Ahmadi, Saied Ali Akbar, Karimzadgan, Davood, Khairati Kazerooni, Toraj. (1394). Data mining of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (to prevent customer churn). , 7(2), 217-238. doi: 10.22059/jitm.2015.53969
Saied Ali Akbar Ahmadi; Davood Karimzadgan; Toraj Khairati Kazerooni. "Data mining of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (to prevent customer churn)". , 7, 2, 1394, 217-238. doi: 10.22059/jitm.2015.53969
Ahmadi, Saied Ali Akbar, Karimzadgan, Davood, Khairati Kazerooni, Toraj. (1394). 'Data mining of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (to prevent customer churn)', , 7(2), pp. 217-238. doi: 10.22059/jitm.2015.53969
Ahmadi, Saied Ali Akbar, Karimzadgan, Davood, Khairati Kazerooni, Toraj. Data mining of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (to prevent customer churn). , 1394; 7(2): 217-238. doi: 10.22059/jitm.2015.53969

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

Journal of Information Technology Management
مقاله 2، دوره 7، شماره 2، 2015، صفحه 217-238    اصل مقاله (445.25 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2015.53969
نویسندگان
Saied Ali Akbar Ahmadi1؛ Davood Karimzadgan2؛ Toraj Khairati Kazerooni* 3
1Prof. Faculty of Management , Payam Noor University West Unit, Tehran, Iran
2Assistant Prof. Faculty of Computer Engineering , Payam Noor University, Tehran, Iran
3MSc. Student in Information Technology Management, Faculty of Manegent Payam Noor University of Tehran, Iran
چکیده
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. 
کلیدواژه‌ها
customer churn؛ customer relation management؛ educational data mining؛ withdrawal
مراجع
آمار
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