Data Mining Withdrawal of the Students of Payame Noor University in Tehran State to Increase Student Rentention Rate (Preventing Customer Rejection)

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Ahmadi Sayed Ali Akbar; KHAIRATI KAZEROONI TORAJ

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Journal: JOURNAL OF PUBLIC ORGANZATIONS MANAGEMENT Year:2019 | Volume:7 | Issue:2 (26) Page(s): 47-58

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Information Journal Paper

Title

Data Mining Withdrawal of the Students of Payame Noor University in Tehran State to Increase Student Rentention Rate (Preventing Customer Rejection)

Author(s)

Ahmadi Sayed Ali Akbar | KHAIRATI KAZEROONI TORAJ | Issue Writer Certificate

Keywords

Student WithdrawsQ1

Retention StudentsQ1

Prevent Customer ChurnQ2

Baizian NetworkQ1

SLR AlgorithmsQ1

Abstract

The challenges facing educational institutions, especially nonprofit higher education institutions, are to earn money to meet their goals. Student withdrawal is in the opposite direction. By identifying students as opt-outs, preventive and supportive polices can be anticipated to prevent a reduction in the image and hopes to attract revenues. This research is aimed at identifying students at risk by using the Data Mining Data of the Student attention Center of Payame Noor University of Tehran during the years 2013-16. Data were extracted from the education system. Of the 20 potentially effective attributes, 92% accuracy model was identified. In the model, six independent characteristics (age, group, grade, probation, and gender) and an associated attribute (term) were identified and subsequently the degree of importance of the attributes involved in the withdrawal and their relationship with each other was determined. Risk of withdrawal and risk ranking table for different terms were presented. Findings indicate that age is the most important factor. The bachelor degree is between the ages of 18-21 in the senior age group of 22-26 and the PhDs 31 to 29 in the most risky groups. In terms of academic and postgraduate degrees, they are the most risky period in the formal and continuing education programs of the research course. attention rates for students aged19&20are about 50% in the third semester.

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APA: Copy

Ahmadi, Sayed Ali Akbar, & KHAIRATI KAZEROONI, TORAJ. (2019). Data Mining Withdrawal of the Students of Payame Noor University in Tehran State to Increase Student Rentention Rate (Preventing Customer Rejection). JOURNAL OF PUBLIC ORGANZATIONS MANAGEMENT, 7(2 (26) ), 47-58. SID. https://sid.ir/paper/400358/en

Vancouver: Copy

Ahmadi Sayed Ali Akbar, KHAIRATI KAZEROONI TORAJ. Data Mining Withdrawal of the Students of Payame Noor University in Tehran State to Increase Student Rentention Rate (Preventing Customer Rejection). JOURNAL OF PUBLIC ORGANZATIONS MANAGEMENT[Internet]. 2019;7(2 (26) ):47-58. Available from: https://sid.ir/paper/400358/en

IEEE: Copy

Sayed Ali Akbar Ahmadi, and TORAJ KHAIRATI KAZEROONI, “Data Mining Withdrawal of the Students of Payame Noor University in Tehran State to Increase Student Rentention Rate (Preventing Customer Rejection),” JOURNAL OF PUBLIC ORGANZATIONS MANAGEMENT, vol. 7, no. 2 (26) , pp. 47–58, 2019, [Online]. Available: https://sid.ir/paper/400358/en

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