Designing a system to identify main factors doctoral applicant's selection by using Variable Precision Rough Set Theory

Increasing demand for doctoral programs and slow growth rate of appropriate academic capacity has caused the candidates of such programs to be involved in a selection process. In this paper, Variable Precision Rough Set Theory (VPRST) was employed to design a system for identifying the main factors in doctoral student's selection. In order to design the system, the evaluation factors in doctoral interview were identified. Using information from previous years, data collection set was prepared. Then, utilizing Variable Precision Rough Set Theory, the effecting factors in doctoral student's selection were identified. These results are used to provide the rules and regulations for applicants’ assessment and acceptance in doctoral programs. To evaluate the efficacy of proposed system, the data obtained from Tarbiat Modares University from 2009 to 2015 doctoral selection examinations were used. Results showed that 3 of 12 factors that were evaluated in the interviews including “number of papers”, “presentation skills” and “result of written examination” were effective in applicant's selection and based on these three factors decision can be made about 71% of the applicants. While using the power of scientific analysis- as the fourth effecting factor- could increase the accuracy of decision-making up to 91%. The system can be used as a decision support system for the evaluation and acceptance of students by faculty members.