Data mining of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (to prevent customer churn)

Abstract

Student withdrawal in higher education is one the important challenges in universities. This paper considers the admission of fee paid students as a business and their withdrawals as customer churn. The aim is to investigate the attrition and predicted risk of attrition to adapt interventionist polices deterrent. This study is a descriptive an applicable technique that uses quantitative and qualitative data. It uses Crisp technology of data mining. The data are derived from educational system of University of Tehran including 21420 fee paid students accepted at 2010 to 2014. The main goal is to analyze the behavior that is at risk of attrition and withdrawal. After data analyze and construction of predictive modeling, the probability table of attrition and regression model will be presented. The final results show that the first and second semester (especially the age range 24-31) of M.Sc students are the most likely risk of withdrawal of happening.