Data mining withdrawal of the students of Payme Noor University in Tehran state to increase student retention rate (Preventing customer rejection) | ||
| مدیریت سازمانهای دولتی | ||
| Article 3, Volume 7, Issue 2 - Serial Number 26, April 2019, Pages 47-58 PDF (1.31 M) | ||
| DOI: 10.30473/ipom.2019.39792.3144 | ||
| Authors | ||
| Saied Ali Akbar Ahmadi1; Toraj Khairatikazerooni* 2 | ||
| 1Professor of Department of Management, Payame Noor University, Tehran, Iran. | ||
| 2. Ph.D of Department of Information Technology Management, Tehran of University, Tehran, Iran. | ||
| 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 policies 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 91-94. Data were extracted from the education system. Of the 20 potentially effective attributes, a 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 (risk of attention) and risk ranking table for different terms were presented. Findings indicate that age is the most important factor. From a Sunni point of view, the bachelor degree is between the ages of 21-18 in the senior age group of 26-22 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 aged 19 and 20 are about 50% in the third semester. | ||
| Keywords | ||
| student withdraws; retention students; prevent customer churn; Baizian network; self-learner's response algorithms | ||
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