of increasing efficiency of the human capital and the institutional economic theory. To describe the economic motivations of an individual by changing the educational path we developed a mathematical-economic model of optimization and tested it on real data on more than 5,3 thousand students. It is necessary to highlight that, mainly, the choice of an educational path by a rational individual is driven by economic motivations and by his knowledge on the expected earning level after the graduation. In our model we compare the educational paths, which were chosen by students and take into account the importance of personal expectations to set and calculate the optimal, from the point of view of economic rationality, educational path, which is compared with current student's educational path. According to the modeling results, 66% of all students have chosen their optimal educational path taking into account their economic preferences and personal skills. To estimate the possibility for each student to change his educational path we developed an approach, that is based on the fuzzy logic model of Mamdani type. According to this approach, the possibility of educational path change for a student is calculated on the panel data taking into account the amount of perspective directions of student's graduation and educational paths, the possibility of budget support of the graduation and the expected salary level after graduation. As the application of this approach we can estimate the probability of educational path change for each student and optimize the education process in the University. © 2018